Optimisation of Oil Spill Dispersants on Weathered Oils. A

NEI-M0--9 7 Per JohanBrandvik

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DR. SCIENT AVHANDLING KJEMISKINSTITUTT FAKULTET FOR KJEMIOG BIOLOGI NORGES TEKNISK-NATLTRVITENSKAPELIGE UNIVERSITET TRONDHEIM 1997 To my parents Printed at the Norwegian University of Science and Technology, NTNU January 1997. Final oral examination held in Trondheim, March 14. 1997.

Keywords: Dispersant, oil spill, oil weathering, multivariate statistics, optimisation and experimental design

Suggested reference: Brandvik, P.J., 1997. Optimisation of Oil Spill Dispersants on Weathered Oils a new Approach using Experimental Design and Multivariate Data Analysis. Dr. thesis. Norwegian University of Science and Technology, Department of Chemistry, N-7055 Trondheim, Norway. ISBN 82-7861-030-4

Optimisation of Oil Spill Dispersants on Weathered Oils a new Approach using Experimental Design and Multivariate Data Analysis

A thesis submitted for the Norwegian academic degree Doctor Scientiarum

Per Johan Brandvik

Norwegian University of Science and Technology, NTNU Department of Chemistry Faculty of Chemistry and Biology N-7055 Trondheim NORWAY

1 Present address: SINTEF Applied Chemistry, Environmental Engineering, 7034 Trondheim. Email: [email protected] , phone: +47 7359 1220 -3-

Table of contents Page

1. Preface...... 7 2. Introduction...... 8 3. Summary of papers used in this thesis...... 11 4. Experimental...... 13 4.1 Laboratory weathering of oils...... 13 4.2 Oil types used in this study...... 13 4.3 Laboratory dispersant effectiveness tests...... 15 4.4 Chemical and physical analysis of the oils ...... 15 5. Weathering processes of an oil slick at sea...... 17 5.1 Spreading ...... 18 5.2 Evaporation ...... 20 5.3 W/o-emulsification ...... 21 5.4 Natural dispersion ...... 22 5.5 Sedimentation ...... 25 5.6 Photo-oxidation ...... 25 5.7 Biodegradation ...... 25 5.8 Dissolution ...... 26 6. Experimental design used...... 27 6.1 Definitions ...... 27 6.2 Why use experimental design?...... 27 6.3 Mixture designs ...... 30 6.4 Factorial design ...... 31 6.5 Blocking and randomisation ...... 33 7. Multivariate analysis - building “soft” models...... 35 7.1 Model building ...... 35 7.2 Pre-treatment of data...... 36 7.3 Explorative data analysis - Principal Component Analysis ...... 36 7.4 Multivariate prediction with the PLS algorithm ...... 37 7.5 Response surfaces modelling ...... 39 7.6 Validation of models ...... 40 8. Use of dispersants to reduce environmental impact from oil spilled at sea...... 44 8.1 What is a dispersant?...... 45 8.2 How do dispersants work? ...... 46 8.3 Why use Dispersants?, pros et cons ...... 47 8.4 Environmental impacts of dispersant use...... 49 8.5 Application of dispersants...... 51 8.6 Dispersants present role and possible future role in Norwegian oil spill contingency.... 54 9. Operational use of dispersants - a case study...... 55 9.1 Natural resources in the area...... 55 9.2 Oil Spill contingency resources in the area...... 57 9.3 Oil spill scenarios ...... 59 9.4 Conclusions ...... 73

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Table of contents (continued) Page

10. Conclusions and recommendations...... 75 10.1 Characterisation of oils for environmental purposes ...... 75 10.2 Need for development of new dispersants...... 75 10.3 Operational use of oil spill dispersants...... 75 11. References...... 77

Appendix A: Reprints of papers

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Table of figures Page

Figure 5.1 Estimated world wide sources of oil pollution to the marine environment (from NRC, 1989) ...... 17 Figure 5.2 The most important weathering processes and their time windows ...... 18 Figure 5.3a-b Spreading and oil film thickness from an experimental oil release in 1996 (Brandvik et ah, 1996b): ...... 19 Figure 5.4 Evaporation loss for six different oil types predicted with IKU weathering model...... 20 Figure 5.5 Water uptake (vol.%) for six different oil types predicted with IKU weathering model...... 21 Figure 5.6 Viscosity (cP at shear rate 10 s'1) of the w/o-emulsion from six different oil types predicted with IKU weathering model...... 22 Figure 5.7 Natural dispersion of six different oil types predicted with IKU weathering model (wind speed 10 m/s)...... 23 Figure 5.8 Mass balance for Gullfaks and Veslefrikk cruds predicted with IKU weathering model using; 15°C water temperature and 15 m/s wind speed...... 24 Figure 6.1a First series of measurements to optimise dispersant composition; The ratio between two of the surfactants is kept constant (Xj and X3) while the content of the third (X2) is varied to find its optimum...... 28 Figure 6. lb Second series of measurements to optimise dispersant composition; The content of X2 is kept constant at the “optimum ” setting from the first series of experiments (figure a) and the ratio between Xi and X3 is varied to optimise this setting ...... 28 Figure 6.2a-b True distribution of dispersant effectiveness as a function of composition together with the first and second series of experiments...... 29 Figure 6.3 Simplex-controid design, with 10 experimental points. A constraint of minimum 10% is used on all three variables...... 31 Figure 7.1 The data matrix (X) decomposed into a structure part (TP1) and noise (E) (from Esbensen et al., 1994) ...... 37 Figure 7.2 X, W, P, T and Y, U, Q matrices used for multivariate calibration with the PLS algorithm (from Esbensen et al., 1994) ...... 39 Figure 7.3 Normal probability plot of the effects calculated from a the fractional factorial design performed without replicate measurements from paper 5...... 43 Figure 8.1a-c Examples of surfactants used in modern oil spill dispersants (from NRC, 1989 and MAFF, 1995) ...... 45

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Table of figures (continued) Page

Figure 8.2 Schematic presentation of how dispersants are applied (a), and seeks to the oil-water interface (b), and promote natural dispersion (c) of the oil into small droplets in the water column (derived from Canevari, 1969) ...... 47 Figure 8.3 Concentration profiles of dispersed oil in the water column at 1, 5 and 8 meters depths before (A) and 20 minutes after dispersant application (B) on a 15 m3 experimental oil spill during the NOFO 1995 sea trial (from Brandvik et al., 1996a) ...... 50 Figure 8.4 Offshore filling of dispersant into Response 3000D directly from a supply vessel using the suction hose (from Brandvik et al., 1996b)...... 53 Figure 8.5 Spraying of dispersant during the NOFO 1996 sea trial with the Response 3000D helicopter bucket (from Brandvik et al., 1996b)...... 53 Figure 9.1 The coastal area outside Trpndelag used for these oil spill scenarios and the most important natural resources ...... 56 Figure 9.2 Mass balance of oil in scenario 1 “Small offshore spill”, alternative A: no response, alternative B: mechanical recovery, alternative C: spraying dispersant from standby vessel and alternative D: spraying dispersant from helicopter bucket...... 61 Figure 9.3 The surface oil trajectory for alternative 1A “no response ” showing the surface oil a few hours from reaching the Froan nature reserve...... 62 Figure 9.4 Surface exposure (km2-hours) caused by the thick parts (> 20 pm) of the surface slick for the four different response options (A-D)...... 62 Figure 9.5 Mass balance of oil in scenario 2 “Coastal bunker spill”, alternative A: no response, alternative B: mechanical recovery and alternative C: use of dispersant...... 66 Figure 9.6 The surface oil trajectory for alternative 2A “no response ” were the oil reach the coastal area outside Trpndelag and impacting the natural resources ...... 67 Figure 9.7 Mass balance of oil in scenario 3 “Underwater blow-out ”, alternative A: no response, alternative B: mechanical recovery and Alternative C: spraying dispersant from standby vessel and Alternative D: spraying dispersant from helicopter bucket...... 70 Figure 9.8 The surface oil trajectory for alternative 3A “no response ” showing the surface Heidrun oil which naturally disperse before reaching Froan nature reserve...... 71 Figure 9.9: Surface exposure (km2-hours) caused by the thick parts (> 20 pm) of the surface slick for the four different response options (A-D)...... 71 Figure 9.10: Surface oil slick from an experimental underwater oil and gas blow-out simulation (Rye et al., 1996b) ...... 72

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1. Preface

First of all, my thanks go to my wife Kari and our two children Dagrun and Trond for their patience especially during the last part of my work with this thesis. Also thanks to my older sister Ann Britt who has “encouraged ” me by asking (twice a week for the last two years), “tell me Per Johan, when are you going to finish your thesis?”.

This Dr.Scient-study have been performed both at the Department of Chemistry, Norwegian University of Science and Technology and at IKU Petroleum Research, SINTEF both located in Trondheim. Professor Marit Trastteberg has been my supervisor at the Department of Chemistry, with Professor Jostein Krane (Department of Chemistry) and Department manager, Department of Environmental Technology, IKU, Dr.Ing. Liv Schou 2 as co-supervisors. I am grateful for the practical and scientific support and the inspiration that I have received from them.

There is one person, senior scientist Per S. Dating, without whose presence at IKU, this doctor degree would never have been within the fascinating field of oil spill contingency. Per has been the architect behind the building of the oil spill contingency activity at IKU and he has also been the main intellectual resource in this group for the last ten years. I started to work with Per as a fresh graduate student and he has been my main supervisor in this Dr.Scient-study.

Also a great “thank you all” to my colleagues at IKU, especially to the highly skilled staff of technicians at our laboratory who have done most of the laboratory work during my Dr.Scient-study. My English speaking colleague Alun Lewis has tried to improve my writing and my colleagues Mark Reed and Ole Morten Aamo from our modelling section, have assisted me with the oil spill scenarios.

This work has been funded by a two and a half year scholarship from the Research Council of Norway and by internal funding from my employer SINTEF.

During the period that this work was performed, 1987-1994, Fina Exploration Norway funded a research program called DIWO “Use of dispersants on weathered oils ” at IKU, which my Dr.Scient-study has been connected to. I am thankful for their support and interesting scientific co-operation. A special thanks goes to Olaf Gram, Fina Exploration Norway, who administrated this research program and to Alain Charlier at Fina Research in Brussels.

2 Present adress: Felleskjppet, Bromstadv. 57, N-7005 Trondheim, Norway

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2. Introduction

The development towards today ’s “state-of-the-art ” use of dispersants to fight oil spills at sea, started with a large tanker accident off the English coast in 1967. One of the largest tankers operating at that time, the “Torrey Canyon ” carrying 300 000 tonnes of crude oil, grounded and approximately 160 000 m3 of crude oil were released into the sea. Over the next 14 days about 1 600 m3 of “degreasing agents ” or “detergents ” were used to disperse surface oil and to clean rocky shorelines. These chemicals were manufactured for engine room cleaning and similar uses and had a high toxicity to marine organisms, due their aromatic solvents and toxic surfactants. Appropriate equipment for application of these chemicals was also scarce, often resulting in low effectiveness and heavy overdosage.

The biological impact of the stranded oil along the rocky shorelines was significant due to the overdosage and bad application of these toxic chemicals. This experience gave oil spill chemicals in general, and especially dispersants a bad reputation and resulted in strict regulations for dispersant use in several countries, including Norway.

On the other hand, these adverse effects of “detergent ” use during the Torrey Canyon incident, initiated new research aiming towards more effective and less toxic dispersants and better application systems. Research programmes were initiated in France, UK, US, Canada and in Norway. These programmes included studies of the toxicity and the fate of the dispersed oil and the dispersants in the environment.

During the 1970 ’s, dispersants were developed using less toxic solvents, but still with a low content of surfactants, to be used in high concentrations compared to the oil (up to 1:1). Later, concentrated dispersants with 10-60% surfactants were developed. These products were originally designed to be applied from boats after dilution with sea water. However, experience at sea and in the laboratory showed that higher effectiveness could be obtained by applying the dispersant neat in lower dosages. This also opened the way for dispersant application from fixed-wing aircraft or helicopters.

The work presented in this thesis was mainly performed in the period 1989-1994 and was a part of a research program called “Dispersant effectiveness on weathered oils ” - DIWO (see preface). The main objectives with this program were to study and describe the weathering processes of oil spilled at sea and to develop dispersants with high effectiveness on weathered oils. During this program, a laboratory approach was developed to describe and predict the weathering processes of an oil slick at sea (evaporation, emulsification, natural dispersion etc.). This approach and how this data were combined with numerical modelling to predict weathering of an oil slick at different environmental conditions (temperature, wind etc.) are described in paper 1. How laboratory test methods are used to screen dispersant effectiveness under different environmental conditions (temperature and salinity) is described in paper 2. Development of a new approach for dispersant optimisation based on statistical design and multivariate analysis is described in paper 3,4 and 5. This development work resulted in a new dispersant with low toxicity and high effectiveness on a broad selection of oil types (paper 5).

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The DIWO program was initiated by and led by my colleague, senior scientist Per S. Baling from the beginning in 1987. From 1991 this program was led by the author. A research group consisting of 3-5 scientist and 2-3 laboratory technicians has been involved in this work during this period. The main contribution from the author to this program has been in connection with dispersant testing and development of new dispersants (paper 2, 3,4, and 5).

My thesis includes both environmental chemistry and applied multivariate statistics and the five papers combine these two areas. As a frame around these papers, more theoretical sections from these two areas concerning; weathering of oil slicks, experimental design, multivariate analysis and use of dispersants are included. This has been done to give the necessary introduction and background for the work described in the papers and hopefully to make this work more available for a broader audience not so familiar with this area.

To illustrate the potential of dispersant used as an operational response method on oil spills, three different oil spill scenarios are described in section 9. The effect of using dispersants compared to mechanical recovery and “doing nothing ” is compared in this section.

The main conclusions from the papers and the scenario study are summarised, at the end of this thesis, together with some recommendations that the author argues will increase the effectiveness of the Norwegian oil spill contingency.

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3. Summary of papers used in this thesis

Five papers are used as a basis for this thesis. Two of them have been published and have been evaluated by peer reviewers, while the three last papers have been submitted for publication, during November 1996. Reviewers comments have been received for these papers and are worked into the papers. Reprints of all five papers are given in appendix A.

Paper 1: Characterisation of crude oils for environmental purposes, Daling P.S. and Brandvik P.J., Mackay, D., Johansen, 0., 1991: Oil & Chemical Pollution 7, 1990/91, pp. 199-224.

This first paper describes the approach used for the characterisation of oil types used in this study. This approach includes artificial weathering of the oils in the laboratory and chemical/physical analysis resulting in an array of bulk oil variables. These variables are then used as input to the IKU Weathering model. This numerical model is used to predict weathering properties (water uptake, emulsion viscosity, evaporative loss, natural dispersion etc.). Predictions from this model combined with laboratory dispersant testing on oil samples with different weathering degrees is then used to estimate the “window of opportunity ” for dispersant use.

Paper 2: Laboratory testing of dispersants under Arctic conditions, Brandvik, P.J., Knudsen 0.0., Moldestad, M.0. and Daling P.S., in The use of Chemicals in Oil Spill Response, ASTM STP 1252, Peter Lane Ed., American Society for testing and Materials, Philadelphia, 1995.

The second paper presents the results of a screening study of dispersants under cold conditions with varying salinity. This paper shows that dispersants used to enhance the rate of natural dispersion at sea have varying effectiveness on different oil types. Dispersants which have high effectiveness at normal North Sea salinity (3.5%) show a very low effectiveness in brackish water (1.5-0.5% salinity). No currently available, dispersant gives a high effectiveness at both North Sea salinity and in brackish water. Low salinity water (1.5-0.5%) may be found in coastal areas where surface layers of low salinity water might be found in the spring, due to river run-off. Salinity may also vary in surface water of Arctic areas (e.g. the northern Barents sea), due to ice melting.

Paper 3: Statistical simulation as an effective tool to evaluate and illustrate the advantage of experimental designs and response surface methods, Brandvik, P.J., Chemometrics and Intelligent Laboratory Systems, submitted for review and publication, November 1996.

The objective with this work was to establish a multivariate alternative to a classical univariate optimisation method for dispersant development. The paper presents simulations which show how experimental design and response surface methods can be used in dispersant optimisation. Several experimental designs are compared with the classical vary One-Variable-At-a-Time (OVAT) approach. A multivariate approach, new within dispersant optimisation, combining a simplex-lattice design and response surface methods is presented.

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Paper 4: Optimisation of oil spill dispersant composition by mixture design and response surface methods, Brandvik, P.J. and Daling P.S., Chemometrics and Intelligent Laboratory Systems, submitted for review and publication, November 1996.

The objective in this paper was to test and verify with real data the multivariate approach for dispersant optimisation presented in the simulation study (paper 1). Experiments were performed using several different surfactant combinations and oil types. Response surfaces and final effectiveness of different optimised surfactant combinations (dispersants) are shown for; 1. Different surfactant combinations on the same oil type 2. Different oil types (crude/bunker) with the same surfactant combination 3. Water-free and emulsified oils

The paper shows how the proposed multivariate approach can effectively be used to optimise dispersants. The earlier used univariate approach assuming that the Hydrophilic-Liphophilic Balance (HLB) should be within 9-11 is shown to have limited value in dispersant optimisation.

Paper 5: Optimising oil spill dispersants as a function of oil type and weathering degree - a multivariate approach using partial least squares (PLS), Brandvik, P.J. and Daling P.S., Chemometrics and Intelligent Laboratory Systems, submitted for review and publication, November 1996.

The objective with this study was to predict optimum dispersant composition for a selection of surfactants as a function of oil type an weathering degree. Optimisation experiments were performed using the approach proposed in paper 1 and verified in paper 2. The oil type and weathering degree were characterised by principal Component Analysis (PCA), based on oil physical/chemical data. The surfactants, oil types and weathering degrees for 16 optimisation experiments were selected following a fractional factorial design. These 16 experiments were used as a calibration set and optimised surfactant compositions were predicted as a function of oil type and weathering degree by using the PLS algorithm.

An optimised dispersant formulation was predicted and compared to commercial products with respect to effectiveness, droplet size and toxicity. The final product had a high effectiveness, a low toxicity and produced very small oil droplets.

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4. Experimental

This section gives a brief description of the experimental procedures used in this thesis, but wherever possible references is made to the five papers forming a basis for this work or other publications.

4.1 Laboratory weathering of oils

The experimental approach used in this thesis concerning laboratory weathering of oils is described in paper 1. The weathering processes simulated and used in this thesis are evaporation and emulsification (paper 2 and 4) and photo oxidation (paper 5).

4.2 Oil types used in this study

Several different oil types have been used in the work described in this thesis. The oil samples and physical/chemical data describing these oils are compiled from different studies performed at IKU, SINTEF during the last 10 years. The sources for the oil samples and the data are presented in the papers where they are used. However, a brief overview of all the oil types used is presented in this chapter.

Gullfaks crude Gullfaks A/B is a medium heavy North Sea crude (0.882 kg/1) with a low wax and very low asphaltene content (1.6 and < 0.1 wt.% of fresh crude). This is a naphthenic crude where most of the n-alkanes have been biodegraded by micro-organisms in the reservoir. The pour point for the 150°C+ residue, which corresponds to a few hours weathering at sea, is less than -30°C, due to the low wax content.

Statfiord crude: Statfjord is a light paraffinic North Sea crude (0.834 kg/1) with a high wax and very low asphaltene content (4.1 and <0.1 wt. % of fresh crude). The pour point for the 150°C+ residue, which corresponds to a few hours weathering at sea, is 21°C.

Arabian Heavy crude This crude is produced in the Middle east and was earlier imported to Norwegian refineries. Arabian Heavy is a medium heavy crude (0.887 kg/1) with a high wax and high asphaltene content (4.6 and 4.3 wt.% of fresh crude). This is a asphaltenic crude with a high content of asphaltenes and resins. The pour point for the 150°C+ residue, which corresponds to a few hours weathering at sea, is -23°C.

IF-30 Bunker fuel (Intermediate Fuel-30) This oil is a refineiy product which contains approximately 65% bunker-C (IF-340) and 35% gas oil which gives a density of 0.936 kg/1. Intermediate fuels are defined from their viscosities and IF-30 has a viscosity of 30 cP (50°C). The oil has a low wax and medium to high asphaltene content (2.5 and 4.1 wt.%). The oil used in this study (150° C+) has a pour point of 0°C.

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DUC: This crude is produced in the Danish sector of the North Sea and is imported to Norwegian refineries. DUG is a light crude (0.849 kg/1) with a low wax and asphaltene content (2.1 and < 0.1 wt.% of fresh crude). The pour point of the 150°C+ residue, which corresponds to a few hours weathering at sea, is -18°C.

Oseberg crude: Different batches of the Oseberg crude, both produced at different fields and from different structures within the same field, have been used in several different projects at IKU. Compared to the variations between Oseberg and the other crudes used in this thesis, the variation within the Oseberg batches is not large and will not be further presented here. Properties of the different Oseberg crudes are presented in earlier studies (Strpm-Kristiansen et ah, 1995). Oseberg C crude is used as representative of the Oseberg crudes for the purpose of this work. This blend is a medium heavy North Sea crude (0.857 kg/1) with a medium wax and a low asphaltene content (3.4 and 0.36 wt.%). The pour point of the 150°C+ residue, which corresponds to a few hours weathering at sea, is 12°C.

Sture Blend: This blend consists of a mixture of Oseberg and Veslefrikk crudes and is transported by pipeline from the Oseberg field centre to Norsk Hydro ’s terminal at Sture close to Bergen. The batch used in this study consists of approx. 70% Oseberg and 30% Veslefrikk and is a light North Sea crude (0.847 kg/1) with a medium wax and a asphaltene content (3.5 and 0.22 wt.%). These values are for the fresh crude, while the pour point of the 150°C+ residue, which corresponds to a few hours weathering at sea, is 6°C.

Brent blend: This blend is produced in the UK sector of the North Sea and is one of the largest UK crudes in volume. Brent blend is a light paraffinic North Sea crude (0.839 kg/1) with a medium wax and low asphaltene content (3.6 and 0.4 wt.% of fresh crude). These values are for the fresh, while the pour point of the 150°C+ residue, which corresponds to a few hours weathering at sea, is 18°C.

Alaska North Slope crude: Alaska North Slope (ANS) is produced in the northern parts of Alaska and transported to Valdez through the large Trans Alaska Pipeline. ANS is a medium heavy asphaltenic crude (0.884 kg/1) with a low wax and medium asphaltene content (2.9 and 1.6 wt.% of fresh crude). The pour point for the 150°C+ residue, which corresponds to a few hours weathering at sea, is 0°C.

Murban Abu Dhabi: This crude is produced in Abu Dhabi and is imported to US refineries. Murban is a light paraffinic crude (0.829 kg/1) with a medium wax and low asphaltene content (3.6 and 0.2 wt.% of fresh crude). The pour point for the 150°C+ residue, which corresponds to a few hours weathering at sea, is -9°C.

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Heidrun crude: This crude is produced at the Heidrun field at Haltenbanken and is a naphthenic North Sea crude (0.883 kg/1) with a low wax and asphaltene content (0.9 and 0.14 wt.%). This is a naphthenic crude where some of the saturated components have been biodegraded by micro-organisms in the reservoir. The pour point for the 150°C+ residue, which corresponds to a few hours weathering at sea, is <-30°C, due to the low wax content.

Veslefrikk crude This crude is produced at the Veslefrikk field in the North Sea and is a light paraffinic crude (0.839 kg/1) with a high wax and low asphaltene content (4.6 and 0.14 wt.%). The fresh crude has a high pour point of 6°C while the weathered oil used in this study (150° C+) has a pour point of 15°C, due to the high wax content.

4.3 Laboratory dispersant effectiveness tests

There is no single laboratory method for testing the effectiveness of dispersants that is generally accepted as being a good simulation of different conditions at sea. Many different test methods have been devised and different methods are used for governmental approval of dispersants e.g. in France, USA, UK, Norway and Canada. The results obtained from these methods vary due to different energy input and sampling regime.

The tests methods used in this thesis were the French dispersant test from Institute Francais du Petrole - the IFF test, and the UK dispersant test from Warren Spring Laboratory - the WSL test.

Both test methods are described in paper 1, more specific experimental conditions (temperature, dispersant-to-oil ratio and water salinity) are defined in each paper.

The IFF test, which discriminates well between efficient and less efficient dispersants, is used in paper 2 for comparing dispersant effectiveness under different environmental conditions (temperature and water salinity) and for dispersant optimisation in paper 4 and 5. The WSL test is the current official test method for dispersant approval in Norway, and for this reason it is used for final verification of optimised dispersant effectiveness in paper 5.

4.4 Chemical and physical analysis of the oils

The physical/chemical properties of the oils are given in table 1 in paper 5. The chemical and physical variables used in this thesis are:

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1. Contents of saturate components (wt.%) 2. Contents of resins (wt.%) 3. Contents of aromatic (wt.%) 4. Contents of asphaltenes (wt.%) 5. Contents of waxes (wt.%) 6. Viscosity (cP at shear 10 or 100 s'1) 7. Interfacial tension between oil and sea water (mN/m) 8. Pour point of oil (°C) 9. Density (kg/1)

Experimental procedures for these analysis are given in paper 1. These variables are used to give a general description of the oils used in this thesis in papers 2,4 and 5. These variables are more extensively used in paper 5 to describe systematic trends (oil type and weathering degree) among 55 oil samples by Principal Component Analysis (PCA).

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5. Weathering processes of an oil slick at sea

In this thesis the term “oil spill” is used to describe an acute release of oil at sea for example from a tanker collision or grounding, offshore blow-out or a pipeline leakage. These accidents account world wide for only 10-15% of the total amount of oil entering the ocean (figure 5.1), but these accidents could release a relatively large amount of oil in a limited time and place. The potential for damage to the environment may be large with such oil spills, especially when the oil is spilt within drifting distance to the coast line or other vulnerable natural resources.

Operational releases Natural sources from ships 8% 32%

Atmosphere fall out 9%

Ship accidents 13%

Municipal waste Offshore production 37 % 1 %

Figure 5.1 Estimated world wide sources of oil pollution to the marine environment (fromNRC, 1989).

An oil spill at sea undergoes several physical and chemical processes which change the behaviour of the oil and these changes must be taken into account during both contingency planning and clean-up operations. Some of these processes lead to removal of the oil from the sea surface and others make the oil spill more persistent. Figure 5.2 illustrates the most important weathering processes.

Weathering of oil slicks at sea is not a homogeneous process for all types of oil. The rate and extent of the weathering processes show a large variation between different oil types. Several laboratory studies (e.g. Brandvik and Baling, 1991, Baling and Brandvik, 1991 and Brandvik et ah, 1996d) and field trials (e.g. Johansen, 1984, Sprstrpm, 1989, Lewis et al., 1995a, Walker and Lewis, 1995, Brandvik et ah, 1996a and Brandvik et ah, 1996b, Strpm-Kristiansen et ah, 1996) have shown that different oil types and environmental conditions may cause different weathering behaviour of an oil spill at sea.

Even within Norwegian crudes, which were earlier regarded as a homogenous group (EPA, 1982), there are different types or groups (e.g. paraffinic, naphthenic or asphaltenic crudes) which will show significant different behaviour when exposed to weathering processes at sea. In addition to the crudes produced and transported in

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Norwegian waters, there is also a large variety of bunker fuels (marine diesel to Bunker- C), which have very different weathering behaviours compared to crude oils.

Photo-oxidation Biodegradation Sedimentation Water-in-oil emulsification

Oil-in-water dispersion

Spreading

Drifting

boss\ik4i96HOO\tegneiMig-eng\emutsion.eps

Figure 5.2 The most important weathering processes and their time windows (derived from Mackay et al., 1983).

Six oil types are used as examples to illustrate the differences in weathering behaviour of different oil types in this section (Heidrun, Sture Blend, Veslefrikk, Gullfaks, and DF-30 Bunker). These six oils span a large variation, of weathering properties. A brief description of these oil types is given in chapter 4. The Heidrun crude and IF-30 bunker fuel are also used as examples in the oil spill scenarios described in section 9.

Weathering of an oil slick at sea is strongly dependant on the environmental conditions. Factors such as wind, breaking waves and temperature will strongly influence the weathering processes. The presentation of oil weathering at sea given below is illustrated by predictions from the IKU weathering model. This model is presented in paper 1. Predictions of weathering properties for the five oils are based on North Sea summer conditions assuming a sea temperature of 15°C, 10 m/s wind speed and oil film thickness decreasing exponentially, due to initial spreading, from 20 mm to 2 mm with a half-time of 1 hour.

5.1 Spreading

Spreading is one of the most dominant weathering processes during the early stages of an oil spill. In the beginning the oil spreads as a continuous slick, and the spreading rate

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is also influenced by the viscosity, pour point and density of the oil. High viscosity oils spread more slowly and oils with a pour point 10-15°C above the sea temperature may have a limited spreading due to a semi-solid behaviour.

Figure 5.3a-b Spreading and oil film thickness from an experimental oil release in 1996 (Brandvik et al, 1996b): a: Schematic presentation of the spreading of an experimental oil slick after 2-28 hours of spreading. b: Remote sensing image after 2 hours weathering, showing the thick emulsion (IR white) and the thin oil (10-100 pm) around the emulsion (1R black). The sheen area (<10 pm) is visible in the UV-image.

Figure 5.3a illustrates the spreading of an 15 m3 experimental oil slick and the distribution of the film thickness during 2-28 hours of weathering. After the initial phase, the spreading will be dominated by oceanographic conditions (waves, wind and currents). In most cases, the oil slick will be scattered as “windrows ” over a large area in the wind direction. The oil thickness will be very non-uniform within the slick and will vary from several mm in the thick emulsified oil (IR-white) to only a few pm in the “blueshine” areas (UV). This non-uniform distribution of the oil slick may limit the effectiveness of clean-up operations at sea.

Figure 5.3b shows a remote sensing image taken from an surveillance aircraft used for detecting illegal oil spills. This composite figure shows images recorded by an Ultra

Dr. thesis Per Johan Brandvik, March 1997 -20-

Violet (UV) and an Infra Red (IR) line scanner. The UV scanner detects thin oil layers in the sub-jam range and these images gives the total size of the oil slick. The IR image distinguishes between the thick emulsified oil (IR-white), often several mm thick, and a thinner layer (10-100 pm ) of oil (IR black), surrounding the thick emulsion. Typically, the thick emulsified oil (IR-white) represents only 5-10% of the total slick area, but contains the major part (80-90%) of the volume of the oil slick. Further details concerning remote sensing techniques are given in Lewis, 1995c.

5.2 Evaporation

The extent of evaporation from an oil spill is primarily determined by the amount of volatile components in the oil. The greater the proportion of oil components with low boiling point, the greater the degree of evaporation. The sea temperature is also an important factor, but the oil film thickness has a greater importance. The initial spreading of a slick produce a large increase in area and increase the rate of evaporation. In broad terms, those oil components with a boiling point below 200°C will evaporate within a period of 24 hours at normal North Sea summer conditions.

Norwegian crudes may have an evaporative loss varying from 15-30% after 24 hours at sea. Figure 5.4 shows the evaporation loss for a selection of oil types.

— Heidrun — Sture Blend —.Veslefrikk -- Gullfaks A/B ...... IF-30 Bunker fuel

Weathering time at sea (hours)

Figure 5.4 Evaporation loss for six different oil types predicted with IKU weathering model.

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5.3 W/o-emulsification

When oils are spilt at sea they usually start to take up water and form water-in-oil (w/o) emulsions. This emulsification increases the volume of the oil slick by a factor of 2-5 (50-80% water). This water uptake causes the formation of viscous and often very stable w/o-emulsions or “chocolate mousse ” (figure 5.5 and 5.6), which makes the oil much more persistent at the sea surface. This formation of w/o-emulsion also retards other weathering processes, especially evaporation and natural dispersion, which reduce the volume of the surface oil slick.

The rate and amount of water uptake are dependant on the composition of the oil and the sea state. The presence of breaking waves (wind speed of approximately 5 m/s or more) is usually regarded as necessary for w/o-emulsification, but water uptake may take place slowly, and to a lesser extent in calmer weather conditions.

The rate of water uptake is strongly dependant on the chemical composition of the oil and light paraffinic crudes with a high wax content (like Veslefrikk) have a more rapid water uptake than nahphtenic oils (Gullfaks and Heidrun), see figure 5.5.

------Heidrun ------Sture Blend ------Veslefrikk ------Gullfaks A/B ...... IF-30 Bunker fuel

Weathering time at sea (hours)

Figure 5.5 Water uptake (vol.%)for six different oil types predicted with IKU weathering model.

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100000

o 10000

------Heidrun ------Sture Blend ------Veslefrikk ----- Guljfaks A/B ...... IF-30 Bunker fuel

Weathering time at sea (hours)

Figure 5.6 Viscosity (cP at shear rate 10 s'1) of the w/o-emulsion from six different oil types predicted with IKU weathering model.

5.4 Natural dispersion

When a surface oil slick is exposed to breaking waves, energy from the waves will force parts of oil down in to the water column. This submerged oil will be divided into droplets by turbulence created by breaking waves. These oil droplets will have a buoyancy dependant on their droplet size, according to Stokes law. The larger droplets will have a sufficient buoyancy to resurface into the surface oil again and some of them will also form a thin oil layer (“sheen”) behind the surface oil. The smaller droplets, usually below 70 pm, will be randomly distributed in the water column due to turbulence (Lunel, 1995a). The size of the droplets formed is, for this reason, an important factor for the rate of natural dispersion. The droplet size is dependant on the energy input, the viscosity of the surface oil/emulsion and the interfacial tension between the oil and the sea water.

The viscosity of the surface oil or emulsion (section 5.3) is important for the rate of natural dispersion. This means that an low viscosity oil spill will have a much higher rate of natural dispersion than an oil spill of an oil type which form high viscosity emulsion.

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------Heidrun ------Sture Blend ------Veslefrikk ------Gullfaks A/B ...... IF-30 Bunker fuel

Weathering time at sea (hours)

Figure 5.7 Natural dispersion of six different oil types predicted with IKU weathering model (wind speed 10 m/s).

The operational importance of natural dispersion was clearly shown during the Braer oil spill at Shetland in 1993 where approximately 87 000 m3 of Gullfaks crude was released in high sea state. Most of the released oil was naturally dispersed, since the fresh Gullfaks crude does not form stable emulsion. Only minor amounts of oil were recovered on the beaches. On the other hand, during the Valdez oil spill in 1989, approximately 47 000 m3 of Alaska North Slope crude were released. This crude form very stable w/o-emulsions, which stranded over a large area and caused one of the largest beach cleaning operations ever. The difference in emulsifying properties of the two crude oils spilt in these incidents was one of the most important factors influencing the outcome, but other factors like release and weather conditions also strongly affected the rates of natural dispersion and emulsification.

The difference in behaviour between the two Norwegian crudes; Gullfaks and Veslefrikk is illustrated by predictions from the IKU weathering model in figure 5.8. The estimated natural dispersion during the first day for a 200 m3 surface oil spill of these two oil types is very different, mainly due to differences in water uptake rate and emulsion stability. After 24 hours at sea (15 °C and 15 m/s wind) only minor amounts of water-free oil are left of the Gullfaks spill - 8%, while 43% is remaining from the corresponding Veslefrikk spill. Taking water uptake into account (figure 5.5) the corresponding volumes are 69 and 507 m3 of emulsified oil. In case of a mechanical recovery operation on two such oil spills, the recovery and storage capacity would have to be 5-10 times larger with the Veslefrikk spill compared to the Gullfaks spill.

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Property: MASS BALANCE Oil Type: GULLTAHS A/S {IKU) MNTireiioup Data Source: IKU Petroleum Research (1992) Copyright 1996 Oil film thickness: Initial (nvn) : 20 Terminal (nvn) : 2 Halftime in thickness reduction (hrs): 1.0 Pred. date: Des.

| Evaporated | Surface | Naturally dispersed

Temperature: 15 *C Wind speed: 15 m/s

Time (Hours)

The algorithm for prediction of natural dispersion is preliminary and is currently under improvement.

Property: MASS BALANCE Oil Type: VESLEFRIKK (IKU) Data Source: IKU Petroleum Research (1991) Copyright 1996 Oil film thickness: Initial (mm): 20 Terminal (nm) : 2 Halftime in thickness reduction (hrs): Pred. date: Des.

| Evaporated | Surface ! Naturally dispersed

Temperature: 15 °C Wind speed: 15 m/s

Time (Hours)

The algorithm for prediction of natural dispersion is preliminary and is currently under improvement.

Figure 5.8 Mass balance for Gullfaks and Veslefrikk cruds predicted with IKU weathering model using; 15°C water temperature and 15 m/s wind speed. -25-

5.5 Sedimentation

A few heavy residual oils have a density greater than 1.00, and will sink in fresh or brackish water, but these occasions are very rare. Normally even a very weathered oil will have a density less than the density of sea water (1.025 kg/1 at 15°C and 3.5 % salinity).

Sinking of oil from an oil spill at sea is often caused by adhesion of sediment particles to the oil. This is more likely to happen when oil is released in shallow waters at high sea states where particles are highly abundant in the water masses. A typical scenario is grounding of a tanker in bad weather on e.g. cliffs on the shoreline. The oil-coated sediment particles or the oil droplets with adhering sediment particles will then follow the currents until they sink due to lower energy conditions. This was the case during the Braer oil spill in 1992, where approximate 30% of the total 85 000 m3 of crude was estimated to have been deposited on the sea bed after adhesion of sediment particles to the oil (ESGOSS, 1994).

5.6 Photo-oxidation

Some of the chemical components in an oil may react with oxygen and the presence of sunlight will promote most of these reactions. Degradation due to photo-oxidation is not a significant process for the mass balance of an oil slick from an operational point of view. However, photo-oxidation may have influence on other weathering processes such as emulsification, due to the creation of polar components with surface active properties.

5.7 Biodegradation

Sea water contains a wide range of marine bacteria, moulds and yeast which can utilise the oil from an oil spill as a source of carbon and energy. Such micro-organisms are widely distributed in the sea and a single litre of sea water could contain upto 109 micro organisms. In chronically polluted waters e.g. harbours and areas which receive industrial discharges and untreated sewage or areas with natural seepage of oil, there is usually an larger abundance of oil-degrading bacteria.

Biodegradation is not, in the short term (days or weeks), regarded as a significant process for the mass balance of the oil slick from an operational point of view. The micro-organisms live in water and the biodegradation takes place at the interface between oil and water. The rate of biodegradation is therefore expected to increase when the oil is dispersed as small droplets into the water column, naturally or enhanced by dispersants, because the oil-water interface area is increased by a factor of several thousands.

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5.8 Dissolution

This process is of particular interest because dissolution increase the bio-availability of the oil components. This increase the potential for acute toxicity on marine organisms. The rate and extent to which oil components dissolves in the sea water depends mainly on the amount of the water soluble fraction (WSF) of the oil. The degree of natural dispersion are also important for the rate of dissolution, although surface spreading and water temperature may have some influence.

Many definitions of WSF exists, but oil components above Cg are virtually insoluble in sea water whereas lighter compounds, particularly aromatic hydrocarbons such as benzene and toluene, are slightly soluble. However, these components are also volatile and are for this reason rapidly lost to the air by evaporation, typically 10 to 1000 times faster than dissolution.

WSF-solutions for fresh North Sea crudes prepared in the laboratory with high oil-to- water ratios (> 1:100), might have concentrations of 10-50 ppm. The total dissolution potential in the laboratory of soluble component to water is typically 5-15% for Fresh North Sea crudes (Dating, 1983 and Baling and Johnsen, 1996). This assumes no evaporation of the light components.

An oil slick at sea where evaporation and dissolution occur simultaneously and the oil- to-water ratio is very low, concentrations averaging 2-20 ppb of dissolved oil or BTX (benzene, Toluene and Xylene) components, are measured in the sea water (1-10 meters depth) (e.g. Brandvik et al., 1996b and Strpm-kristiansen et al., 1996).

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6. Experimental design used

This section presents additional background concerning experimental design compared to paper 3 and 4 for the two types of experimental designs used in this thesis. Use of experimental design was not well established at IKU, SINTEF or at the Department of Chemistry, NTNU. For this reason, this background material is included to make the thesis more complete for persons new to this area, and hopefully to motivate them to do designed experiments.

6.1 Definitions

The following terms will be used in this thesis in connection with experimental design and multivariate analysis.

A variable: Scales of measured or calculated values, e.g. grams of component B, time of an experiment or boiling temperature.

A process: The phenomena we want to describe, e.g. interaction of the surfactants in an oil dispersant, chemical dispersion of an oil slick at sea or the melting of iron in alloy production.

Process variables: Variables that influence the process and are used to control it, e.g. the amount of surfactant SI in a dispersant, type of oil used dispersant testing or the temperature in an melting furnace.

Design variable: A process variable which is used to define the conditions for experiments in an experimental design.

Response variable: The variable from the process which is modelled; that means expressed as a function of the process variables, e.g. the effectiveness of an oil dispersant, the yield of a synthesis or the purity of a product. This variable is also called modelled variable or quality describing variable.

6.2 Why use experimental design?

The basic problem when optimising a process is to decide which variables to change and the intervals and resolution for this variation. Even when this problem have been solved, a further problem arises, what pattern of the experimental points will best reveal the aspects of interest? The aim is to discover the optimum conditions and to understand the interaction of the process variables on the response variable (e.g. how the surfactant composition influences the dispersant effectiveness) by using as few experiments (or as low cost) as possible.

The classical approach is to vary only one process variable at a time, to keep track of which variable causes the changes in the response variable. There are several disadvantages with this classical change one-variable-at-a-time strategy (OVAT), and

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these are described in the literature (e.g. Box, et al., 1978). An example to illustrate the problems with the OVAT-strategy, from this work is given in Figures 6.1a-b and 6.2.

Content of X2

Figure 6.1a First series of measurements to optimise dispersant composition; The ratio between two of the surfactants is kept constant (Xj and X3) while the content of the third (X2) is varied to find its optimum.

Content of X,

Figure 6.1b Second series of measurements to optimise dispersant composition; The content ofX2 is kept constant at the “optimum” setting from the first series of experiments (figure a) and the ratio between X; and X3 is varied to optimise this setting.

Figure 6. la shows the variation in dispersant effectiveness when the ratio between two surfactants was kept constant and the content of the third surfactant was varied. This series of experiment gave a clear and well defined optimum at X2=0.5. This content of X2, was then used in the next series of experiments, where the ratio between Xi and X3

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was varied (figure 6.1b). This second series of experiment gave also a very clear and well defined optimum of Xi=0.30, which gave the following settings for Xi, X% and X3; 0.3, 0.5 and 0.2. These settings which gave the "maximum" effectiveness in these two series of experiments, could then have been reported as a "optimised" composition for this product.

This is not only a hypothetical approach within dispersant development. Often a fixed level of one of the surfactants is assumed (e.g. a ionic wetting agent) and the ratio between the two other surfactants is “optimised ” for example to find the best etoxylation degree. Then the final content of ionic surfactant is determined with the “optimised ” ratio between etoxylated and non-etoxylated surfactants from the first series of experiments.

B X1

Figure 6.2a-b True distribution of dispersant effectiveness as a function of composition together with the first and second series of experiments.

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However, the real distribution of effectiveness as a function of surfactant composition is shown in figure 6.2a-b. This figure shows that the reported optimised composition is not a true optimum. The OVAT-strategy is based on the assumption that variables are independent and could be varied one at the time, which is seldom correct in the laboratory or in nature. Figure 6.2 shows that there is a strong synergistic interaction between the three constituents in this product.

The efficiency of the OVAT-strategy compared to alternative experimental designs are given in paper 3, where statistical simulations are used to compare different experimental designs.

Fortunately, other experimental approaches then the OVAT strategy are available and should be used, since interactions between process or design variables are the normal situation, not the exception. The main advantage with statistically founded experimental designs is that the combined or interaction effects between the process variables can be estimated.

6.3 Mixture designs

In mixture experiments, the process variables are constituents of a mixture, and their levels are not independent. For example, in chemical dispersant, which consist of a mixture of surfactants dissolved in a solvent, the total amount of surfactants and solvent has to add up to 1.0 (or 100%). If the amount of one surfactant is changed then the relative ratio of all surfactants is changed. In one series of mixture experiments the measured response (modelled variable), such as percentage of oil dispersed by a dispersant, is assumed to depend only on the relative proportions of the components in the dispersant.

For mixture experiments, x* denotes the proportions of the i-th component in the mixture and the number of components is denoted by q.

Equation 6.1: X; >= 0 i - l,2....,q Equation 6.2: Sum(Xj) = x,+x2+....+xq =1.0

The constraints in Equations 6.1 and 6.2 on the value of x, and the sum of all Xj make mixture designs different from other design types such as factorial experiments (see next section). The experimental region of interest, which is defined by the values of Xj, is a regular (q-l)-dimensional simplex. In this thesis q equals 3, (three different surfactants in an oil spill dispersant), then the experimental region is an ordinal 2-dimensional simplex (a triangle). This simplex-shaped experimental region for mixture experiments has lead to the name "simplex designs". Since the experiments can be visualised as a lattice of points in this simplex, "simplex-lattice design" is often used as a name for this type of experimental designs. Figure 6.3 shows the simplex-lattice design with q=3, used in this work, a simplex-centroid design (paper 4 and 5).

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Figure 6.3 Simplex-controid design, with 10 experimental points. A constraint of minimum 10% is used on all three variables.

The required complexity of the simplex design and the response function in this work was studied in paper 3 and these findings were used to optimise surfactant mixtures in paper 4 and 5 and further details are found there. The modelling performed to estimate the response surface describing the distribution of the response variable (figure 6.2) is briefly described in section 7.5.

The design in Figure 6.3 is constructed so that a minimum of 10 per cent of all three surfactants are in all mixtures. This was done because all three surfactants were assumed to be essential for the dispersion process. Dispersants consisting of only one or two surfactants (located along the outer side of the triangle in Figure 6.3) were assumed to show very low effectiveness. For further reading concerning mixture designs see Khuri and Cornell (1987) or Cornell (1990).

6.4 Factorial design

In the dispersant optimisation described in this thesis, factorial designs are used to calculate the effect of different surfactants, oil types and weathering degree on the amount of dispersed oil (paper 5).

To perform a factorial design, the experimenter selects a fixed number of "levels" for each of the process variables and performs experiments in all possible combinations of these variables. In this study only factorial designs with two levels are used.

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6.4.1 Full factorial designs

To give an introduction to full factorial designs, an specific example will be used. If we want to test the effect of three different components in a standardised mixture (same ratio used in all experiments) and for each of these components we have two different modifications or types, then 23 - 8 experiments are needed. For each of the three components (A, B and C) the two different modifications will be called and "+" in the table below. These two modifications could for example be surfactants of the same basic type but with structural differences.

Table 6.1 Possible coding of the design variables for a 23 full factorial design and the measured response.

Run A B C Response (y)

1 - - - 60

2 + - - 72

3 - + - 54

4 + + - 68

5 - - + 52

6 + - + 83 7 - + + 45 8 + + + 80

What can this full factorial design be used for? What does it tell us about the effect of changing between the two modifications of component A or the effect of changing two components simultaneously or maybe changing all three? Since 8 experiments are performed 8 different effects can be calculated. The effect of design variable A is named a and the interaction effect between variable A and B is named ab etc. The calculated effects are:

1 total average effect average 3 main effects: a,b,c 4 second order effects: ab, ac, be 1 third order effects: abc

The first order effect a explain how the response variable y is changed when component a is changed from its to its “+” modification, e.g. if a=23 it means that if the modification of A is changed to the “+” modification the response variable is increased with 23. The second order effects ac explain the interaction between component a and c. An interaction effects ac of 10 means that there is an synergistic effect between a and c, which also influence the response variables.

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6.4.2 Fractional factorial designs

In the 23 full factorial design used as an example in the previous section, 8 experiments were done and 8 effects were estimated. If the same design is used with 5 process variables, 25 = 32 experiments would be needed. If only first and second order interactions were assumed significant, only 16 effects are of interest to the experimenter from these 32 experiments. Since only 16 effects are of interest, it should be possible to reduce the number of experiments to 16. For many purposes this is possible by using fractional factorial designs. The algorithm for selection of the 16 of total 32 experiments (a half-fraction) is described elsewhere (Box et al., 1978) and will not be further explained.

If the assumption that third and higher interactions are negligible is valid, we have gained the same information from only 16 experiments with a half-fractional design and 5 process variables (251 = 16). A smaller fraction of the total number can also be selected (lower resolution), but then fewer effects can be estimated. Degree of resolution and confounding of effects in these designs are further described by Box et al., (1978). If the analysis of the data indicates that the higher order effects could be significant, the second half-fraction (the 16 experiments originally omitted) can be added and a full factorial design will be the result. The risk by doing a half-fraction first is minimal because the experiments can always be supplemented with the second half-fraction, but the saving in the number of experiments (read cost!) is impressive.

The effect of five design variables; oil type, weathering degree, and the type of three different surfactants (SI, S2, S3) on the effectiveness and composition of the optimised dispersant, is studied in paper 5. This is done by using a 2s"1 factorial design.

The interested reader (and all experimenters should be interested!) is recommended to read more about this topic in Box et al. 1978, which gives a clear and "non-statistical" introduction into the field of experimental design, model-building and optimisation.

6.5 Blocking and randomisation

Quality assurance concerning laboratory procedures is important and most analytical laboratories have a high standard within this area. Nevertheless, systematic variation from the laboratory may enter the data and could bias the data analysis. Two important countermeasure to avoid systematic biasing should be considered; blocking and randomisation.

6.5.1 Blocking

In the previous section, the 32 experiments in the 25 full factorial design consisted of two half-fractionals or 25"1 designs. These two "blocks" of 16 experiments could be performed separately and the results merged together afterwards, if the experimenter first wanted to run a half-fraction and then decided to run a full factorial design. This division of the 32 experiments into two blocks could still be favourable if the

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experimenter wanted to run a full factorial design from the beginning. These two half fractions are complementary to each other. If these half-fractions were analysed using two different instruments, a possible systematic difference, between the two instruments, would be cancelled out. If the experiment was divided into two groups in any other way, this systematic difference would interfere with the results. Such systematic variations might occur because the experiments are run by different personnel, on different instruments, in different weeks, with different batches of chemicals, before and after lunch etc. Experiments should, when possible, always be divided into such blocks, to cancel out systematic variation.

6.5.2 Randomisation

When all known systematic interfering variation is cancelled out by a proper blocking strategy, some unwanted interfering variation could still be left. This could be solved inside each block by NOT doing the experiments in a "defined" order, but in a random order. Systematic variation often occurs as a function of time e.g. ageing of a bulb in a UV-instrument or a chromatographic column, temperature variation, oxidation of chemicals etc. To avoid this systematic variation to interfere with the calculation of the effects, the sequence of the experiments should be randomised.

Randomisation of the experimental sequence is done for each simplex-lattice design in paper 4 and 5 and for the experiments in the factorial design in paper 5.

A good strategy for all experimental designs is to "Block what is possible to block and randomise the rest".

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7. Multivariate analysis - building “soft” models

This section presents additional background concerning multivariate analysis performed in papers 3,4 and 5. This background material is included to make the thesis more complete for persons new to this area, and hopefully to motivate them to try to utilise their data better by using multivariate analysis.

7.1 Model building

"A model is a simplified intermediate representative, intended to have, from a specific perspective and for a specific purpose, a structural or functional analogy to some phenomenon in the inaccessible complex reality" (Martens and Nass, 1989). A good model is an adequate compromise between simplicity and completeness. The former implies sufficient interpretability; the latter implies sufficient realism and detailed description.

It is important to bear in mind that models are only approximations to the real world. All models are “wrong ”, but some models can be very useful. In science we express our models in form of mathematical expressions. The science of model building is often a matter of fitting real life data to mathematical expressions or getting mathematical expressions from real life data.

Equation 7.1: y = F((3„p 2....|3n, x„ x2....xn) + e

A general expression for a model is given in Equation 7.1 and describes a process with a response variable y and some process variables x„ x2....xn. The residuals (e) describes the difference between y-value estimated by the model and the real measured y-value. The function F describes the functional relationship between x„ x2....xn and y. The 13- values (l...n) are constants in the function, often used to fit the model to measured data (minimise the e-values).

There are two main approaches in model building:

(1) To build the model on a theoretical or empirical understanding of the process. This model equation is then fitted to the experimental data available and constants in the equation are changed (or tuned) until an adequate fit is obtained (small residuals). This type of modelling is often called "knowledge-driven" or “hard” modelling. The IKU weathering model described in paper 1 is an example of this type of models.

(2) The second alternative is the search for systematic structures in the data. This is done with minimum use of prior knowledge, and these structures are then used to build a model with adequate fit. The prior knowledge about the process is then used to interpret this "data-driven" or “soft ” models. This approach is used in the multivariate modelling described in this section.

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7.2 Pre-treatment of data

Projection techniques like Principal Component Analysis (PCA) and Partial Least Squared regression (PLS) are so-called scale dependant. This implies that the results will be dependant on the scale (e.g. mm or m, grams or kilos) of the original variables.

To avoid unexpected influence of different scale and variance in the variables the data in this study are centred and standardised before the PCA and PLS analysis. This is performed by subtracting the mean value (centring) and dividing on the variance (standardisation). This pre-treatment gives all variables used in the analysis equal weight and a distribution centred around zero (|d = 0) and with equal variance (S = 1).

7.3 Explorative data analysis - Principal Component Analysis

This section will give a brief introduction to PCA, further details are available in several text books (e.g. Esbensen et al., 1994 or Nortvedt et al., 1996) and in tutorial publications e.g. Wold et al., 1987 or Kvalheim, 1988).

Large data matrices often contain information which is difficult to extract and interpret due to high internal correlation and noise in the data. Efficient interpretation of complex data sets should include an extraction method to identify and visualise the important trends in the data material.

One basic method for multivariate explorative data analysis is Principal Component Analysis (PCA). PCA is used to extract the main trends in the data material from the general background noise. Often the first few principal components (PCs) can explain large portions (e.g. 70-80%) of the total variation in large correlated matrices for example chromatographic profiles with several hundred variables.

The first principal component (PC) is the linear combination of the original variables which explains as much as possible of the total variation in data matrix (X). This PC is then subtracted from X and the next PC is a new linear combination which explains as much as possible of the residual variation in X and is orthogonal to the first PC. The PC is defined by the loading vector e.g. PCI = p,,-v, + p )2-vi...... pi p-vp. This extraction of principal components can be continued until we have reach the number of variables (p) or the number of objects (n). However, since the main objective with PCA is dimensional reduction the number of PCs is often small compared to p and n. If too many PCs are extracted noise will be included into the structure part (TP1) and the trends we want to study will be difficult to interpret. Several procedures exist to estimate the optimal number of PCs based their predicting ability; crossvalidation and leverage correction (see section 7.6). However, careful interpretation of the variation described by the principal component is always important.

As illustrated in figure 7.1 PCA can be used to decompose the data matrix (X) into a structure part (TP1) with orthogonal properties explaining the systematic variation in the data matrix and a residual matrix (E) containing the random noise.

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1 - - - - p 1 - - - - p P1 P2

X = t1 + *2 + • • • + E

X = TPt + E (X: centred data matrix, T: score matrix, PT: transposed loading matrix and E: residual or noise matrix )

Figure 7.1 The data matrix (X) decomposed into a structure part (TP7 ) and noise (E) (from Esbensen etal., 1994).

A plot of the principal component scores (T) explains the main relationship between the samples (objects), while plotting the principal component loadings (P) explains the contribution and the correlation between the measured variables.

In this thesis, PCA is used to map the properties of different oil types with different weathering degrees based on measured physical/chemical properties in paper 5.

7.4 Multivariate prediction with the PLS algorithm

This section presents a very brief introduction to the theory behind, and the use of the Partial Least Square (PLS) approach for multivariate calibration. Some attention is also payed to the two alternative regression methods; Multiple Linear Regression (MLR) and Principal Component Regression (PCR). Further details concerning theory and use of PLS is available in several excellent text books (e.g. Esbensen et al., 1994 or Nortvedt et al., 1996) and in tutorial publications e.g. (Kvalheim, 1988 and Hoskuldsson, 1988).

Identifying important trends in the data material by PCA is often the first step in selecting objects for a calibration set using PLS to build a multivariate model. When an adequate model is established, it can be used to predict new values for the modelled variable. One of the simplest regression models, the univariate regression line, can be used to predict the value of y for new samples where only x is known. These predicted values should be clearly distinguished from the measured values of y. If the model should give an adequate description of the process and give meaningful predictions, the samples in the calibration set must be representative. The term “representative” means that all the effects or properties we want to model in our process must be described by the calibration samples.

Multiple Linear Regression (MLR) is often used for multivariate calibration, but suffer from several limitations. The most important limitation is the assumption of

Dr. thesis Per Johan Brandvik, March 1997 - 38 -

uncorrelated variables. MLR assumes that the rank of the X-matrix is the same as the number of variables. Also, if the number of variables is larger than the number of objects, the needed inversion of the X-matrix fails, and MLR can not be used. This is often the situation in multivariate data analysis, for example with spectroscopic data were the number of (strongly correlated) variables can be very large compared to the number of samples.

Principal Component Regression (PCR) is another alternative to PLS and avoids the two important limitations from MLR, by using the principal components as new variables in the multiple linear regression. The PCs satisfy the strict conditions in MLR by being uncorrelated (orthogonal) and are usually few compared to the number of samples, so the new matrix can easily be inverted.

However, PLS offers an advantage over PCR utilising the correlation between the X and Y matrix in an iterative calculation of the PLS scores and loadings. In practice, this usualy imply that PLS discovers the main trends in the X matrix, which correlates with the main trends in the Y matrix. In this way PLS, different from MLR and PCR, can handle Y matrices with several y-variables and utilise the internal correlation within the Y matrix. If PCR is used with several y-variables, a separate model has to be calculated for each y-variable.

A PLS model, which utilises the correlation between X and Y, often consists of fewer components than a PCR model, explains more of the total variance and the components are often easier to interpret.

Calculation of the PLS component is an iterative process (figure 7.2) where information from the Y matrix is used to find the latent variables in the X matrix. The score matrix U from Y is used for the X-decomposition, t, is replaced by ui, thereby letting the Y- data structure directly guide the decomposition of X. The loadings calculated in this first step are called weights wj. In the same manner, u, is substituted by t, in the subsequent decomposition of Y. Calculation of the first PLS component using the U|=>L and t|=>U[ substitutions, is then continued until convergence, where a final set of t,, w,, p, and ui, qi is calculated.

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X = ITPt + E A y = iu -qt + f A

Figure 7.2 X, W, P, T and Y, U, Q matrices used for multivariate calibration with the PLS algorithm (from Esbensen etal., 1994).

In this thesis, the PLS algorithm have been used to establish a multivariate model describing the influence of surfactants, oil types and weathering degree on the optimum dispersant composition. This model was then used to estimate optimum composition for new oil types and weathering degrees not included in the calibration set. This work is presented in paper 5.

7.5 Response surfaces modelling

A calibrated model should describe how the process variables influence on the modelled variable or the response variable(s). When this relationship is adequately described, the model can be used to predict the settings for the process variables which give the optimal value for the response variable(s). A model used for optimisation must often be able to describe a high degree of complexity since we often are interested in describing regions with large changes in the response variable. An example of this is to find the optimal composition of an oil spill dispersant (process variables: x„ x2, x3), which gives the highest effectiveness (response variable: y). This is also an example of a process which we know that the interaction between the design variables are large and a high degree of complexity of the models would be needed.

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7.5.1 Response surface methods and mixture designs

We will limit this introduction to response surface methods to experimental data from the mixture designs, which is used in papers 3,4 and 5 (simplex-lattice designs and response surface methods).

Both Scheffe (1958 and 1963) and Cox (1971) developed canonical polynomial models with constraints on the coefficients which are widely used to analyse mixture data (Cornell, 1990). Our approach using the simplex-centroid design and the Cox-Scheffe models is further described in paper 3.

Different types of Scheffe-Cox models were initially tested to approximate the response function in this multiple regression approach; linear, quadratic, cubic and special cubic were tested and the lack-of-fit calculated. Since the complexity of this system was known to be large (Brandvik and Baling, 1990), a special quartic equation (Cornell, 1990) with a constant term was used to describe the response surfaces in this study (see equation 7.2 and example in figure 6.2). This high complexity of the model was necessary, due to the strong molecular synergy between the surfactants.

The number of experimental points in the design also limits the complexity of the estimated model, so that only 10 parameters or coefficients for the model equation describing the dispersability of the oil can be estimated, without over fitting the model (Khuri and Cornell, 1987).

Equation 7.2: The special quartic equation describing the response surface in figure 7.3.

y = Po + Pl*i + P2X2 + P3X3 + Pi2X,X2 + P,3X,X3 + P23x2x3 + Pn23xi X2X3 + Pi223X jX 2 X3 + p l233X,X2X3

In regression models with high complexity like this (equation 7.2) it is often difficult to interpret the coefficient. In this study the response surfaces with sufficient lack-of-fit (see section 7.6.3) were used to find the optimum region in effectiveness without any thorough chemical interpretation of the coefficients.

Examples of models with a high degree of complexity used for optimisation purposes is the response surface modelling in paper 4 and 5, where data generated with simplex- lattice designs are used to describe the effectiveness of an oil spill dispersant as a function of surfactant composition.

7.6 Validation of models

In the ideal world an experimenter should always have two different data set; one to calibrate the model and a second independent data set to validate the model. The cost of performing extra experiments, not used to refine our models, is often difficult to justify. As a compromise we can sequentially use parts of the calibration set for validation, this technique is by statisticians often referred to as “jack knifing ” in chemometrics the

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approach is called “cross validation ”. Three other validation approaches used in this study are also briefly presented in this section; validation of response surfaces based on a F-test, a quick approximate method based on the samples residuals (leverage correction) and normal probability plot of regression coefficients

7.6.1 Crossvalidation

In cross validation we utilise all available objects for model calibration but not simultaneously. The model is calibrated using one part of the data and the other part are used for validation, calculating the prediction error. This is continued until all parts of the data have been used for validation, then the mean prediction error is used to validate the model. Mean squared prediction error (MSPE) versus the number of the principal component can be used to estimate the optimal number of principal components to avoid including noise in the model.

Cross validation can be performed by dividing the data material into different number of groups from two to “full crossvalidation ”. Full crossvalidation implies that only one object is kept outside the calibration and used for validation in a sequential order until all samples have been omitted once. This imply that 100 models have to be calculated in a data set with 100 samples, this can be very time consuming if the number of variables is high. Larger parts of the data set can also be selected e.g. 20% of the objects (segmented crossvalidation) giving a rougher estimate of the prediction error, but increases the of the calculation speed.

Using cross validation on small and well balanced data set, typically resulting from designed experiments is difficult. All the samples are equally important to span out the variance and omitting one of them in an sequential order, would often create very high prediction errors.

In paper 5, a PLS model is built from a fractional factorial 25"1 design without replicate measurements. This data set is not suitable for validation with the cross validation algorithm and predictions should be used with care. Further details concerning cross validation are presented earlier e.g. by Wold (1978).

7.6.2 Leverage correction

Leverage correction is a quicker, but a less reliable validation method compared to cross validation. It is quicker because it only requires one model to be calculated, less reliable because it tends to underestimate the prediction errors. Leverage correction is often used as a quick method in an early stage of a modelling study and replaced with cross validation for the final validation.

Leverage is a measure of the effect of an object on the established model, and is related to the objects distance from the centre of the model, also called residuals. An extreme object, with a large distance to the model centre, will have a high leverage and a high estimated prediction error. A typical object for the model with a small residual will have

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a corresponding small estimated prediction error. Further details concerning leverage correction are given earlier e.g. by Esbensen et al., (1994).

7.6.3 Testing response surface models for lack-of-fit

Only response surface which showed a good "fit" to the experimental data, should be used for interpretation of the relationship between the response variable(s) and the process variables.

In the response surface work described in papers 4 and 5, replicate IFF measurements, were performed and the prediction error was calculated and compared to the uncertainty in the measurement of the response variable. The squared prediction error (SPE), here called F is calculated in the following manner:

Equation 7.3: Quantification of lack-of-fit; F:

F - Z(y measured y predicted) / XCymcan " y measured)

This F-value was used to quantify the lack-of-fit of the estimated response surface to the measured data:

1. No lack-of-fit : F is small (error in prediction < uncertainty in measurements) 2. Lack-of-fit : F is large (error in prediction > uncertainty in measurements)

When this F-value is corrected for the degrees of freedom in the experiments, it is approximately Fisher distributed. The critical value for a given significance level can then be determined. Only response surfaces with a sufficient lack-of-fit (5% significance level or better) were used in papers 4 and 5.

7.6.4 Significance of the estimated regression coefficients

If no replicate measurements are performed and no standard errors can be calculated, we still have an option to estimate the significance of the calculated effects. This approach is plotting the calculated effects in a normal probability plot (Box. et al., 1978). If we assume that all effects are not significant and only represents background noise, they should be normal distributed and form a straight line, when plotted in a normal probability plot (figure 7.3).

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Up, Y-var: IFP%

Figure 7.3 Normal probability plot of the effects calculated from a the fractional factorial design performed without replicate measurements from paper 5.

The main effects in figure 7.3 are named A, B, C, D and E, the interaction effects between A and D is then labeled AE and so on. If none of the effects in figure 7.3 were significant they should fit into a straight line. Figure 6.4 shows that some of the effects; E, A and AE are clearly significant, while others; AB, BD, BE and CD have a smaller, but probably significant, deviation from the normal distribution. Six of the calculated effects fit on the straight line and are assumed not significant.

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8. Use of dispersants to reduce environmental impact from oil spilled at sea

Since this thesis describes a new approach to dispersant optimisation (paper 3,4 and 5) and oil spill scenarios using dispersants as an response option against oil spills at sea (section 9), this section presents background information concerning dispersants; what . dispersants are, how they work and why and how they are used.

8.1 What is a dispersant?

A dispersant is a mixture of surface active components (or surfactants) in a solvent or a blend of solvents. Surfactant molecules have both water-compatible (hydrophilic) and oil-compatible (lipophilic) parts. The hydrophilic part of the surfactant molecule could be e.g. an carboxy group, one or several hydroxy units, conjugated double bound or even a ionic charge. The lipophilic part of the surfactant is often an aliphatic chain e.g. from a fatty acids. Surfactants used in modern dispersants are often the same surfactants used in cosmetics and by the food industry due to their low toxicity and high biodegrad ability. Figure 8.1 shows some examples of some frequently used surfactants in modern oil spill dispersants (NRC, 1989 and MAFF, 1995).

(CH2)7C------OCHgCH CKO — C(CH2)7CH

CH CH20— C(CH2)7CH CH2COOR R= iso-octyl CH 0 CH

Na<±> ©SO3C-CHCOOR (CH2)7CH3 (CH2)7CH3

A: Diethylhexyl sulfosuccinate Sodium salt B: Sorbitan monooleate ester

(CH2)7CH3

(CH2)7(CH2CH20)„C 0. .OH o o

(CH2)7(CH2CH20)nC —OCH2CH o'

CH I II C(CH2CH20)„(CH2)7—CH=CH(CH2)7CH3 CH II I 0 (CH2)7CH3

C: Ethoxylated sorbitan trioleate ester

Figure 8.1a-c Examples of surfactants used in modem oil spill dispersants (from NRC, 1989 and MAFF, 1995)

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The solvents in the dispersant are used to dissolve solid surfactants and to reduce viscosity. Reduced viscosity is needed so that the dispersant can be applied onto the oil slick by spraying. Dispersant may consist of from 10 to 60 % of surfactants in the solvent. The nature of the solvent is important since it is an important contributor to the total chemical and physical properties of the dispersant (Fioco et al., 1995).

Early dispersants, manufactured in the early seventies after the Torrey Canyon oil spill, were produced with hydrocarbon solvents and a low content of surfactants, to be used in high ratios compared to the oil. These are sometimes known as type I dispersants. Dispersant have also been produced with water-miscible solvents, to be diluted in water before applied to the oil slick (known as type II dispersants). More details are given in the introduction to this thesis.

Today, most dispersants are concentrates and are applied directly onto the oil slick at a concentration of approximately 2-4% of the oil. In this work the term “dispersant” only refers to modern concentrated dispersant, also called type III dispersants.

8.2 How do dispersants work?

It is common knowledge that “oil and water do not mix”, but oil might be dispersed into the water as small droplets. If a small amount of oil is shaken in a glass jar together with water, small oil droplets will form in the water phase, but after a few minutes settling, a continuous oil film will be formed again. At sea, waves or other turbulence will naturally disperse an oil slick into small oil droplets, but the largest droplets (above 70 jam in diameter) will still resurface into the oil slick again (see section 5.4).

Dispersants enhance this natural dispersion by creating a higher number of oil droplets that are small enough to be permanently captured in the water column and resist resurfacing. For this reason a higher fraction of the oil slick will be removed from the sea surface. The energy level needed to start the dispersion process (waves or other turbulence) will also be significantly lowered when dispersants are used.

The surfactants are the active components in the dispersant and the combined lipophilic and hydrophilic properties lower the interfacial tension between oil and water. The lowering of interfacial tension occurs because the surfactants migrate to the oil-water interface and orientates themselves with the lipophilic part into the oil and the hydrophilic part in the water.

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1. Application of dispersant Surfactant Air Hydrophilic Group Lipophilic Group

Water

2. Surfactant locates at interface, depressing interfacial tension

3. Oil slick disperses into small droplets with minimal energy

;; ffiail ,*" ,’rr:"V;:'1; Oil droplets

r^.i ndSR);-: %: adm4i00:tegner/psd'chem_dis.eps

This lowering of interfacial tension from a typical value of 20-30 mN/m without surfactants, to below 0.1 mN/m with surfactants, stabilises the droplets formed and this stabilisation reduces the size of the droplets and increases the number of the droplets formed. Using a combination of different surfactants is often more favourable than using a single surfactant, since more effective packing of surfactants is obtained when surfactants with different molecular size are used. The surfactant interaction at the oil- water interface will also be different for different oil types and weathering degree.

Different dispersants show varying effectiveness with the same oil type, due to different interaction between surfactants and oil components at the oil-water interface. This is illustrated in paper 2 were different commercial dispersants are tested with different oil types and at different environmental conditions (temperature and salinity).

8.3 Why use Dispersants?, pros et cons

As stated in the previous section, dispersants are used to enhance the natural dispersion rate of an oil slick into the water column. This will reduce the amount of oil drifting at the sea and reduce the risk of damage to birds active on the sea surface. The possibility of the oil reaching the shoreline and damaging environmental resources such as bird or seal habitats is also reduced. The potential of impact on other resources like, recreation

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areas or fish farms, is also reduced by treating oil slicks with dispersant before it comes ashore.

Table 8.1: Pros et cons for using dispersant to enhance natural dispersion and remove surface oil.

Benefits Disadvantages Reduced damage to marine fowl and other Increased concentrations of oil in the animals at the sea surface water column Reduced damage on natural resources on May cause sub-lethal effects on beaches marine organisms Enhanced biodegradation of dispersed oil Not effective on all oil types compared to surface slick Short mobilisation time is possible Reduced effectiveness on heavily weathered oil Reduce formation of W/O-emulsification of “Time window” for use may be treated, but not dispersed oil limited in some scenarios

Dispersing the oil as small droplets into the water column increases the rate of natural biodegradation, due to the increase in oil-water interface area. However, as indicated in the table above, the increased oil concentration in the water column due to the use of dispersant may cause toxic effects to marine organisms. This topic is discussed further in the next section. In some cases where oil slicks have a short drifting time to vulnerable resources, dispersants application from helicopters could be the only effective response option, due to short mobilisation time. One of the limitations of the dispersants available today is their low effectiveness on some oil types (e.g. heavy bunker fuels) or heavily weathered oils. Dispersant use can be an effective oil spill response option in many oil spill scenarios, but several consideration have to made before dispersion is used; 1. Expected effectiveness of the dispersant on the particular oil type and weathering degree 2. Natural resources threatened by the drifting surface oil slick (e.g. bird/seal habitats) 3. Natural resources affected by the dispersed oil plume before being diluted (e.g. fish spawning grounds) 4. Is the existing water depth and water circulation sufficient for rapid dilution of dispersed oil?

The effect of several response options (“doing nothing ”, mechanical recovery and use of dispersants) is illustrated by an oil spill scenario study in section 9.

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8.4 Environmental impacts of dispersant use

This chapter gives a brief introduction to the possible environmental impact of dispersant use. Further details concerning using dispersant to obtain a net environmental benefit are discussed by Lewis and Aurand (1997).

The potential environmental impact to marine organisms by using dispersants on an oil spill is mainly caused by the increased oil concentration in the water column (see chapter 8.2). The oil in the water column is both present as small droplets and as dissolved oil components, leached out from the dispersed oil droplets. These dissolved components, called water soluble fraction (WSF) are generally considered to be the main source for a potential acute toxic effects an marine organisms (Neff, 1995 and McAuliffe, 1987).

Dispersants consist of low toxicity and biodegradable components and have only a minor contribution to the total toxicity compared to the toxicity from the oil. Bobra and co-workers (1984) and Mackay and Wells (1983) separated the toxic contribution from the dispersed oil droplets, WSF and the dispersants in several series of laboratory experiments. They used Norman Wells crude and the salt water organism Daphnia Magna. Their conclusions, assuming that the dispersant is correctly applied with a suitable dosage (2-5% of surface oil) before the oil slick becomes too viscouse, are summarised in table 8.2.

Table 8.2: Relative contribution to total toxicity from WSF, dispersed oil droplets and dispersant, Experiments performed with Norman Wells crude, Corexit 9527 and Daphnia Magna.

Water soluble Dispersed oil Dispersant alone fraction - WSF droplets Fresh crude 85% 14% <1% Weathered crude 10% 87% 2% (42% evaporated)

However, field trials have indicated that most of the light-end aromatics in the fresh crude are removed from the oil slick by evaporation before dispersants are used, and no significant increase of WSF after dispersion is observed (e.g. Brandvik et al., 1996b, Strpm-Kristiansen et ah, 1996).

It is not the concentration of dispersed oil or WSF itself which causes the toxic effect, but the exposure. Exposure is defined as concentration (ppm or 10"6) multiplied by time (hours). The term “ppm - hours ” is used to quantify the exposure of the marine organisms to dispersed oil or dissolved components. Since the composition of the WSF is dependant of the oil type, the toxicity will also be different. However, several studies have indicated a lower limit for measuring effects of WSF on marine organisms to be in the area of 0.5 ppm-hour for plankton and fish eggs and up to approx. 30-40 ppm-hours for higher marine life such as e.g. older larvae, adult fish and shellfish (e.g. Serigstad, 1991, Bprresen, 1993, Rice, 1985 and NRC, 1989).

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Static laboratory screening tests with high concentrations and long exposure periods are today often used as screening tests for both single surfactants and dispersants. Results from the toxicity screening method used for dispersants in Norway (LC50 after 96 hours exposure with Skeletonema Costatum) is presented in paper 5. These tests gives relative toxicity and should only be used to exclude surfactants or dispersants with significantly higher toxicity than comparable products. Other laboratory tests methods which aim towards simulating field conditions with respect to exposure (declining concentration versus time due to dilution) are also available (Singer, 1991). These new approaches should be used to obtain a more realistic measure of potential effects of dispersed oil, WSF and dispersant on marine organisms.

A: Before dispersant treatment — 1m

Distance across underwater plume (m)

B: After dispersant treatment

100 150 200 Distance across underwater plume (m)

Figure 8.3 Concentration profiles of dispersed oil in the water column at 1, 5 and 8 meters depths before (A) and 20 minutes after dispersant application (B) on a 15 ni experimental oil spill during the NOFO 1995 sea trial (from Brandvik et al, 1996a).

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Figure 8.6 shows the concentration of dispersed oil in the water column during offshore testing of dispersant application. The concentration profiles show concentrations of dispersed oil in the range of 0.1-0.3 ppm before and 2-20 ppm immediately after dispersant application. Similar concentrations have been observed also in earlier sea trials with dispersant treatment of oil slicks (Lichtentaler and Baling, 1983, Lichtentaler and Baling, 1985, Lunel et ah, 1995b, Lunel et al., 1996, Lewis et ah, 1995b, Brandvik et ah, 1995, Brandvik et ah, 1996a, Brandvik et ah, 1996b and Strpm-Kristiansen et ah, 1996).

The corresponding concentrations of WSF under a fresh crude oil spill during the first hours after release are in the range of 7-30 ppb (McAuliffe, 1989, Brandvik, et ah, 1996b and Str0m-Kristiansen, 1996). Buring the Norwegian sea trial in 1996 dispersant was applied after 4 hours of weathering and no significant increase in WSF was measured. The concentrations were in the range of 7-23 ppb both before and after dispersant application. In this case, most of the light-end components had probably evaporated before the dispersant was applied (Brandvik, et ah, 1996b and Strpm- Kristiansen et ah, 1996). These increased concentrations of WSF immediately after release of an oil spill and even a possible limited increase of WSF after dispersant application only affect a relatively limited water volume for a limited period of time (a few hours).

The Norwegian field trials in 1994, 1995 and 1996 showed that the concentration of dispersed oil was reduced to less than 0.5 ppm (a factor of 10-50) 3 hours after dispersant application, due to natural dilution of the dispersed oil plume (Lewis et ah, 1995a, Brandvik et ah, 1996a and Brandvik et ah, 1996b).

This brief presentation of experimental field results indicates that only limited water volumes are exposed to water soluble components (WSF) above the indicated limits for acute toxicity (0.5-40 ppm-hour), when dispersant is correctly applied in areas with good water dilution. Similar conclusions have also been made in earlier studies e.g. in NRC (1989), McAuliffe (1987) and Bprresen (1993).

8.5 Application of dispersants

In Norway, the two main methods for dispersant application are; 1. application by spraying systems from boats 2. application by underslung helicopter systems

Application equipment for boats are only mounted on some offshore supply vessels and on tugboats in harbours and terminals. Only relatively small quantities of dispersant (2-5 m3) for treating limited oil slicks (20-100 m3) are stored onboard these vessels. The size (e.g. 2 x 8m) and spraying capacity (e.g. 2 x 301/min) of the existing sprayarms are also limited. However, the Norwegian Clean Seas Association (NOFO) is currently developing a new and improved boat spray system, based on the recommendations after the 1995 sea trial (Brandvik et ah, 1996a). This system will be more suitable with

Dr. thesis Per Johan Brandvik, March 1997 -52-

respect to spray width and capacity for operation from the large supply vessels used in connection to Norwegian offshore oil production.

To utilise the large helicopter resource used for offshore personnel transport in Norway. A new helicopter bucket called Response 3000D, has been developed for aerial dispersant application. A helicopter bucket consists, of a dispersant container, sprayarms and a pump. This new system has been developed in close co-operation with Norwegian oil companies to fulfil the need of the Norwegian oil industry (Brandvik et al., 1996c). Use of this new system for aerial dispersant application during an offshore field testing spraying dispersant on a 15 m3 oil slick is shown in figure 8.3 and 8.4 below.

This helicopter bucket for dispersant application is used in the oil spill scenarios in chapter 9.

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Figure 8.4 Offshore filling of dispersant into Response 3000D directly from a supply vessel using the suction hose (from Brandvik et ctl, 1996b).

Figure 8.5 Spraying of dispersant during the NOFO 1996 sea trial with the Response 3000D helicopter bucket (from Brandvik et at., 1996b).

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8.6 Dispersants present role and possible future role in Norwegian oil spill contingency

The current Norwegian regulations for approval and use of dispersants are from 1980, but new regulations are now being issued. The old regulations reflect an earlier strict Norwegian policy toward using dispersant from the mid-seventies, while the new regulations recognise the use of dispersant as one of several alternative oil spill response options.

The main philosophy in the old regulations from 1980 was that dispersant suppliers had to apply for approval of individual products based on effectiveness, toxicity and biodegradability testing. Products which passed this testing were included on the official list of approved products. The responder had to apply for and receive a permit from the authorities, before using significant amount of dispersant (>1 m3). In case of an oil spill, the time needed for obtaining such a permit was considered, by the oil industry, to be too long to make use of dispersants a realistic response option.

The main philosophy in the new regulations issued in 1997 is that the oil industry itself is responsible for optimal use of dispersants following the “internal control ” system. Use of dispersant should be considered both as a supplement and as an alternative to existing methods. The best response option with respect to net environmental benefit should be used. No dispersant testing or record of approved products will probably be issued or maintained by the authorities. Necessary screening of products with respect to toxicity and effectiveness on the actual oil types will be the responsibility of the industry. Pre-approval for dispersants use will be given, for use within defined criteria, in connection with approval of contingency plans. In this way, dispersant can be used without delay in case of an oil spill utilising the advantage of the short response time.

The new regulations, together with new equipment for applying dispersant (chapter 8.5), are expected to increase the future interest from the Norwegian oil industry to use dispersants in their oil spill contingency plans.

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9. Operational use of dispersants - a case study

Three oil spills scenarios are used to illustrate the possibilities and limitations of using dispersant as an operational counter measure technique. The scenarios describe different oil spills off the coast of Trpndelag. Realistic natural resources (nature reserves, spawning grounds, bird habitats and fish farming areas) and oil spill contingency resources (mechanical recovery and dispersant capabilities) are described as a part of these case studies. This information has been compiled from the National Contingency Plan for acute pollution in the Trpndelag region (S0r Trpndelag County, 1996).

A modelling system developed at IKU, SINTEF, called Oil Spill Contingency and Response (OSCAR) is used to describe the behaviour of an oil spill at sea and the effect of different response strategies. OSCAR consists of an oil weathering model, a 3D oil trajectory model, an oil spill response model, a GIS database containing natural resources and an oil spill contingency database. A further description of the OSCAR modelling system is given elsewhere (Reed et al., 1995).

9.1 Natural resources in the area

The coastline outside Tr0ndelag is very complex with a few large islands (Hitra and Fr0ya), pluss probably thousands of minor islands. The area has nature reserves with international status and many areas of high national and regional importance. The area is also important for leisure boating, hunting and commercial fish farming. These oil spill scenarios are mainly used for illustrative purposes and the complex structure of the nature and commercial resources is simplified to serve this purpose.

The main natural resources in the area affected by the scenarios are listed in table 9. la-b and shown on the map in figure 9.1.

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Table 9.1a Overview of the natural resources (Scenario 1 and 3).

Name of area Type of resource ID Index1 Season 2 Froan nature reserve Seals and bird habitat N1 B A Gjaessingen Bird habitat, nesting/winter area B1 B S+W Several locations Fish farms A D A Halten Bird habitat, nesting area B2 B S Toskjaeret Bird habitat, nesting area B3 A S

Table 9.1b Overview of the natural resources (Scenario 2).

Name of area Type of resource ID Index1 Season 2 Vzeret Bird habitat, nesting/winter area B4 B A Gjesingen Bird habitat, nesting/winter area B5 C A LinesOya Bird habitat, nesting/winter area B6 B S+W Stor0y Special beach fauna A1 B A Valsneset Special beach fauna A2 B A Several locations Fish farms A D A Bjugn fjord Local spawning grounds FI B W+Sp ’) Seasonal index; S: summer, W: winter, Sp: spring, F: fall, A: All year. 2) Priority index for protection against oil spill (A: highest - D: lowest priority)

Figure 9.1 The coastal area outside Tr0ndelag used for these oil spill scenarios and the most important natural resources. -57-

9.2 Oil Spill contingency resources in the area

The main oil spill response equipment in this area are located at 0rland (figure 9.1) where both the inter-community oil spill contingency and the Norwegian Pollution Control Authorities have equipment stored (table 9.2). The depot has one part-time employee who has to alert the rest of the contingency staff in the event of an oil spill. Additional mechanical recovery equipment is also located other places in the area (Fr0ya, Hitra, Bjugn and Afjord), but this is small-scale equipment, mainly for use in harbours and sheltered areas. This equipment is not included in this study.

The new helicopter bucket for dispersant application, “Response 3000D” (Brandvik et ah, 1996c) is, for the time being, only existing as a prototype and the “location ” of this unit and dispersant at 0rland is defined for this study only (table 9.2). The helicopter bucket is assumed to be operated by the 330 Search and Rescue helicopter Squadron. This squadron is on a 24 hour alert, with a very short mobilisation time.

A standby vessel is located nearby an exploration drilling platform active in the area, and the vessel is equipped for mechanical recovery and dispersant application (table 9.2).

The Norwegian Clean Seas association (NOFO) has an oil spill contingency base in Kristiansund (120 km south of 0rland, see figure 9.1), containing heavy equipment for mechanical recovery offshore. This equipment is included in scenario three, the underwater blow-out.

Table 9.2 Real and tentative oil spill countermeasure resources located in the scenario area.

Location Type of resource Capacity ID 0rland Mechanical recovery unit, medium size 40 m3/h Ml 0rland Dispersant equipment, helicopter bucket unit, 3 m3 D1 with FLIR camera 0rland Dispersant storage 50 m3 Exploration rig - alternative I Mechanical recovery unit, on stand-by vessel 50 m3/h M2 - alternative II Dispersant unit, on-board stand-by vessel D2 Kristiansund Mechanical recovery units, large size 300 nvVh M3

Dr. thesis Per Johan Brandvik, March 1997 -58 -

9.2.1 Specifications of equipment

Mechanical recovery - 0rland base (Ml) Boom system type/length: “Ringnot ” boom, 200 m Skimmer type/capacity: Foilex GT185, 40 m3/hour Mobilisation time: 5 hours Time to deploy system: 1 hour Operational speed: 0.5 knots Cruise speed, vessel: 12 knots Recovery effectiveness: 80%*

Mechanical recovery - Standby vessel (M2) Boom system type/length: “Ringnot ” boom, 200 m Skimmer type/capacity: “Foxtail ” VAB 8-9, 50 m3/hour Mobilisation time: 0.5 hours Time to deploy system: 1 hour Operational speed: 0.5 knots Cruise speed, vessel: 12 knots Recovery effectiveness: 80%*

Mechanical recovery - Kristiansund NOFO base (M3) Boom system type/length: “Ro-boom 3500” - 400 m Skimmer type/capacity: “Transrech 300 system” - 300 m3/hour Mobilisation time: 17, 19 and 26 hours (system 1, 2 and 3) Time to deploy system: 2 hours Operational speed: 0.5 knots Cruise speed: 15 knots Recovery effectiveness: 80%*

This recovery effectiveness is estimated for daylight operation until 10-15 m/s wind and estimate that 80% of the surface oil entering the boom systems will be recovered (Reed etal., 1995).

Dispersant equipment - 0rland base (Dl) Equipment type: Helicopter bucket - Response 3000D Spraying capacity: 200 or 900 1/min, (twin nozzle system) Spraying width: 30 m (2001/min) and 23 m (900 1/min) Mobilisation time: 1 hour Dosage rates: 2 - 4% dispersant (compared to water-free oil volume) Operational speed: 30 - 60 knots Total bucket capacity: 30001 Dispersant amount stored: 50 0001 Treatment effectiveness: 80%** Spraying guidance system: Forward Looking Infra Red camera (FLIR)

Dr. thesis Per Johan Brandvik, March 1997 -59 -

Dispersant equipment - Standby vessel (D2) Equipment type: Modified “Boat Spray ” system Spraying width: 30 m (2 x 10 m spray arms + boat width) Mobilisation time: 0.5 hour Spraying capacity: 100 l/min Dosage rates: 2 - 4% dispersant (compared to water-free oil) Operational speed: 3-5 knots Dispersant amount stored: 50 0001 Treatment effectiveness: 80%" Spraying guidance system: Visual observations from vessel

This effectiveness of dispersant treatment is estimated for daylight operation in until 15 m/s wind and estimate that 80% of the surface oil hit by 2-4% dispersant will be dispersed into the water masses with a half-time of 1 hour (Reed et al, 7995).

9.3 Oil spill scenarios

It is not the intention of this study to simulate an oil release from any authentic installation or vessel operating in this area. A hypothetical exploration rig “Green Explorer ” and freight vessel “MS Freighter ” are “constructed ” for these scenarios. The exploration rig is located approx. 70 km outside 0rland. The closest real offshore installations at Haltenbanken is Draugen, approximately 90 km outside 0rland.

The oil spill scenarios are constructed to visualise some of the weakness and strengths of using mechanical recovery and dispersants to remove the surface oil before stranding. However, all three scenarios are realistic in many respects and are defined taking local conditions into account (natural resources, environmental conditions, oil types etc.).

Only simulations of the surface oil and the influence of the response option on this oil are shown in this section. Concentrations of the dispersed oil and water soluble fractions (WSF) in the water column is only briefly discussed.

Dr. thesis Per Johan Brandvik, March 1997 - 60 -

9.3.1 Scenario 1: Medium offshore spill

The exploration rig “Green explorer ” is drilling offshore 70 km outside 0rland or 30 km from Froan nature reserve. Since it is drilling relatively close to the coast, the rig is accompanied by a standby vessel with oil spill response equipment (table 9.2). During a test production of a promising well, problems with the process equipment on the rig causes a limited release of crude oil. The oil is released from the platform deck into the sea.

The following conditions apply for this scenario: • Time of release: August, early morning (0800 - local time) • Temperature: 13 °C • Wind speed: 6-10, average 8 m/s • Wind direction: from Northwest (averaged 322°) • Oil released: 200 m3 • Duration: 10 min • Oil type: naphthenic crude (similar to the oils in the area e.g. Heidrun)

This crude will have a relatively slow water uptake (70% water after 24 hours at sea) and will form w/o-emulsions with relatively low viscosity (approx. 5000 cP, at shear rate 10 s'1) after 24 hours at sea. Due to this, relatively low increase in viscosity, the oil slick is expected to be dispersible for 24 hours at 10 m/s wind. Expected drift of the surface oil is 1 km/hour and expected life time of the thick surface oil is estimated at 2-3 days. With the prevailing environmental conditions this oil slick is expected to reach Froan nature reserve within 24 hours.

Four different response alternatives are evaluated in this scenario:

Alternative 1 A: No response - leaving it all to “mother nature” Alternative IB: Use mechanical recovery equipment onboard standby vessel Alternative 1C: Use Spray arms and dispersant onboard standby vessel Alternative ID: Use the helicopter bucket and dispersant stored at 0rland

The effect of these four alternative response strategies were modelled with the OSCAR system and the mass balances for the first 1.5 days are shown in figure 9.2. The trajectory of the surface oil from alternative A (no response) and the main local nature resources are shown in figure 9.3.

Dr. thesis Per Johan Brandvik, March 1997 No response

Mechanical recovery by stand-by vessel

0.21 0.2 0 0.37 0.4 5 0.5 5 0.67 0 .70 0 Time (days) Dispersant applied from stand-by vessel

0.01 0.11 0.21 0.3 0 0.4 0 0.5 2 0.6 1 0.7 0 0.8 0 0.8 0 0.0 7 1.05 1.1 4 1.2 2 1.20 1.3 7 1.43 1 .5 1 Time (days) Dispersant application from helicopter D

0.0 1 0.11 0.2 1 0.30 0.39 0.48 0.58 0.88 0.7 7 0.87 0.9 7 1.0 7 1.1 5 1.23 1.3 1 1.38 1 .4 5 Time (days)

Figure 9.2 Mass balance of oil in scenario 1 “Medium offshore spill”, alternative A: no response, alternative B: mechanical recovery, alternative C: spraying dispersant from standby vessel and alternative D: spraying dispersant from helicopter bucket. 62

|^ Nature to selves {NJ E8 Oiled area I I Fish farms

Figure 9.3 The surface oil trajectory for alternative 1A “no response” showing the surface oil a few hours from reaching the Froan nature reserve.

No response Mechanical recovery Boat dispersant treatment Helicopter dispersant treatment

Time (days)

Figure 9.4 Surface exposure (km2-hours) caused by the thick parts (> 20 pm) of the surface slick for the four different response options (A-D). - 63 -

Discussion of scenario 1 and response alternatives Figure 9.2a shows that without any response this relatively limited offshore spill will reach the island in the Froan nature reserve after 22-24 hours. Approx. 30% of the oil will strand in the reserve within 1.5 day. 25% of the oil will evaporate and 47% will have naturally dispersed. Only minor amounts of the oil released will have sedimented (< 1 %). The high degree of natural dispersion of the oils produced in this area is caused by the low viscosity and stability of the surface emulsions formed. The total area affected by this relatively small amount of surface oil (55-60 m3) is large due to tidal currents and includes a high number of small islands and is estimated to be approx. 60- 100 km2, see figure 9.3.

Protection of this area from surface oil should be given high priority since some of the bird populations in the area are ranked in group A, (on a scale from A to D) i.e. have a large vulnerability from oil slicks. Up to 50% of the Norwegian population of the seal species Havert (Halichorus crypus), or approximate 2 000 individuals, are located in this area in August due to their combined “birth/mating ” period which starts in September. Also Alke {Alca torda L.) and Lomvi (Uria aalge) are gathering in these coastal areas in August. The adult birds cannot fly due to seasonal loss of feathers. They are very active on the sea surface in this period and have a high vulnerability towards surface oil slicks.

Due to the relatively short drifting distance and the natural resources present in the area, alternative A (doing nothing) is not regarded as an option in this scenario. The simulations of the different response options in figure 9.2 show that both mechanical recovery and dispersant treatment are capable of preventing the surface oil from reaching the Froan nature reserve. With mechanical recovery, all of the oil can be removed from the sea surface within 8-10 hours. By using dispersant from either boat or helicopter, the surface oil will be removed within 4-5 hours. This use of dispersants assume that the necessary permits are given by the authorities.

The oil concentrations in the water column caused by the increased rate of dispersion (peaking at 20-50 ppm) could locally cause toxic effects, but the dilution is expected to very rapid due to large depth (300 m) and the coastal current. Only minor increases of WSF in the water column are expected, due to evaporative loss of these components before dispersant treatment. A low density of fish eggs and larvae is also expected in this coastal area in August. However, the environmental damage caused by the use of dispersants will be less than the damage caused by alternative A (no response).

The success of all three response methods in preventing oil from stranding in the nature reserve, gives the responder a choice during the planning stage; what type of contingency is most cost effective? A combined strategy could also be possible and the OSCAR system can be used to estimate the effect of a combined response using both mechanical recovery and dispersants. A helicopter with an FLIR camera should be used to locate the thickest parts of the oil slick and direct the standby vessel to optimise the effectiveness of both mechanical recovery and use of dispersant by the vessel.

The sea surface exposed to the drifting oil slick during the different four response alternatives is illustrated in figure 9.4. This figure gives estimates for this exposure

Dr. thesis Per Johan Brandvik, March 1997 - 64 -

expressed in km2-day versus time. The km2-day unit is used to quantify the surface area which has been exposed to oil and is similar to the exposure unit (ppm-hour) which is used to quantify sub-surface exposure. This estimate of sea surface exposure (km2-day) together with the density of birds active at the sea surface can be used to quantify environmental damage to sea birds.

Use of dispersant is in this scenario reducing the sea surface exposure with 50% compared to mechanical recovery and with a factor of 4-5 compared to “doing nothing ”.

Dr. thesis Per Johan Brandvik, March 1997 -65-

9.3.2 Scenario 2: Coastal bunker spill

This coastal area has a large amount of ship traffic with the majority being medium sized coastal freighters, and few of them have crude or petroleum products as their main cargo. This scenario describes the grounding of the coastal vessel “MS Freighter ” due to machinery failure. The vessel grounded on a small island inside Melstein light house, 10-13 km from the shoreline (map in figure 9.6), releasing 200 m3 medium bunker fuel (IF-30).

In the period 1989-92, three different freight vessels grounded along the Norwegian coast (Mercantile Marcia, Sonata and Arisan) and caused bunker fuel spills similar to this scenario.

The following conditions apply for this scenario: Time of release: October, early morning (0800 - local time) Temperature: 8 °C Water depth: 100 m (outside island) Wind speed: 5-10 m/s (averaged: 7.5 m/s) # Wind direction: from North (approx. 355°) Oil released: 200 m3 # Duration: 1 hour # Oil type: Medium bunker fuel IF-30

Oil is leaking out of the vessel after the grounding and the IF-30 bunker fuel will rapidly emulsify and form a very viscous and stable emulsion. The rate of natural dispersion is very low for this viscous emulsion. This emulsion has also a reduced “window of opportunity ” for dispersant use, due to high viscosity (see section 8). However, the oil slick can effectively be dispersed within the first 4-6 hours after release in 5-10 m/s wind. Expected drift of the surface oil is approximate 1 km/hour. With the prevailing environmental conditions this oil slick may reach the first island at Valneset and Stordya with their special fauna and Vaeret bird habitat within 8-10 hours. A high density of vulnerable spieces in the bird habitat is expected, due to seasonal migration.

Three different response alternatives are evaluated in this scenario:

Alternative 2A: No response - leaving it all to “mother nature” Alternative 2B: Use mechanical recovery equipment at 0rland depot Alternative 2C: Use the helicopter bucket and dispersant at 0rland depot

The effect of these three alternative response strategies were modelled with the OSCAR system and the mass balances are shown in figure 9.5. The trajectory of the surface oil from alternative A (No response) and the main local nature resources are shown in figure 9.6.

Dr. thesis Per Johan Brandvik, March 1997 Figure Mass balance Mass balance Mass balance 100% 50 20 40 80 30 60

9.5 0

% % % % % % %

0.01

).01 -||||| J S ggg g# RgKgSSS 511

0.1 of no Mass 0.13 .13 l|

dispersant. 0.23 response, 0.23

0.23

____

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___

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0.42 ___

alternative 0.51

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oil 0.58

0.58

Mechanical 0.60 in

0.66

application scenario 0.66

No B: 0.72 0.70

Time Time Time - 0.72

mechanical

response

0.78 66

0.78

0.78 (days) (days) (days)

- 0.84 2

recovery

“

0.85 0.85

Coastal from 0.90

0.91

0.97 recovery 0.92

helicopter

0.99

1.04 bunker 1.05

1.1 1.1

and

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1.17 1.19 1.19

spill SSiranded □ ® ^Decayed ^Cleaned © E v a p o r a t e d

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Surface Dispersed 1.28 1.28 1.29 ",

1.35 alternative 1.37 1.40

1.45

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- 67 -

|y \ ^ biicd:0^'o:;7^:;; :;:;';:>;::::::Vlx:::;: y • Q Fi5h1faYms;:i:;:;:;:::ii:;:;:;:;:;:.:::;iA # 8Si yuincrtibtd hdAch jaiuiVd ^

Figure 9.6 The surface oil trajectory for alternative 2A “no response” were the oil reach the coastal area outside Tr0ndelag and impacting the natural resources.

Discussion of scenario 2 and response alternatives Without any response, this limited bunker spill will reach the first island in the bird habitats at Gjesingen (B5) within 10-12 hours. Approx. 35% of the oil will strand on these island and another 55% will strand after 12-14 hours in the bird habitat Vas ret (B4) and on the shorelines on the mainland. Low evaporative loss (8-9%) is expected before stranding and only minor amounts (1-2%) will naturally disperse due to the high viscosity and relatively low sea state.

Spreading of surface oil close to the shoreline is expected to be large due to tidal currents. The potential for environmental damage in the Vasret bird habitat (B4) is large since the density of birds is high, due to seasonal migration. This area is given a high priority for protection in case of an oil spill (grade B on a scale from A-D).

The mobilisation time for the mechanical recovery equipment at 0rland (4 hours) and the cruise time to the wreck site (22 nautical miles - 1.8 hours), and the time needed to deploy the booms and skimmer (1 hour) is too large for efficient recovery. When the mechanical recovery vessel arrives and the equipment is deployed, the oil is already stranding on the first islands (figure 9.5b and 9.6). There is also only 4-5 hours of daylight left, when the recovery vessel arrives at the spill site. Therefore only approx. 20% of the oil is recovered before it reach the beaches. -68 -

In this scenario, only application of dispersant by helicopter has the potential of preventing the oil slick from stranding in the bird habitats and on the vulnerable beaches (see figure 9.5c). The mobilisation time for the helicopter and dispersant spraying unit (1 hour) and the flying distance (16 nautical miles - 6 minutes) enables the helicopter to start dispersant application immediately after the last of the oil is released. The whole slick can be treated by 4 applications runs with the bucket (10-12 m3 of dispersants), within 3.5 hours after start of release. This scenario assume that the necessary pre approval to use dispersants in this type of oil spill is given by the authorities. The amount of oil stranded could be reduced from 85% to only traces of oil by using dispersants.

The water depth in the area where the oil slick is treated with dispersant is around 100 meters. The drifting distance for the dispersed oil with the dominant current towards land are 10-20 km. No fish spawning is expected in this area in October, but several fish farms (A) located Northeast of this area (figure 9.6), and might be affected by the dispersed oil.

The decision whether or not to use dispersant in this scenario should be based on a comparison of the possible economic damage to the fish farms (A) and the ecological damage to the bird habitats (B4, B5) and to the beach fauna (Al, A2). Guidelines for such considerations are worked out as a part of the National Contingency Plan for acute pollution in the Trpndelag region (Spr Trpndelag county, 1996). Pish farming installations are regarded as a replaceable resource with low priority for protection against oil pollution, priority D (on a range from A to D).

With a “coastal oil spill”, as described in this scenario, application of dispersant from helicopter is a very effective countermeasure technique, due to its short response time.

The oil spill in this scenario is located very close to the 0rland depot (12 km) and the drifting time before hitting the beaches is relatively long (10-12 hours) for this type of oil spills. The closest depots operated by the Norwegian Pollution Control Authorities is at Alesund (215 km south) and at Sandnessjpen (280 km north). Increasing the distance from the depot to the spill site, would favour use of dispersant even more. The helicopter bucket, could be used to treat an oil spill of this size (200 m3) within 10 working hours, and within a radius of 130-150 km from the helicopter and dispersant depot.

Dr. thesis Per Johan Brandvik, March 1997 -69 -

9.3.3 Scenario 3: Underwater blow-out

This scenario simulates an underwater blow-out from “Green Explorer ” during drilling. An unexpected event occurred during well testing, causing an uncontrolled release of crude oil from the sea bottom. However, well pressure is expected to fall and the oil release reduced to a minimum during 3 days, due to reservoir characteristics.

The following conditions apply for this scenario: # Time of release: August, early morning (0800 - local time) # Temperature: 13 °C # Water depth: 300 m # Wind speed: 10 m/s Wind direction: from Northwest (approx. 330°) Oil released: 3000 m3/day or 125 m3/hour Gas released: 210 000 SmVday, giving a Gas to Oil ratio (GOR) of 70 Duration: expected duration 3 days Oil type: naphthenic crude (similar to other oils in the area e.g. Heidrun)

The high shear forces between oil, gas and sea water at the release point create very small oil droplets. The small oil droplets formed and the entrainment of water in the gas/oil underwater plume gives a high dilution factor of the oil droplets. Due to the small oil droplets formed and the high dilution factor, only 50% of the oil released will reach the surface. Large amounts of entrained water also cause the oil to surface over a large area forming a thin surface oil slick (Rye et ah, 1996a). The surface slick is in this scenario assumed to have an initial width of 500 meters and a film thickness of 0.15 mm.

The crude oil is not expected to emulsify due to the low film thickness. Expected drift of the surface oil is approximate 0.5 km/hour, but the thin oil slick is expected to have a high degree of natural dispersion resulting in a relative short living time of the surface oil (6-12 hours). In contrast to the 200 m3 surface release in scenario 1, the remains of the surface oil in this scenario is not expected to reach Froan nature reserve.

Four different response alternatives are evaluated in this scenario:

Alternative 3A: No response - leaving it all to “mother nature” Alternative 3B: Use mechanical recovery equipment onboard standby vessel Alternative 3C: Use dispersant spraying equipment onboard standby vessel Alternative 3D: Use the helicopter bucket and dispersant stored at 0rland

The effect of these four alternative response strategies were modelled with the OSCAR program and the mass balances are shown in figure 9.7. The trajectory of the surface oil from alternative A (no response) and the main local nature resources are shown in figure 9.8.

Dr. thesis Per Johan Brandvik, March 1997 Figure

M ass b alan ce M ass b alan ce M ass balance M oss balance

9.7

no Mass dispersant spraying

response,

balance

Dispersant dispersant from

alternative

of helicopter

oil

from in

applied

scenario B: H

standby o Time

bucket. - mechanical response 70

from (days)

3 -

vessel “

Underwater stand-by

recovery

and

Alternative

vessel

blow-out

and

Alternative

D: ” ,

alternative spraying

-71 -

Figure 9.8 The surface oil trajectory for alternative 3A "no response" showing the surface Heidrun oil which naturally disperse before reaching Froan nature reserve.

0.7 --

Br 0.6

3 0.2 ■■ •No Response 'Mechanical recovery Dispersant applied by boat •Dispersant applied from helicopter

Time (days)

Figure 9.9 Surface exposure (km2-hours) caused by the thick parts (> 20 pin) of the surface slick for the four different response options (A-D). -72-

Discussion of scenario 3 and response alternatives Figure 9.7a shows that approx. 90% of the oil will naturally disperse during the first 6- 12 hours after being released and the rest will evaporate. However, when the underwater release has reached an equilibrium phase after approximate 12 hours, the surface oil will constantly cover a relatively large sea surface area during the blow-out period. The thin surface oil slick (> 20 pm) is expected to be approx. 1000-1200 m wide and 2-4000 m long and will cover a surface area of 2-4 km2.

Figure 9.10 shows the surface oil slick from an experimental underwater release of oil simulating a blow-out. The oil release rate was approx. 1500 m3/day with a Gas-to-oil ratio (GOR) of 70, releasing 100 800 Sm3/day. The release lasted for 45 minutes and approx. 20% of the released oil surfaced during the experiment (Rye et al., 1996b).

Figure 9.10 Surface oil slick from an experimental underwater oil and gas blow-out simulation (Rye et al, 1996b).

Mechanical recovery equipment has a very low effectiveness in this scenario due to the large width of the oil slick and the thin oil film (figure 9.8b). The boom opening (60 m) of the standby vessel is, 1000 m downwind from the release point, only covering about 5% of the width of the slick. Even after the large NOFO Ro-boom systems from Kristiansund were mobilised (approx. 180 m boom opening) and arrive after 17, 19 and 29 hours, the efficiency of the mechanical recovery would still be low (figure 9.8b).

Dr. thesis Per Johan Brandvik, March 1997 -73-

Approximately 1000 m3 or 27% of the released oil is recovered. However, if left on the surface most of this recovered oil would naturally disperse within 6-12 hours.

Dispersant application from the stand-by vessel is also assumed to have a low effectiveness, because this spraying equipment was designed to treat thick emulsions (mm-range) and not thin water-free oil slicks from an underwater blow-out (figure 9.8c). This results in overdosage of dispersant and herding, because the large dispersant droplets penetrate the thin surface oil and were lost to he underlying water. 50 m3 of dispersant is used and only 200-300 m3 of the oil is expected to be dispersed.

The Response 3000D helicopter bucket at 0rland has a low dosage system (2001/min) creating small dispersant droplets suitable for treating thin oil slicks (Brandvik et al., 1996). The helicopter uses both the dispersant storage at 0rland (50 m3) and onboard the supply vessel (50 m3). The helicopter sprays approximately every second hour and performs 8 missions each day, a total of 24 missions from 0rland. 70-80 m3 of dispersant is used during these three days and 800-900 m3 of oil is expected to be dispersed.

The resources used for the mechanical recovery operation are large. Each of the three NOFO units consists of one large offshore supply vessel and a smaller trawler. The amount of bunker fuel consumed by the six NOFO vessels participating in this three day blow-out scenario is estimated to 180 - 200 m3 (assuming; full speed to reach the area, then a third of the time at cruising speed - 12 kn, and two thirds at operating speed - 2 kn). In addition, 800 meters of booms and an large quantity of pumps, hoses and oil storage tanks need cleaning after this response operation. Each NOFO unit consists approximately of 15 seamen and 5 equipment operators and 2500-3000 man-hours are used during this operation (three days with oil recovery and one day with emptying tanks, loading equipment and in transit afterwards).

The helicopter is estimated to use 35-40 flying hours and consume 80-90 m3 of Al jet- fuel during this three-day operation and no significant amount of equipment need special cleaning. A double crew (2x5 persons) giving 400-450 man-hours (12 hours shift for four days) were used. A backup helicopter was also mobilised for this operation.

As mentioned earlier, the natural dispersion rate of the surface oil is high and the majority of the oil is estimated to disperse during the first 6-12 hours after release. However, by treating the oil with dispersant from the helicopter, the sea surface area exposed by oil could be reduced by approx. 20% with dispersant treatment from boat and 50% by helicopter treatment (figure 9.9). This could reduce the damages to birds gathering outside Froan Nature reserve at this time of year. Surface exposure in this scenario is similar to scenario 1 (figures 9.4 and 9.9), despite the large dimensions of the surface slick in this scenario, due to the short life-time of this thin surface oil slick (only 6-12 hours).

Dr. thesis Per Johan Brandvik, March 1997 -74-

9.4 Conclusions - oil spill scenarios

The three scenarios presented here are only a small fraction of oil spill scenarios possible in Norwegian waters. Some will argue that my selection favours the use of dispersants. Several other scenarios, where use of dispersant would not be favourable, could have been presented.

Examples of such scenarios are; • large surface blow-out (9000 nrVday); Dispersant can only be used as a supplementary method, since sufficient treatment capacity is not available. ® grounding of a large crude carrier (200 000 m3); Dispersant can only be used as a supplementary method, since sufficient treatment capacity is not available. • heavy bunker fuel or heavelv weathered oil spill : Dispersant should not be used due to expected low effectiveness, caused by very high w/o-emulsion viscosity. • Oil spill in shallow waters during spawning season . Dispersants should not be used, due to high risk of toxic effects on fish eggs and larvae.

However, the objective with this case study was to illustrate the potential of using dispersant in some realistic oil spill scenarios. A more general discussion of the advantages and disadvantages of using dispersant are briefly given in chapter 8.

These three scenarios have shown that the use of dispersant can be a rapid and efficient response option for reducing the environmental damage from oil spills at sea. In some scenarios, which require short response time, the use of dispersants is the only available option to avoid large beach cleaning operations. In other scenarios, the use of dispersants is a cost effective alternative to mechanical recovery operations.

Dr. thesis Per Johan Brandvik, March 1997 -75-

10. Conclusions and recommendations

The most important conclusions and recommendations from the individual papers are summarised in this sections together with the main conclusions from the scenarios presented in section 9.

10.1 Characterisation of oils for environmental purposes

Understanding the weathering behaviour of different oil types spilled at sea is essential for planning and performing an effective oil spill response. The approach described in paper 1 combines laboratory analysis of the oil with numerical modelling to predict weathering of oil spills at sea under different environmental conditions.

This method has later been established as a standardised method in Norway for characterisation and predicting oil weathering.

10.2 Need for development of new dispersants

This and other studies have shown that effectiveness varies between different dispersants and for the same dispersant used on different oil types.

The effectiveness of the best currently available dispersants is high and one dispersant can be used with an acceptable effectiveness on a broad selection of oil types. However, it is recommended that new dispersants are developed for special oil types; • highly weathered oil spills • heavy bunker fuels • crude oils with a high wax content

This year, oil exploration starts again in the Southern part of the Barents Sea and an opening of the Northern areas is also being evaluated. Use of dispersants in these areas with lower temperatures and varying water salinity due to ice melting, would also require improved dispersant formulations.

Use of designed experiments and multivariate analysis is a very powerful and cost/effective approach for dispersant development.

10.3 Operational use of oil spill dispersants

The largest advantage of using dispersant as an oil spill response method is the short response time. In many scenarios, when oil is spilled it has a short drifting distance to the shoreline and dispersant applied from a helicopter is the only response option available to prevent surface oil reaching vulnerable nature resources.

A new dispersant based contingency in Norway must utilise our large national helicopter resource; the governmental Search and Rescue helicopters (Stavanger, 0rland, Bod0,

Dr. thesis Per Johan Brandvik, March 1997 -76-

Lakselv, Alesund and Kristiansand) and the helicopters used offshore by the oil companies operated from Stavanger, Bergen, Kristiansund, Flor0, Br0nn0ysund and Hammerfest.

Governmental oil spill contingency Dispersants applied from helicopter should be included in the Norwegian governmental oil spill contingency operated by the Norwegian Pollution Control Authorities (SFT). The necessary equipment (helicopter buckets and dispersants) could be stored at some of SFTs depots along the coast or where suitable helicopter resources are located.

Private Oil spill contingency Offshore exploration and production installations are now being operated closer to the coastline than previously. Use of dispersants should be extended to include offshore oil spills of small to medium size (10-1000 m3). This assumes the existence of suitable application equipment (“state-of-the-art ” boat and/or helicopter spraying equipment) and sufficient amounts of dispersants (10-50 m3) close to the spill site.

Dispersants sprayed with specialised equipment are also an effective response option to reduce the risk of environmental damage from an underwater blow-out which might create a very thin surface oil film covering large areas.

However, a close co-operation between the authorities and private enterprises concerning equipment is necessary in building up a future (cost effective) dispersant based oil spill contingency in Norway.

Dr. thesis Per Johan Brandvik, March 1997 -77-

11. References

Bobra, A.M., Albernethy, S., Wells, P.G. and Mackay D., 1984. Recent toxicity studies at the University of Toronto. In: Proc. 7th Arctic Marine Oilspill Program Technical Seminar. Edmonton, Alberta. Environment Canada, pp. 82-90. Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for experimenters, An introduction to design, data analysis and model building, John Wiley & Sons, New York. Brandvik, P.J. and Baling, P.S., 1990. Statistical experimental design in the optimisation of dispersant’s performance. In: Proc. 13th Arctic Marine Oilspill Technical seminar, Environment Canada, Edmonton, 1990. pp. 243-254. Brandvik, P.J. and Baling, P S, 1991, Weathering properties of the Veslefrikk crude at sea - an handbook for Statoil, IKU report no. 22.1984.00/01/91. Brandvik, P.J., Baling, P.S., Lewis, A., and Lunel, T., 1995. Measurements of Bispersed Oil Concentrations by In-Situ UV Fluorescence Buring the Norwegian Experimental Oil Spill 1994 with Sture Blend. In: Proc. 18th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 519-535. Brandvik, P.J., Strpm-Kristiansen, T., Lewis, A., Baling, P.S., Reed, M., Rye H., and Jensen, H., 1996a. The Norwegian Sea Trial 1995: Offshore Testing of Two Bispersant Application Systems and Simulation of an Underwater Pipeline Leakage - A Summary Paper. In: Proc. 19th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 1395-1405. Brandvik, P.J., Lewis, A., Strpm-Kristiansen, T„ Hokstad, J.N., Baling, P.S., 1996b. NOFO 1996 Oil on water exercise - Operational testing of “Response 3000" Helibucket. IKU report no. 41.5164.00/01/96. Brandvik, P.J., Lewis, A., Baling, P.S., Tpmmervik, T., 1996c. Bevelopment of a new helicopter bucket for dispersant application “Response 3000” - IKU’s contribution to design, land and field testing. IKU report no. 41.5142.00/01/96. Brandvik, P.J., Strpm-Kristiansen, T., and Baling, P.S., 1996d. Weathering properties of the Frpy crude, Lillefrigg condensate and the 75:25 blend of these two products - a handbook for Elf Norge. IKU report no. 41.5135/01/96. Bprresen, J.A., 1993. Oil at Sea, Ad Notam Gyldendal AS, Oslo Norway, 308 p. (In Norwegian). Canevari, G.P., 1969. The role of chemical dispersants in oil cleanup. In: Oil on the Sea, Hoult, B.P. Ed., New York, Plenum Press, pp. 29-51. Cornell, J.A., 1990, Experiments with mixtures, Wiley, New York, 2nd Ed., 1990.

Dr. thesis Per Johan Brandvik, March 1997 -78-

Cox, D.R., 1971. A note on polynomial response functions for mixtures, Biometrica, 58(1), 155-159. Baling, P.S., 1983. Transport of water soluble components from oil to water. Ms. thesis at Department of Chemistry, University of Trondheim, Norway. (In Norwegian). Baling, P.S., and Brandvik, P.J., 1991. Characterisation and prediction of the weathering properties of oils at sea - a manual for the oils investigated in the DIWO project, IKU report no: 02.0786.00/16/91. 140 p. Baling, P.S., and Johnsen, S., 1996. Characterisation of water soluble fractions from the Troll crude., IKU report no. 41.5172.00/01/96. 13 p. (In Norwegian). EPA, (US - Environmental Protection Agency), 1982. Manual of practice Chemical Agents in oil spill control. EPA Report no. 600/8-82-0/0, Washington, DC. Esbensen, K., Midtgaard, T., and Schonkopf, S., 1994. Multivariate Analysis in Practice, Camo AS, Trondheim. Norway. ESGOSS, (Ecological Steering Group on the Oil Spill in Scotland), 1994. The Environmental Impact of the Wreck of the Braer. Scottish Office, Edinburgh, UK. Fiocco, R.J., Lessard, R.R., Canevari, G.P., Becker, K.W., and Baling, P.S., 1995. The Impact of Oil Dispersant Solvent on Performance. In: The use of Chemicals in Oil Spill Response, ASTM STP 1252, Peter Lane, Ed., American Society for Testing and Materials, Philadelphia, USA. Hoskuldsson, A., 1988. PLS regression methods, Jour. Chemometrics., 2, pp. 211-228. Johansen, 0., 1984. The Haltenbank Experiment. In: Proc. 7th Arcic Marine Oilspill Program Technical Seminar. Edmonton, Alberta. Environmental protection Service, pp. 17-36. Khuri. A.L., Cornell, J.A., 1987. Response surfaces, Design and analyses, Marcel Dekker Inc., ASQC Quality Press, New York. Kvalheim, O.M., 1988. Interpretation of direct latent-variable projection methods and their aims and use in the analysis of multicomponent spectroscopic and chromatographic data. Chemometrics and Intelligent Laboratory Systems 4, pp. 11-25. Lewis, A., Baling, P.S., Str0m-Kristiansen, T., and Brandvik, P.J., 1995a. The Behaviour of Sture Blend Crude Oil Spilled at Sea and Treated with Dispersant. In: Proc. 18th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 453-469. Lewis, A., Baling, P.S., Strpm-Kristiansen, T., Nordvik, A.B., and Fiocco, R.J., 1995b. Weathering and Chemical Dispersion of Oil at Sea. In: Proc. 1995 International Oil Spill Conference, API, Washington, DC. pp. 157-164.

Dr. thesis Per Johan Brandvik, March 1997 -79 -

Lewis, A., 1995. NOFO exercise 1995c; Dispersant and underwater release experiments - Remote sensing imaginary from aircraft and satellites - Data report. IKU report no. 41.5151/03/95. Lewis, A., and Aurand, D., 1997. Putting Dispersants to Work: Overcoming Obstacles. In: Proc. 1997 International Oil Spill Conference, API, Washington, USA. (In press). Licthentaler, R.G., and Daling, P.S., 1983. Dispersion of chemically treated crude oil in Norwegian offshore waters. In: Proc. 1983 Oil Spill Conference. Washington, D C., American Petroleum Institute, pp. 7-14. Lichtenthaler, R.G., and Daling, P.S., 1985. Aerial Application of Dispersants - Comparison of Slick Behaviour of Chemically Treated Versus Non- Treated Slicks. In: Proc. 1985 International Oil Spill Conference, API, Washington, DC., pp. 471-482. Lunel, T., 1995a. Understanding the mechanism of Dispersion trough Oil Droplet Size Measurements at Sea. In: The use of Chemicals in Oil Spill Response, ASTM STP 1252, Peter Lane, Ed., American Society for Testing and Materials, Philadelphia, USA. Lunel, T., 1995b. Dispersant Effectiveness at Sea. In: Proc. 1995 International Oil Spill Conference, API, Washington D C. pp. 147-155. Lunel T. 1996. Dispersion Measurements at the Sea Empress Oil Spill. In: Proc. 19th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 100-110. Mackay, D., and Wells, P.G., 1983. Effectiveness, behaviour, and toxicity of (oil spill) dispersants. In: Proc. 1983 Oil Spill Conference. Washington, D.C., American Petroleum Institute, pp. 65-71. Mackay, D., Stiver, W., and Tebeau, P.A, 1983. Testing of crude oils and petroleum products for environmental purposes. In: Proc. 1983 Oil Spill Conference. Washington, D C., American Petroleum Institute, pp. 331-337. MAFF (UK - Ministry of Agriculture, Fisheries and Food), 1995. Testing, Approval and Use of Oil Dispersants - Final Report of the Government Review. MAFF, London. UK. Martens, H., and Ntes, T., 1989. Multivariate Calibration, John Wiley and Sons, Chichester. McAuliffe, C.D., 1987. Organism exposure to volatile/soluble hydrocarbons from crude oil spills - a field and laboratory comparison. In: Proc. 1987 Oil Spill Conferance. Washington, D.C., American Petroleum Institute, pp. 555-566. McAuliffe, C D., 1989. The Use of Chemical Dispersants to Control Oil Spills in Shallow Nearshore Waters. In: Oil Dispersants: New Ecological Approaches. Flaherty, L.M. (ed.). ASTM STP 1018. pp. 49-72.

Dr. thesis Per Johan Brandvik, March 1997 -80 -

Neff, J.M., and Stubblefield, W.A., 1995. Chemical and Toxicological Evaluation of Water Quality following the Exxon Valdez Oil Spill. In: Exxon Valdez Oil Spill: Fate And Effects In Alaskan Waters. Wells, P.G., Butler, J.N., and Hughes J.S. (Editors). ASTM STP 1219. pp. 141-177. Nortvedt, R., Brakstad, F., Kvalheim, O.M., and Lundstedt T., (Editors) 1996. Applied Chemometrics within Research and Development. Infometrics Publisher, Bergen, Norway (In Norwegian/Swedish). NRC (US - National Research Council), 1989. Using Oil Spill Dispersants on the Sea. National Academy Press, Washington D C. 335p. Reed, M., Aamo, O.M., and Daling, P.S., 1995. OSCAR, a Model System for Quantitative Analysis of alternative Oil Spill response Strategies. In: Proc. 18th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 815-834. Rice, S.D., 1985. Effects of Oil on Fish. In: Petroleum effects in the arctic environment. F.R. Engelhard! (Editor), pp. 157-182, Elsevier. 281 p. Rye, H., Brandvik, P.J., and Reed, M. 1996a. Subsurface Oil Release Field Experiment - Observations and Modelling of subsurface plume behaviour. In: Proc. 19th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 1395-1416. Rye, H., Brandvik, P.J., Strpm-Kristiansen, T., Lewis, A., Hokstad, J.N., and Daling, P.S. 1996b. NOFO 1996 Oil on water exercise - Simulating blow out, releasing oil and gas at 106 m depth. IKU report no. 41.5164.00/02/96. Draft version. Serigstad, B., 1991. Effects on fisheggs and larveas of Gullfaks and Veslefrikk crudes. Havforskningsinstetuttet, report no: 15/1991/HSM; ISBN 82-7461- 031-8. 52 pages (In Norwegian). Singer, M.M., Smalheer, D.L., Tjeerdema, R.S., and Martin, M., 1991. Effects of Spiked Exposure to an Oil Dispersant on the Early Life Stages of Four Marine Species. Environmental Toxicilogy and Chemistry 10. pp. 1367-1374. Scheffe, H., 1958. Experiments with mixtures. J. R. Stat Soc., B, 20, No. 2, 344-360. Scheffe, H., 1963. Simplex-centroid design for experiments with mixtures. J. R.Stat. Soc., B, 25, No. 2, 235-263. Strpm-kristiansen T, Knudsen, 0.0. Singsaas, I. and Daling, P.S., 1995. The weathering properties at sea for Sture Blend, Oseberg field center and Oseberg C crudes - a weathering manual for Norsk Hydro (2nd ed.), IKU report no: 95.084, (In norwegian). Strpm-Kristiansen, T., Daling P.S., and Brandvik P.J., 1996. NOFO 1996 Oil on water exercise - Surface oil sampling and analysis. IKU report no. 41.5164.00/03/96.

Dr. thesis Per Johan Brandvik, March 1997 -81 -

S0rstr0m, S.E., 1989. Full-scale experimental oil spill at Haltenbanken, Norway. Oceanographic Company of Norway (OCEANOR). Report OCN89054. S0r Tr0ndelag County, 1996. Contingency plan for accute pollution in the S0r Tr0ndeiag region, Fylkesmannen i S0r Tr0ndelag, Statens Hus, Trondheim (In Norwegian). Walker, M.I., and Lewis, A., 1995. Emulsification processes at sea - Forties crude oil. In: Proc. 18th Arctic Marine Oilspill Technical Seminar, Environment Canada, Ottawa, Ontario, pp. 471-493. Wold, S., 1978. Cross-validatory estimation of the number of components in factor and principal components models, Technometrics, 20 (4). pp. 397-405 Wold, S., Esbensen, K., and Geladi, P., 1987. Principal component analysis - A tutorial, Chemom. Inteil. Lab. Syst., 2, 37-52

Dr. thesis Per Johan Brandvik, March 1997 -83 -

AppendixA: Reprints of papers

The following 5 papers are used a basis of this thesis. A summary of the papers are given in chapter 3.

Paper 1: Characterisation of crude oils for environmental purposes, Baling P.S. and Brandvik P.J., Mackay, D., Johansen, 0., 1991: Oil & Chemical Pollution 7, 1990/91, pp. 199-224.

Paper 2: Laboratory testing of dispersants under arctic conditions, Brandvik, P.J., Knudsen O.0., Moldestad, M.0. and Baling P.S., in The use of Chemicals in Oil Spill Response . ASTM STP 1252, Peter Lane Ed., American Society for testing and Materials, Philadelphia, 1995.

Paper 4: Optimisation of oil spill dispersant composition by mixture design and response surface methods, Brandvik, P.J. and Baling P.S., Chemometrics and Intelligent Laboratory Systems, submitted for review and publication, November 1996.

Paper 5: Optimising oil spill dispersants as a function of oil type and weathering degree - a multivariate approach using partial least squares (PLS), Brandvik, P.J. and Baling P.S., Chemometrics and Intelligent Laboratory Systems, submitted for review and publication, November 1996.

Two of these papers are already published. Paper 1 is published in an international journal and paper 2 is published in an ASTM publication, both papers have been evaluated of peer reviewers.

The three last papers are submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996. Reviewers comments have been received for these three papers and are worked into the papers.

Dr. thesis Per Johan Brandvik, March 1997 (paper 1)

Characterisation of crude oils for environmental purposes

Oil & Chemical Pollution 7, 1990/91, pp. 199-224

Per S. Baling and Per Johan Brandvik IKU Petroleum Research, SINTEF Trondheim, Norway

Donald Mackay, University of Toronto, Ontario, Canada

Oistein Johansen Oceonographic Company of Norway (Oceanor), Trondheim, Norway

Dr. thesis Per Johan Brandvik, March 1997 Oil & Chemical Pollution 7 (1990) 199-224

Characterization of Crude Oils for Environmental Purposes

Per S. Dating, Per Johan Brandvik

Continental Shelf and Petroleum Technology Research Institute (IKU), Sintef-Group, N-7034 Trondheim. Norway

Donald Mackay

Institute for Environmental Studies. University of Toronto, Toronto. Ontario MSS 1A4. Canada

& 0istein Johansen

Oceanographic Company of Norway A/S (Oceanor). PO Box 2514. N-7005 Trondheim. Norway (Received 5 July 1990: accepted 30 November 1990)

ABSTRACT

A new approach for predicting the behaviour of oil spilled on the sea has recently been developed at IKU, Sintef-Group. The approach includes an extensive laboratory investigation of an oil’s properties when exposed to weathering. Parameters especially tested are the tendency of the oil to form water-in-oil (w/o) emulsion (mousse), and the susceptibility of the w/o- emulsion or water-free weathered oil to disperse using oil spill dispersants. The laboratory results are transformed to field conditions in a numerical model which predicts the rate of weathering processes at sea under different weather conditions. The computer system displays graphical chartsfor the development of each property with time, and estimates the ‘time window' e.g. for effective application of dispersants under a chosen set of sea conditions. The system may represent an important tool, for contingency planning andfor'on-scene’ commanders to facilitate decision-making concerning the use of different countermeasure techniques during oil spill combat operations. This approach 199 Oil & Chemical Pollution 0269-8579/9 1/S03.50 © 1991 Elsevier Science Publishers Ltd, England. Printed in Ireland. 200 Per S. Doling. Per Johan Brandvik. Donald Mackay. Oistein Johansen

may form the basis for a standard method for future characterization of the weathering properties of different oil types which may be spilled under a variety of environmental conditions.

1 BACKGROUND

As long as crude oils and petroleum products are transported across the seas by ships or pipelines there will be the risk of a spillage with the potential to cause significant environmental damage. The Exxon Valdez incident in Alaska, and also recent smaller ship accidents, e.g. in Norway, have again demonstrated the high level of public concern about the damaging effect of oil spills. These spills also demonstrated the need for a rapid decision-making process for assessing the feasibility and effectiveness of different countermeasures (mechanical recovery equip ments, burning, dispersants, etc.) during the oil spill combat operation. When oil is discharged into sea water, it is subject to several processes including spreading, drifting, evaporation, dissolution, photolysis, biodegradation and formation of both oil-in-water and water-in-oil emulsions. Figure 1 gives a pictorial representation of the dominant oil spill processes. In principle, if the oil is well characterized and the environmental conditions of wind speed, sea state, currents, salinity, temperature and solar insolation are known, it should be possible to calculate the rates of many of these processes and thus establish how the condition of the oil changes with time. The physical and chemical properties of crude oils play an important role in determining environmental fate and the effects of marine spills (National Academy of Sciences, 1985). Countermeasures can only be implemented effectively if the oils ’ condition is fully understood. Oils should therefore be subjected to analytical programmes of property determinations which will help enable the fate of the oil to be predicted, and the feasibility of various countermeasures to be better determined. Of particular interest are the extents to which the weathering processes of evaporation, dissolution and photo-oxidation alter the oil ’s properties and affect the tendency of the oil to form water-in-oil (w/o) emulsions (mousses), and to disperse naturally (oil-in-water emulsions). Similar processes will also change the susceptibility of the oil to effective dispersion by chemical agents, i.e. chemical dispersants, and the effectiveness of burning the weathered oil. The tendency for crude oil slicks to form w/o-emulsions on the sea surface results in substantial changes in behaviour and necessitates changes in countermeasure approaches. It is, therefore, desirable to Characterization

Evaporation Wind

of Watcr-in-oil Photolysis

Drifting crude emulsion Spreading

oils

Resurfacing of larger oil droplets for Dissolution of water soluble components

environmental

Adsorption to particles Microbiological Uptake by biota Vertical diffusion degradation

Horisontal diffusion

Sedimentation purposes

Fig. 1. Oil spill processes. 202 Per S. Doling. Per Johan Brandvik, Donald Mackay. Oistein Johansen understand, as fully as possible, the factors which cause an oil to emulsify, the rate at which the w/o-emulsification occurs and the w/o- emulsion viscosity and stability for a given oil under given environmental conditions. The chemical composition of the oil and the influence of weathering processes like evaporation and photolysis are especially important for the properties of the w/o-emulsions formed (e.g. Mackay, 1987). The effectiveness of chemical treatment on different w/o-emulsion qualities has also been poorly investigated. Demulsifiers may be used to ‘break’ w/o-emulsions (to separate the water from the oil) either during in-situ treatment on the sea surface or on mechanically recovered w/o- emulsions. W/o-emulsion inhibitors may be added in small amounts to fresh oil to inhibit or retard w/o-emulsification, thereby enhancing the dispersion of the oil by natural sea turbulence. Recent studies (e.g. Ross, 1986; Lichtenthaler & Dating, 1985) have shown that certain oil spill dispersants also may act both as w/o-emulsion breakers and as w/o- emulsion inhibitors. The use of dispersants as a means of treating oil is being viewed with increasing favour as a result of the manufacture of more effective and less toxic dispersants, and better application techniques. It is now widely accepted that chemical dispersants have an important role in oil spill countermeasures (e.g. NRC, 1989), especially in near-shoreline situations where most of the damage will result from the presence of a surface slick which may foul birds and/or shorelines. Although much progress has been made the past 10 years, doubt still remains concerning the actual effectiveness of dispersants at sea under different oil spill situations. From an operational viewpoint it is essential to identify the types of crude oils which will show the best response to dispersant use. Furthermore, it is still poorly understood how changes in physico-chemical composition due to weathering of an oil at sea (e.g. evaporation, w/o-emulsion formation, photolysis) affect the performance of dispersants. The weathering issue has been largely ignored and it is appropriate to devote more attention to it, as is intended in this paper. As part of the four year project called ‘Application of Dispersants on Weathered Oils’ (DIWO) undertaken by IKU, Sintef-Group and funded by Fina Exploration Norway, available methods for oil characterization have been evaluated and a set of analytical procedures has been developed and applied to a number of crude oils and refined oil products. In this paper, we describe these methods, present illustrative laboratory results and discuss briefly the utilization of these experimental data in numerical weathering models. It is hoped that these methods will be considered by other workers, thus improving consistency between Characterization of crude oils for environmental purposes 203 measurements made by different groups and enabling more reliable prediction of an oil ’s environmental fate from knowledge of its characteristics.

2 LABORATORY PROCEDURES

The laboratory methodology for investigating the properties of each oil type consists of four parts. First is the preparation of 12 different weathered oil samples from the fresh oil as a result of exposure to a combination of evaporation, w/o-emulsification and photolysis. Secondly, the determination of the physical and chemical properties of the fresh and weathered oil samples. Thirdly, an assessment of the susceptibility of the weathered (water-free) oil samples to natural formation of w/o- emulsions (‘chocolate mousses ’). Finally, the susceptibility to chemical dispersion of a total of 13 oil samples (12 weathered and 1 fresh) from each crude is extensively tested using different laboratory dispersant effectiveness test methods. The laboratory methodology is briefly presented below. Full details of the laboratory methods have been described by Baling and Almas (1988).

2.1 Preparation of weathered oil samples

To isolate the influence of the different weathering processes (i.e. evaporative loss, photolysis and w/o-emulsification), the weathering of the fresh crude was carried out using a systematic, step-wise procedure. Figure 2 shows a schematic diagram of the weathering procedure. The first step involved three different degrees of evaporative loss by a modified ASTM D86/82 distillation (Stiver & Mackay, 1984): the crude was topped at 150°C, 200°C and 250°C (vapour temperature) which approximately (depending on weather conditions) simulates 0-5-1 h, 0-5-1 d and 2-5 d evaporative loss at sea, respectively. Secondly, the fresh crude was photolysed (artificial sunlight, see Fig. 3) for 20 h. This caused an evaporative loss of the lightest components similar to the 250°C+ topped oil samples. Finally, the topped and the photolysed (water-free) oil samples were emulsified with 50% and 75% sea water by using the rotating flask apparatus (Mackay & Zagorski, 1982; see section 2.3). Thus, 12 weathered oil samples were prepared from the fresh crude and each sample was subjected to physico-chemical analysis, w/o-emulsification studies and chemical dispersibility tests. 204 Per S. Doling, Per Johan Brandvik, Donald Mackay, 0istein Johansen

Fresh oil

'Simulated weathering- i 0.5 - 1 hour 0.5 - 1 day 2-5 days Evaporative loss: weathered oil weathered oil weathered oil

1. Topping (ASTM D 86/82) 150°C+ 200°C+ 250°C+ 2. Photolysis - - 20 hours

3. W/O-emulsification (50 and 75% water) of: 150°C+,200°C+,250°C+ and 20 hours photolyses fractions.

Fig. 2. Schematic diagram of the oil weathering processes. (A total of 12 different weathered samples from each crude.)

Reflector

Filter glass

Oil film Sea water p.— Cooling

Experimental conditions:

Lamp: Xenon high pressure (Osram - type XBF.6000W)

Filter: IR - Reflection and UV - absorption below 300 nm - (sunlight condition)

Type: 60cm x 80cm, with water cooling system

Oil film: 1 mm

Exposure time: 20 hours (—250°C+) Fig. 3. Schematic diagram of photolysis of the oil Film. Characterization of crude oils for environmental purposes 205

2.2 Physico-chemical analysis

The physico-chemical properties of the fresh, topped and photolysed oil samples were characterized by the analytical methods listed below: • Specific gravity ASTM-method D4052-81 • Viscosity (dynamic) Haake Rotovisco RV3 • Pour point ASTM-method D97-66 (IP-method 15/67) • Flash point ASTM-method D93-80 (IP-34/85) • Interfacial tension (a) Pendant drop tensiometer (b) de Nouy ring method (ASTM-method 971-82) • ‘Soft" asphaltene content iz-Pentane insolubles (Baling & Almas. 1988) • ‘Hard" asphaltene content IP-method 143/84 • Wax content Precipitation of deasphalted oil in 2-butanone/dichloromethane (1/1 v/v) at -10°C (Bridie et al., 1980) • Saturate, aromatic and resin content Iatroscan TLC/FID Examples of the changes in the physico-chemical properties of crudes with increased weathering are given in Tables 1 and 2.

2.3 Formation and properties of water-in-oil emulsions

The method used for studying w/o-emulsion properties of a crude was based on the rotating flask apparatus of Mackay and Zagorski (1982). The method involves rotation of centrifuge bottles containing sea water and oil (ratio 10:1) for a 1-h or 24-h period of time at a rotation speed of 65 rpm. After the mixing period, both the formation tendency and the stability of any w/o-emulsions formed were assessed (see Fig. 4(a)). Using this rotating flask method, it is possible to study the following: • relative rate of w/o-emulsion formation (f,/2-value) • maximum water content in a w/o-emulsion (%, Em,) • stability of w/o-emulsions (R2/r value, untreated or treated with chemicals, e.g. demulsifiers) • inhibition effectiveness of chemicals (e.g. dispersants or emulsion inhibitors) on w/o-emulsion formation. TABLE 1 Chemical Properties of Fresh and Weathered Crude Oils Used in the Experiments

Oil type Saturatesf Aromatics a Resins0 Asphaltenes (wt%f Waxes? Per (wt%) (wt%)

‘hard’ 'soft' Doling.

Gullfaks

— Fresh — — 0 02 0 49 160 Per

— 150°C+ —- ■-- 0 02 0 52 172 Johan 200°C+ — — — 003 0 58 1 90 250°C+ 52 1 40 0 68 0 03 0 65 2 14

20 h ph.ox (250°C+)'/ 45 3 37 8 153 0 24 1-11 — Brandvik. Statfjord Fresh — — — 0 01 0 39 4-14

150°C+ — — — 001 0 47 4-78 onald D 200°C+ — — -- - 0 02 0 54 5-77 250°C+ 56 2 34-8 7-8 002 0-63 668

20 h ph.ox (250°C+)'/ 59 5 25 6 13 7 0 29 1-14 — ackav, M Arabian heavy

Fresh — — — 4 33 7 38 4-62 Oistein 150°C+ — — — 4 89 834 5-22 200° C+ — — — 5-31 9-05 567

250°C+ 23 2 490 18 4 5 80 989 6-19 Johansen 20 h ph.ox (250°C+f 23 6 414 23 6 7-21 10-42 —

"Quantification by latrosean TLC/FID. AAsphaltene content is calculated from data of the 250°C+ fraction, 'hard' asphaltenes: IP-143 method, 'soft' asphaltenes: n-pentane insoluble. rWax content is calculated from data of the maithene fraction of 250°C+ (after the 'hard' asphaltene separation). ''Means that the oil has been photolysed for 20 h (see Section 2.1). TABLE 2 Physical Properties of Fresh and Weathered Crude Oils Used in the Experiments

Oil type Evaporative Density Pour-point Interfacial Viscosity of oil and w/o-emulsions° loss (g/ml) ro tension (cPat 13°C) (Vol%) wo (mN/m) 0% Water 50% Water 75% Water Characterization

Gullfaks Fresh — 0-882 <-30 13 20 — — 150°C+ 8 0-893 -30 13 33 440 1780

200° C+ 18 0-905 -9 15 72 800 4980 f o

250°C+ 28 0-914 0 17 241 1780 6760 crude 20 h ph.ox (250°C+y 28 0-916 -9 1-1 282 2300 14000

Statfjord oils

Fresh — 0-834 0 23 7 — — r fo 150°C+ 20 0-867 21 16 20 160 1200

200° C+ 32 0 882 24 15 57 490 3560 ental environm 250°C+ 42 0-895 27 16 221 1800 4600 20 h ph.ox (250°C+X 42 0-896 27 10 314 2850 18500 Arabian heavy

Fresh 0-887 -28 20 41 purposes 150°C+ 15 0-920 -23 14 241 4800 —h 200° C+ 23 0-935 —18 18 700 6000 _h 250°C+ 30 0-951 -5 17 2344 7350 _h 20 h ph.ox (250°C+y 30 0-954 -8 1-1 4350 11500 —h

“Measured at a shear rate = 10 s-'. ^Emulsion not possible to prepare. rMeans that the oil has been photolysed for 20 h (see Section 2.1). 8 -~j 208 Per S. Doling, Per Johan Brandvik, Donald Mackay, Oistein Johansen

Figure 5 shows an example of the experimental curves for the water uptake of three different crudes (200°C+ topped samples). Both the relative water uptake rate and maximum water uptake ability seem to depend strongly on the physical and chemical properties of the crudes. To express the rate for the water uptake, a theoretical exponential curve is fitted to each experimental curve. A r1/2-parameter can then be calculated, which is the mixing time elapsed when the water content is half of the maximum water uptake ability. Figure 6 shows the close correlation which appears to exist between the viscosity of an oil (water- free) and the ability of the same oil to take up water. By measuring the water-to-oil ratio of a w/o-emulsion before {R\- value) and after a 24 h settling time (i?2-value), we can calculate the Rm- value (defined as the Rvalue divided by the i?)-value). The /?2/1-value (between 0 and 1) is used as a parameter for the relative stability of the Fig. w/o-emulsion shows has Fig. while

Vol.% water in emulsion o

6. 40- 5. been

Maximum

Formation R that 100 40 80 20 30 50 60 90 70 2 10 crudes /

settled

= the

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plotted stability formed.

water means of

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uptake against

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> Gullfaks,

w/o-emulsions depends

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ability

of the example, the GulHaks the

Oil

crude Mixing

(water viscosity

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Arabian (based 10 Statfjord

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uptake time

for

R 12 heavy

on V

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(hours) |

a = 14 for

24-h parent

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stable. strongly (water-free)

purposes heavy).

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topped Figure

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all oil. 22

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the samples Oil + ► < • # ■

Statfjord

different Oseberg Ekofisk Ula crude Arabian types Gullfaks 7 type 24

water crude time, (a-b) :

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of of

K) O

1.0 ARABIAN HEAVY

0.9 Per 0.8

f 0.7 S.

STATEJORD 1 °'6 Doling, c 0.5 0.4 a

0.3 GULLFAKS Per 0.2

0.1 Johan 0.0

250°C+ 15h ph.ox. 20h ph.ox.

Oil Fractions Brandvik, (a) (C)

1.0 onald D 0.9 ARABIAN HEAVY „------0.8 / STATFJORD/ Z

ackay. M 4: u? 0.3 GULLFAKS/ z

/ 0.2 / istein 0 0.1

0.0

250°C+ 15h ph.ox. 20h ph.ox. Johansen Oil Fractions (b) (d) Fig. 7. Relative w/o-emulsion stability (R?/,-values) for (a), 1-h mixed w/o-emulsion (untreated); (b), 24-h mixed w/o-emulsion (untreated); (c), 1-h mixed w/o-emulsion treated with emulsion breaker; (d), 24-h mixed w/o-emulsion treated with emulsion breaker. Characterization of crude oils for environmental purposes 211 crude, weathering degree and the mixing time for the preparation of the w/o-emulsions. When adding chemicals (e.g. emulsion-breakers) to the prepared w/o-emulsions, the /?2/i-value can be used to obtain information concerning the relative effectiveness of the chemicals on different w/o-emulsion qualities (see Fig. 7(c-d)). Photolysis of the oils has a marked influence on the stability of the w/o-emulsion and on the effectiveness of the emulsion-breaking chemicals (Fig. 7(a-d)). The influence of the mixing time on w/o-emulsion stability may reflect the relatively large amount of smaller water droplets in w/o-emulsions mixed over a 24-h period relative to those mixed over a 1-h period (Fig. 8(a-b)). 212 Per S. Doling, Per Johan Brandvik, Donald Mackay, 0istein Johansen

2.4 Testing of chemical dispersibility

There has been some controversy as to which is the best laboratory method for dispersant effectiveness testing. From an assessment of the literature and the results of various studies in this field, it is clear that the various laboratory tests give different effectiveness results even for the same oil/dispersant combination because each test measures different phenomena. Variations in experimental parameters like turbulence (energy input), settling time and oil-to-water ratio in various test apparatus, are found to be very important for the effectiveness value (e.g. Fingas et al, 1989; Baling, 1988; Gillot et al, 1987). Since different test methods may give divergent results, it is valuable to conduct tests with different methods. From considerations of the scale, speed of testing and cost, we have evaluated three laboratory tests. Their basic principles and designs for producing mixing energy are very different. This implies that they will simulate different ‘sea states’ or conditions of use: • In the Mackay-Nadeau-Steelman test (MNS test: Mackay & Szeto, 1980) energy is applied by blowing air across the oil/water surface, producing a circular wave motion. The energy input in this system corresponds more to a medium to high sea-state condition. One sample of the oily water is taken under dynamic conditions and a second sample is taken after a settling period of 5 min (static conditions). • In the Labofma/Warren Spring Laboratory test (WSL test: Martinelli, 1984) mixing energy is supplied by revolving flasks which gives a relatively higher specific energy input compared to the MNS test. The mixing period is followed by a settling period of 1 min before sampling (static sampling). The WSL test was originally designed to compare dispersant performance on a standard oil prior to toxicity testing as a part of the UK’s dispersant approval regulations. • The IFF test (Bocard etal, 1984) is a low energy input test (compared to the MNS test) and is probably a more realistic approach because of the introduction of the dilution concept The sampling is taken under dynamic conditions. The chemical dispersibility of all the 13 oil samples (1 fresh and 12 weathered and w/o-emulsified samples, see Fig. 2) from each crude was tested using the three laboratory test methods (MNS, WSL and IFF tests). Additionally, the size distribution of oil droplets present in the dispersions produced by the test methods was measured. Characterization of crude oils for environmental purposes 213

The dispersibility testing was carried out under the following conditions: • Dispersants Finasol OSR-5 (concentrate, type 3) Finasol OSR-12 (conventional, type 1) • Dispersant-to-oil ratio (DOR) 1/25 for type 3 dispersants 1/2-5 for type 1 dispersants • Temperatures (water and air) 6°C and 13°C (North Sea winter and summer conditions) The dispersion results presented in this paper deal mainly with the 13°C test results with Finasol OSR-5. However, it is thought that the same approach can be used for other commercial dispersants. Figure 9(a and b) shows an example of the effectiveness of Finasol OSR-5 at 13°C on different North Sea crudes with the IFF and MNS methods respectively. The figures summarize the effectiveness results of fresh, topped, photolysed and emulsified samples (13 samples from each crude in total), all plotted as a function of the oil or w/o-emulsion viscosity (shear rate = 10 s_1). Due to the different design of the two test apparatus, the effectiveness results become somewhat different. However, the main trends agree fairly well with the two methods. Both Fig. 9(a) and 9(b) clearly demonstrate that the type of crude is an important factor in determining the performance of a dispersant at sea. The viscosity limit for the dispersibility of w/o-emulsions seems to be specific for the different crudes. This prevents the formulation of a general rule concerning the upper viscosity limit for dispersibility of w/o-emulsions. This kind of information represents crucial input to numerical weathering models for estimating the ‘time window ’ for chemical dispersion of specific crudes under different weather conditons. This issue will be discussed further in Section 3. No significance should be attached to the undulations in the curves in Fig. 9(a) and 9(b). The curves are drawn merely to convey an impression of the general trends in the data, the effect of oil type and the rapid drop in efficiency at high viscosities. The Malvern particle size analyser has been used to characterize the oil droplet size distribution in all the dispersions produced by the 13 oil samples in the three test methods. The droplet size measurements gave valuable information about the effectiveness of dispersants in addition to the mass balance of dispersed oil. Earlier work by Lewis et al. (1985) has shown that considerable insight can be obtained about dispersant performance by making such droplet size measurements. The median 214 Per S. Doling. Per Johan Brandvik. Donald Mackay, 0istein Johansen

IFF -TEST

Type of crudes:

* Ula crude • Ekotisk crude < Oseberg crude ► Statfjord crude ■ Gulifaks crude

Viscosity (cP)

WINS -TEST

\ \ Gull-Oseb|

Type of crudes:

* Ula crude • Ekofisk crude ◄ Oseberg crude ► Statfjord crude ■ Gulifaks crude

Viscosity (cP) (b) Fig. 9. ' Dispersibility of weathered North Sea crudes tested by two test methods: (a) IFP test; (b) MNS test (1st sampling). Dispersant: OSR-5 (DOR = 1:25), temperature 13°C. droplet size (Z)50, defined as the oil droplet diameter at which 50 vol% of the oil droplets consist of droplets less than the Ds0 value) and the droplet size distribution (expressed by the span = - Dw)/Dso) are sig nificantly different in the three tests (Fig. 10). This reflects the different design (particularly the energy input and the sampling procedure of the Characterization of crude oils for environmental purposes 215

WSL-TEST IFP-TEST

Median: 23.5 am Median: 30.6 urn

Span: 1.9 Span: 2.6

MNS-TEST MNS-TEST (2 sample

Median: 33.6 um Median: 23.6 urn

Span: 2.4 Span: 1.8

Particle sin lim). Fig. 10. Example of the median droplet size (/mi) and droplet size distribution (span) in the different test methods (Gullfaks 150°C+, treated with Finasol OSR-5 at 13°C). methods. In the systems with static sampling (WSL and 2nd MNS), the settling time allows the largest oil droplets to float up and out of the sample zone in accordance with Stokes ’ Law. This results in smaller values for both the Dso and the span. Both the dispersant effectiveness measurements (mass balance of dispersed oil) and droplet size measurements of the dispersed oil have clearly demonstrated that the three test methods are measuring different phenomena. The IFF method appears to be the most selective and most sensitive of the three laboratory methods according to type of dispersant, oil quality and weathering effects (see Fig. 9(a)). Due to the low surface energy and turbulence, the IFF method is especially sensitive to oils that are unusually viscous or close to their pour-points. Also the MNS tends to give good selective results between the different crudes particularly in the higher viscosity area. Due to the higher energy level, the MNS method (1st sampling) tends to give higher effectiveness values compared to the IFF results. Due to the settling time before sampling, the WSL test results become very sensitive to the resurfacing velocity of oil droplets and thus tend to give high performances for dense oils and low effectiveness values for light crudes. The high relative volume of oil used in this test system may be another important negative factor, causing interaction and coalescence of oil droplets giving low and unreliable results (Daling, 1988). The WSL 216 Per S. Doling, Per Johan Brandvik, Donald Maekay, 0istein Johansen method is, therefore, not as suitable a laboratory method as the MNS and IFF tests in studying chemical dispersibility with respect to different oil qualities and effects of weathering and w/o-emulsification. More systematic field testing is, however, still needed before we can fully evaluate the reliability of the different test methods for predicting oceanic performance.

3 USE OF EXPERIMENTAL DATA IN NUMERICAL WEATHERING MODELLING

The efficiency of various marine oil spill combat methods is known to depend to a large extent on the physical properties of the oil. As discussed above, this is particularly true for chemical dispersants, where increased viscosity due to weathering and w/o-emulsion formation may make the oil resistant to dispersants within hours (or days) after the oil is spilled. Hence, reliable predictions of the changing properties of the oil during variable sea conditions may be of great value in determining the ‘time window' for efficient application of dispersants. It is believed that similar limitations also exist e.g. for the efficiency of burning. Different approaches to the oil property prediction problem have been established. One approach is to derive a set of ‘mixing rules’, where the various physical properties of the oil are derived on the basis of the changing composition of the weathered oil. Simple mixing rules may be relevant for prediction of some of the properties, e.g. the density of the oil, while this type of approach maybe less relevant for other properties, such as viscosity and pour-point. As a consequence, we have chosen a more direct, empirical approach, where the laboratory measurements discussed previously serve as the basis for the predictions. The laboratory investigations characterize an oil ’s properties at different degrees of evaporative loss by distillation (topping, see Section 2.1). A link between the laboratory data and weathering properties under field conditions may be established on the basis of computations of evaporative loss and w/o-emulsion formation. The basic assumptions providing this link are: (1) The product remaining after a given fraction is topped by distillation corresponds to the product remaining after an equal amount is removed by evaporation at sea. (2) The w/o-emulsification process studied in the laboratory corres ponds to the process taking place at sea. Characterization of crude oils for environmental purposes 217

3.1 Evaporative loss

The first assumption implies that in general a 1:1 relation exists between the properties of the oil after evaporation of a certain fraction of the crude, and the properties measured in the laboratory after an equal fraction has been topped off by distillation. However, corrections for temperature are made for the viscosity, based on a standard ASTM temperature relationship. The fraction evaporated is computed by a pseudo-component concept, with the original composition defined in terms of the boiling point curve (Reinhart & Rose, 1982). Parameters such as equilibrium vapour pressure, molecular weight and specific density are derived from empirical relations related to the mean boiling point of each boiling point cut and the ambient temperature. The other parameters taken into account include the oil film thickness and a wind speed dependent mass transfer coefficient (Smith. 1988). The evaporative loss is obtained by numerical integration of the evaporation rate equation with variable time steps, adjusted on the basis of stability consideration related to the most volatile component remaining in the mixture. The weathered oil properties include the following, with reference to non-emulsified (water-free) oil: oil density, oil viscosity, flash-point and pour-point. They are predicted by interpolation of the experimental data (Table 2) on the basis of the computed evaporative loss.

3.2 W/o-emulsion formation

For w/o-emulsions, the viscosity is known to depend on the viscosity of the parent oil and the water content. To establish the water content in field conditions, the following procedure is utilized (ref. assumption 2): The rate of w/o-emulsion formation is known to depend on the mixing energy applied. Based on the rotating flask experiments, this rate may be expressed in terms of the parameter t12. i.e. the mixing time elapsed when the water content is half of the maximum water content obtained after 24 h of mixing. To establish a link between the laboratory experiment and sea conditions, the laboratory values of tm must be related to a given sea state, and the sea state dependence on the rate of w/o-emulsion formation must be known. Presently, this link has been established on the basis of laboratory measurements on Ekofisk crude and field data on the same crude published by Cormack (1983). The comparison between these sets of data indicates that the rate of water uptake obtained with the rotating 218 Per S. Doling. Per Johan Brandvik. Donald Mackav. Oistein Johansen flask corresponds to field conditions with wind speed of 20 m s_1. This comparison was based on a wind dependence of the rate of w/o-emulsion formation proposed by Mackay et al (1980), i.e.

' ,/2((/2) = Wt/,) (1 + (/,)'/(! + C/2): (1) where C/,, U2 represent different wind speeds. The maximum water content obtained after 24 h of mixing in the rotating flask experiment was found to depend on the stage of weathering of the parent oil, as shown previously in Fig. 6. The method used implies an exponential increase in the water content towards these maximum values, with a rate determined by ther,/: values adjusted to the actual sea state. The viscosity of the w/o-emulsion fu) depends, as indicated above, on the viscosity of the parent oil (ju oil ), and the water content. A relationship proposed by Hossain and Mackay (1980) is applied for this purpose:

jj = /joil exp[a FT/(100 - b W)] (2) where a and b are empirical constants: a = 2-5, b = 0-654, and W is the water content in the w/o-emulsion (%). However, results from the viscosity measurements of w/o-emulsions formed in the laboratory procedure indicate that the empirical constant a in eqn (2) is dependent on the stage of weathering, and the type of crude under investigation. For this reason, we have chosen to use an empirically determined factor a in the predictions, adjusted to fit the viscosity ratio obtained from the laboratory measurements on the different weathered oil samples.

3.3 Applications

The method described above implies in short terms that the properties of weathered oils are obtained from a standardized laboratory investigation of the crude oil. These data are then transferred to sea conditions by computations of evaporative loss and w/o-emulsion formation related to a chosen set of initial conditions (sea state, sea temperature, oil film thicknesses). Figure 11 gives a schematic diagram of the experimental data that are put into the model, and the predicted properties that come out of the model. This procedure is presently applied in Norway for production of manuals on weathered properties of selected North Sea crude oils, including graphical charts for the development of each property with time under a chosen set of sea conditions (wind speeds) both at summer Numerical

Weathering Characterization models

* Laboratory data of fresh and Predicted oil properties by time weathered oil samples: at chosen environmental conditions:

Distillation curve (TBP) Evaporative loss of

Densities Oil density crude Viscosities Oil viscosity

Flash point

Flash points oils Pour point Pour points

Water content for Water uptake rates (t 1 ^-values) Viscosity of W/O-emulsion

Maximum water uptake ability * "Time window" for use of dispersants environmental Viscosity ratios (W/O-emulsion/parenl oil) t Viscosity limits for chemical dispersion Criteria used in the model

Environmetal

conditions purposes (Wind speed, sea temperature, oil film thickness)

Fig. 11. Schematic diagram of the input data to the model and the predicted output oil properties.

3 220 Per S. Dating, Per Johan Brandvik. Donald Mackay, 0istein Johansen

PROPERTY: EVAPORATIVE LOSS Summer conditions (15*0

80 -

0 25 050 6 9 12 3 4 5 Hours

...... Wind speed 2 0 m/s ------Wind speed 5 0 m/s ------— Wind speed 10 0 m/s ■------Wind speed 15 0 m/s

PROPERTY: POUR-POINT Summer conditions M5*C)

025 050 2 3 6 9 12 Hours

...... Wind-speed 2 0 m/s I I Dispersible — ------—Wind speed 50 m/s r\\S Reduced dispersibility — — —Wind speed 10 0 m/s -Wind speed 150 m/s Y/A Not dispersible Fig. 12 (a). Examples of standard charts on predicted oil properties (evaporative loss and pour-point) of Oseberg crude under a chosen set of sea conditions (wind speeds). Characterization of crude oils for environmental purposes 221

PROPERTY: WATER CONTENT Summer conditions (15eC)

025 0-50 3 4 5 Hours

...... Wind speed 2 0 m/s ------Wind speed 5 0 m/s ------Wind speed 10 0 m/s ------Wind speed 15 0 m/s

PROPERTY: VISCOSITY OF EMULSION Summer conditions (15“C) 100000

10000

* 1000

0-25 050 6 9 12 Hours

-••- Wind speed 2 0 m/s I I Dispersible --Wind speed 5-0 m/s KVi Reduced dispersibility — Wind speed 10 0 m/s Y//A Not dispersible — Wind speed 15 0 m/s Fig. 12 (b). Examples of standard charts on predicted oil properties (water content and viscosity) of Oseberg crude under a chosen set of sea conditions (wind speeds). 222 Per S. Dating, Per Johan Brandvik, Donald Mackay. 0istein Johansen and winter sea temperatures. Examples of such standard charts are given in Fig. 12(a) and 12(b), including evaporative loss, pour-point, water content and viscosity of the w/o-emulsion in terms of time for a set of wind speeds. Corresponding charts are produced for the other properties of concern, i.e. density, flash-point and viscosity of the parent oil. Criteria related to the effectiveness of dispersants are included in the charts, based on the results from the laboratory investigations (e.g. Fig. 9). This implies threshold values for viscosity of the w/o-emulsion, and limitations resulting from the increase in the pour-point of the oil. This means that the ‘time window ’ for chemical treatment operations can be estimated for the specific crude under a chosen set of sea conditions (Fig. 12(b)). The prediction model is presently running on a VAX main-frame computer; however, to make this model generally available, we aim in the near future at implementing the model on IBM-compatible personal computers.

4 CONCLUSIONS AND RECOMMENDATIONS

Extensive laboratory investigations of crude properties combined with numerical modelling have been used for predicting the behaviour of oils spilled on the sea. The new approach has clearly demonstrated that the type of crude is essential for the rate of the weathering processes (water uptake, viscosity increase, etc.) under different sea conditions. The method reveals that even for the North Sea crudes, where variation in the physico-chemical parameters is relatively small (same oil-class according to EPA, 1982), the weathering behaviour and the effectiveness of dispersants at sea may vary greatly. It is hoped that the approach used here will be considered by other laboratories and used in a similar way on a large number of crude oils. This kind of investigation will provide valuable data input which may be used later for extending present prediction models. With experimental weathering data available for a wide range of oils, correlations may be derived for reliable predictions of oil weathering properties on the basis of only generally available crude assay data (e.g. true boiling point curves, density, pour point, wax and asphaltene content, etc.). It is also hoped that such oil property predictions can form a basis for reliable evaluations of the ‘time window ’ for effective use of other countermeasures (including mechanical recovery equipment, burning and use of other oil spill chemicals) on various oil qualities which may be spilled under a variety of environmental conditions. Characterization of crude oils for environmental purposes 223

ACKNOWLEDGEMENTS

The work reported here is part of the research programme presently running at IKU called ‘Application of Dispersants on Weathered Oils (DIWO)'. The DIWO programme is funded by Fina Exploration Norway Utenl. A/S. Special gratitude goes to Halfdan Akre-Aas (Akre-Aas Miljokjemi A/S) and Alain Charlier (Fina Research) for their extensive engagement in the research program.

REFERENCES

Bocard, C., Castaing, G. & Gatellier. C. (1984). Chemical oil dispersion in trials at sea and in laboratory tests: the key role of dilution processes. In Oil Spill Chemical Dispersants: Research , Experience and Recommendations. STP 840, ed. T. E. Allen. American Society for Testing and Materials. Philadelphia. PA, pp. 125-42. Bridie, A. L., Wanders, Th. H., Zegveld, W. & van der Heijde, H. B. (1980). Formation, prevention arid breaking of sea water in crude oil emulsions, chocolate mousse. Marine Poll. Bull., 11, 343-8. Cormack, D. (1983). Response to Oil and Chemical Marine Pollution, Applied Science Publishers Ltd, Barking. 36 pp. Baling, P. S. (1988). A study of the chemical dispersibility of fresh and weathered crude oils. In Proc. 11th Arctic and Marine Oil-Spill Program Technical Seminar. Environment Canada, Ottawa, Ontario, pp. 481-99. Baling, P. S.&Alm&s, I. K. (1988). Bescription of laboratory methods in Part 1 of the BIWO-project. BIWO Report No. 2. IKU, Trondheim, Norway. Baling, P. S. & Brandvik, P. J. (1988). A study of the formation and stability of water-in-oil emulsions. In Proc. 11th Arctic and Marine Oil-Spill Program Technical Seminar. Environment Canada, Ottawa. Ontario, pp. 153-70. EPA (Environmental Protection Agency) (1982). Manual of practice — Chemical agents in oil spill control, EPA Report No. 600/8-82-0/0, Washington, BC. Fingas, M., Bebra, L. M., White, B., Stoodley, R. G. & Crerar, I. B. (1989). Laboratory testing of dispersant effectiveness: The importance of oil-to- water ratio and settling time. In Proc. 1989 Oil Spill Conf, API, Washington, BC, pp. 365-73. Gillot, A., Charlier, A. & van Elmbt, R. (1987). Correlation results between IFP and WSL laboratory tests of dispersants. Oil & Chem. Pollut. 3(6). 445-53. Hossain, K. & Mackay, B. (1980). Bemulsifiers — a new chemical for oil spill countermeasures. Spill Technology Newsletter, 5(6), 154-6. Lewis, A., Byford, B. C. & Laskey, P. R. (1985). Significance of dispersed oil droplet size in determining dispersant effectiveness under various conditions. In Proc. 1985 Oil Spill Conf, API, Washington, BC, pp. 433-40. Lichtenthaler, R. G. & Baling, P. S. (1985). Aerial application of dispersants — comparison of slick behaviour of chemically treated versus non-treated slicks. In Proc. 1985 Oil Spill Conf. API, Washington, BC, pp. 471-8. Mackay, B. (1987). Formation and stability of water-in-oil emulsions. BIWO Report No. 1, IKU, Trondheim, Norway. 224 Per S. Doling. Per Johan Brandvik. Donald Mackay, Oistein Johansen

Mackay, D. &Chau, A. (1987). A study of oil dispersion. The role of mixing and weathering. Environment Canada Report, Ottawa, Ontario, March. Mackay, D. & Szeto, F. (1980). Effectiveness of Oil Spill Dispersants — Development of a Laboratory Method and Results for Selected Commercial Products. Institute of Environmental Studies. University of Toronto, Ontario, Publ. No. EE-16. Mackay, D. & Zagorski, W. (1982). Studies of water-in-oil emulsions. Environment Canada, Ottawa, Ontario, Report EE-34. Mackay, D., Buist, I., Mascarenhas, R. & Paterson, S. (1980). Oil spill processes and models. Environment Canada. Ottawa. Ontario, Report EE-8. Mackay, D., Chau, A. & Poon, Y. C. (1985). A study of the mechanism of chemical dispersion of oil spills. Environment Canada Report, Ottawa, Ontario, March. Martinelli. F. N. (1984). The status of the Warren Spring Laboratory’s Rolling flask test. In Oil Spill Chemical Dispersants: Research. Experience and Recom mendations. STP 840, ed. T. E. Allen. American Society for Testing and Materials, Philadelphia. PA, pp. 55-68. Martinelli. F. N. & Lynch, B. W. J. (1980). Factors affecting the efficiency of dispersants, Warren Spring Laboratory. Stevenage, Report LR363. Morris. P. R. & Martinelli, F. N. (1983). A Specification for oil spill dispersants. Warren Spring Laboratory. Stevenage. Report LR4488(OP). National Academy of Sciences (1985). Oil in the Sea: Inputs. Fates and Effects. National Academy Press. Washington. DC. NRC (National Research Council) (1989). Using Oil Spill Dispersants on the Sea. Report of the Committee on Effectiveness of Oil Spill Dispersants, Chairman J. N. Butler. National Academy Press, Washington, DC. Reinhart, R. & Rose, R. (1982). Evaporation of crude oil at sea. Water Res., 16, 1319-25. Ross, S. L. (1986). An experimental study of the oil spill treating agents that inhibit emulsification and promote dispersion. Environment Canada. Ottawa, Ontario, Report EE-87. Smith, S. D. (1988). Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J. Geophys. Res.. 93 (Cl 2). 15467-72. Stiver, W. & Mackay. D. (1984). Evaporation rate of spills of hydrocarbons and petroleum mixtures. Environ. Sci. Techno!., 18 (11), 834-40. (paper 2)

Laboratory testing of dispersants under arctic conditions

The use of Chemicals in Oil Spill Response, ASTM STP 1252, Peter Lane, Ed., American Society for testing and Materials, Philadelphia, 1995

Per Johan Brandvik, Merete M. 0verli, Ole 0ysten Knudsen and Per S. Baling IKU Petroleum Research, SINTEF Trondheim, Norway

Dr. thesis Per Johan Brandvik, March 1997 P. J. Brandvik1, O. 0. Knudsen1, M. 0. Moldestad 1 and P. S. Baling 1

LABORATORY TESTING OF DISPERSANTS UNDER ARCTIC CONDITIONS

REFERENCE: Brandvik, P. J., Knudsen, O. 0., Moldestad, M. 0., and Baling, P. S., "Laboratory Testing of Dispersants under Arctic Conditions," The Use of Chemicals in Oil Spill Response. ASTM STP 1252. Peter Lane, Ed., American Society for Testing and Materials, Philadelphia, 1995.

ABSTRACT: The effectiveness of relevant dispersants for use under "Arctic conditions" has been tested with the IFP dilution test. "Arctic conditions" in this context are defined as low temperature (0°C) and water salinities varying between 0.5% and 3.5%. The study was performed in three steps with a screening activity first, where 14 dispersants were tested on water-in-oil (w/o) emulsions from two weathered oil types. In the next step five dispersants were tested on both weathered water free oils and w/o emulsions from four different oil types. As a third step, dispersant effectiveness as a function of salinity (0.5 to 3.5%) was tested with the most effective dispersants at high and low salinity.

The results from this study shows that many of the most used dispersants which previously have shown an excellent effectiveness at high sea water salinity (3.5%) may give a very low effectiveness at low salinity (0.5%). Recently developed products especially designed for low salinity use (e.g. Inipol IFF) are very effective at low salinities, but suffer from a rather poor effectiveness at higher salinities. This is of significant operational importance in Arctic oil spill combat operations since the salinity of the surface water may vary due to ice melting.

This study of dispersants' effectiveness under Arctic conditions shows the need for development of dispersants with high effectiveness both at low temperature (0°C) and over a wide range of salinities (3.5% to 0.5%). Dispersant development has been an limited but important activity at IKU for the last five years and one of the objectives for an ongoing Arctic program at IKU is to develop such new dispersants for use under Arctic conditions.

KEYWORDS: Dispersant, oil weathering, optimization, effectiveness testing, Arctic conditions, salinity.

*IKU Petroleum Research, SINTEF-group, N-7034 Trondheim, Norway.

191 192 THE USE OF CHEMICALS IN OIL SPILL RESPONSE

INTRODUCTION

Since the Torrey Canyon accident more than 20 years ago, extensive studies have been carried out in order to assess the effectiveness and biological effects of using dispersants in oil spill combat operations, see e.g. [1]- As a result of these research activities, the use of dispersants have got increasingly favourable attention in many countries during the past years. This is due to the manufacturing of less toxic dispersants, better application techniques and better knowledge of the biological effects of dispersants and chemically dispersed oil. The limitations for the use of existing mechanical equipment (booms and skimmers) have also become more clearly defined during recent years.

From an operational point of view it is essential to identify the types of dispersant products which will show the best response to the different groups of oils, and under various environmental conditions. Although much progress has been made during the past 10 years, further research is needed in optimizing the effectiveness of dispersants under different oil spill situations. One aim of the previous research at IKU has been to map the chemical dispersability of different North Sea crudes at various degrees of weathering at typical North Sea environmental conditions [2], Development of new dispersants for North Sea crudes and North Sea environmental conditions has been another objective for previous projects at IKU [3]. The main part of the commercial available dispersants have been formulated and tested mainly for use at temperate sea water (sea temperature above 10°C and salinities around 3.5%). Preliminary studies on a limited number of dispersants indicate that many of these products may fail at low temperatures and also at low salinity [4, 5, 6 and 7].

Research performed during the last years at IKU and at other research institutions (e.g. ELF in France and Exxon in the US) has shown a large potential for optimization of dispersants. This is particularly relevant for customizing dispersants for different groups of oil types (e.g. waxy crudes or bunker oils) or specific environmental conditions (e.g. Arctic conditions; low temperature and low salinity).

As oil exploration moves further north on the Norwegian continental shelf, the questions concerning the efficiency of oil spill contingency under Arctic conditions become more important. The results presented in this paper are from the first part of a research project at IKU where the aim is to test the existing dispersants and to develop new dispersants with higher effectiveness under Arctic conditions. "Arctic laboratory conditions" in this context are defined as low temperature (0°C) and sea water salinity varying between 0.5% and 3.5%. The aim of the work presented in this paper is to see how the water salinity influenced on the effectiveness of the dispersants at low temperature both with different types of oils and with different degrees of weathering. This work contains results from a laboratory screening of the effectiveness of 14 commercial dispersants and results from a more extensive testing of five dispersants from the screening test are also given. In addition, dispersant effectiveness as a function of salinity (0.5 to 3.5%) is tested with the most effective dispersants at high and low salinity. BRANDVIK ET AL. ON TESTING UNDER ARCTIC CONDITIONS 193

EXPERIMENTAL CONDITIONS

Effectiveness testing The effectiveness of the dispersants were tested in the laboratory with the IFP dilution test [8], Further details concerning this dispersant effectiveness test method are described by Baling and Brandvik, [9]. The effectiveness from the IFP-test shows the different dispersants relative ability to disperse an oil into the water column. The test conditions for the IFP-test in this study were: Dispersant to oil or dispersant to w/o- emulsion ratio: 1:25, temperature: 0°C and sea water salinity: 3.3 and 0.5 %. These salinities were selected because 0.5% and 3.5% are expected to be the extreme values for surface waters in Arctic areas.

The effectiveness results presented are mean values of 2-4 parallel measurements. The overall pooled standard deviation of all the IFP-measurements in this study is 5.7. If two mean values (with 3 parallel measurements) is to be compared, the difference must be 13 (IFP%) or larger to be significant at a 5% level. This confidence level is calculated by using a single sided Student t-test.

Droplet size measurements In addition to these mass balance measurements, median volume diameter of the dispersed oil droplets was measured with a Malvern Ec Laser Particle Sizer. Further details concerning these measurements are given in earlier reports [9] and f 101.

Oil viscosity measurements The viscosity of the oils and the w/o-emulsions was measured by using a Haake RV20. The measurements were performed by increasing the shear rate from 0 to 11.7 s -1 and from 0 to 117 s~* respectively within two minutes. The viscosities were calculated from the flow curve at shear rate 100 s'* for the waterfree oils and 10 s~* for the w/o- emulsions.

Oil weathering The oils were artificially weathered in the laboratory before they were used in this study. The weathering processes taken into account are evaporation and w/o- emulsification. The natural evaporation from an oil slick was simulated by a one stage distillation procedure, also called "topping". The procedure is a modified ASTM DF86/82 and is further described in 1111. The distillation removes the most volatile components from the oils and the degree of weathering is determined of the distillation temperature. The terminology "Oseberg 250°C+" indicates an Oseberg crude where the one stage distillation is continued until the vapour temperature has reached 250°C.

The w/o-emulsions were prepared by a modified rotating flask apparatus [121. The cylindrical separation funnels (0.51) containing sea water and oil (volume ratio 1:1) were rotated (30 rpm) for a period of 24 hours [9]. The weathering degrees of the oils used in this study are given in table 1. 194 THE USE OF CHEMICALS IN OIL SPILL RESPONSE

Table 1: The oil types used in this study and the degrees of weathering for both waterfree and w/o-emulsions. The water content in the w/o-emulsions is 50%.

Oil type Weathering Pour point Oil viscosity Emulsion degree (°C) waterfree viscosity waterfree oil (cP)1 50% water (cP)2 Oseberg 150°C+ 9 100 500 Gullfaks 250°C+ 0 810 4300 Veslefrikk 150°C+ 15 90 1500 IF-30 Bunker 150°C+ 0 3200 10000 1 measured at shear rate 100 s'1 2 measured at shear rate 10 s'1

Oils types used in this study Very limited data material exist concerning the chemical properties of the crudes from the Arctic areas of Norway. The oils used in this "Arctic study" have for this reason been selected among the crudes produced in the North Sea. The oils have been selected to give a large variation in their physico-chemical properties. A short description of the different oils used in this study is given below.

Oseberg blend crude is a medium heavy North Sea crude (0.853 kg/1) with a relatively high wax and a low asphaltene content (4.5 and 0.45 wt.%). These values are for the fresh crude while the weathered oil used in this study (150°C+) has a pour point of 9°C.

Gullfaks crude is a medium heavy North Sea crude (0.882 kg/1) with a low wax and very low asphaltene content (1.6 and 0.07 wt.%). This is a naphthenic crude where most of the n-alkanes are biodegraded by micro-organisms in the reservoir. The pour point for the fresh crude is less than -30°C while the weathered oil used in this study (250°C+) has a pour point of 0°C.

Veslefrikk crude is a light paraffinic North Sea crude (0.839 kg/1) with a relatively high waxcontent (4.6 wt.%) and a low asphaltene content (0.1 wt.%). The fresh crude has a pour point of 6°C while the weathered oil used in this study (I50°C+) has a pour point of 15°C.

Bunker fuel (Intermediate Fuel-30) is a refinery product which contains approximately 65% bunker-C (DF-340) and 35% gasoil which gives a density of 0.936 kg/1. Intermediate fuels are defined from their viscosities and IF-30 has a viscosity of 30 cP (50°C). IF-30 has a low wax and medium to high asphaltene content (2.5 and 4.1 wt.%). The oil used in this study (150°C+) has a pour point of 0°C. BRANDVIK ET AL. ON TESTING UNDER ARCTIC CONDITIONS 195

Dispersants used in this study The dispersants used in this study are listed in table 2. The selection of these products were based on earlier effectiveness testing both at 0 and 13 °C and at 0 and 3.5% salinity, see 1131 and [7], The selected dispersants should also form a representative selection of the products commercially available to day. In addition one experimental product (IKU-9) was added.

Table 2: Density and viscosity of dispersants used in this study. Viscosity is measured at 0°C and density at 15.5°C.

Density (kg/1) Viscosity (cP) Corexit 9527 1.017 208 Corexit 9550 0.940 290 Basic slick gone FW 0.902 45 Basic slick gone LTS 0.970 155 Basic slick gone NS 0.874 56 Dispolene 36S 1.029 216 Dispolene 38S 1.041 237 Enersperse 700 0.981 171 Enersperse 1037 0.948 39 Finasol OSR-5 1.035 309 Finasol OSR-5 2 1.004 245 IKU-9 0.922 101 Inipol IPC 0.902 50 Inipol IPF 0.925 95

RESULTS AND DISCUSSIONS

Screening testing of dispersants To test a broader selection of commercially available oil spill dispersants, totally 14 dispersants were tested in a screening stage of the study. These dispersants were tested with two North Sea crudes (Oseberg and Gullfaks), two salinities (3.5 and 0.5%) and at 0°C. Mean values (2-4 parallel measurements) from the effectiveness testing are given in figure la-b and 2. Figure 1 shows dispersant effectiveness with Oseberg 150°C+ w/o- emulsion containing 50% water with a viscosity of 500 cP. Figure 1 a-b illustrate that many dispersants which show a high effectiveness at 3.5% salinity have a rather poor effectiveness at 0.5% salinity.

With the IFP test, which is a low energy test, a value below 40% is regarded as low and a value in the effectiveness range of 60 - 80% is regarded as high. At high salinity (figure la) only 6 of the 14 tested dispersants have an high effectiveness (60-80%), while only 3 of these 6 dispersants have also an medium effectiveness (40 - 60%) at low salinity (figure lb). So of the 14 tested dispersants only 3 dispersants in figure la-b have 196 THE USE OF CHEMICALS IN OIL SPILL RESPONSE an effectiveness above 40% at both salinities. In addition 2 products especially designed for fresh water use (Dasic Fresh Water and Inipol IFF) have an effectiveness above 40% only at low salinity (figure lb). None of the tested dispersants showed an effectiveness above 60% at low salinity in figure lb.

The most drastic drop in effectiveness when salinity was reduced from 3.5% to 0.5% was observed for Dasic NS and IKU-9 which both reduced their effectiveness from approx. 80% to below 15%. The fact that these products are especially optimized for North Sea conditions (3.5% salinity) may explain this drastic reduction in effectiveness.

The w/o-emulsion prepared with the weathered Gullfaks crude (250°C+) in figure 2 has a higher viscosity (4300 cP) than the Oseberg crude (500 cP) in figure 1 a-b and is therefore generally more difficult to disperse. However, the main trends in figure lb and 2 are the same and the 5-6 most effective dispersant are the same.

A limited number of dispersants were tested in the second phase due to the high number of IFF test required. From this first screening test, 5 products were selected for phase two. These products were selected based mainly on the ranking in the screening test. Inipol IPC, Inipol IFF and Enersperse 700 were selected based on their general high effectiveness. Enersperse 1037 compared to Dasic Fresh Water (FW) have higher or equal effectiveness in all three tests, but Dasic Fresh Water was selected. This was done because Dasic FW is a product designed for low salinity, and that Enersperse 1037 showed the same performance trends as Enersperse 700, but with a lower effectiveness. Finasol OSR-52 was also included although it was not among the most effective products since it is a new product replacing Finasol OSR-5, previously used as a "reference dispersant" in our studies at IKU. The selected products were: Inipol IPC, Enersperse 700, Finasol OSR-52, Dasic Fresh Water and Inipol IFF. Figure Effectivness (%) Effectivness (%) 100 80

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197 198 THE USE OF CHEMICALS IN OIL SPILL RESPONSE

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Disc NS InipolTC DticLTS Disic Fresh W. Disp.36S OSR52 DispjSS KU-9 E-700 E.-1037 Cmstil 9527 CorexS 9550 1*01 IFF OSR5 Dispersant

Figure 2: Effectiveness and median droplet size of the same 14 dispersants as in figure 1 at 0°C and 0.5% salinity, with Gullfaks crude w/o-emulsion (250°C+ with 50% water).

Extended dispersant testing The five dispersants selected from the screening study were tested with four different oil types at two different salinities (3.5% and 0.5%). These four oil types were used at two different weathering degrees (see table 1). The main objective of this extended study has not been to rank the individual dispersants, but to get an overview of the dispersant effectiveness at 0.5 and 3.5% salinity on different weathered oils with a large variation in both chemical and physical properties.

The effectiveness of the five dispersants on these oil types is given in figure 3 for the water free oils and in figure 4 for the w/o-emulsions. Average values for the effectiveness of each dispersant, with all the four oils and emulsions, are calculated to give an overview for the discussion of the general trends among the dispersants. The average values are given in table 3.

The average values for the dispersant effectiveness for all the four oil types in table 3, shows mainly the same trend both with the waterfree oils and the w/o emulsions. None of the tested dispersants give a high average effectiveness both at high and low salinity. At low salinity (0.5%) Inipol IFF has the significantly highest average effectiveness both for waterfree oils and for w/o emulsions. En'ersperse 700 has the second highest average effectiveness, while the other three products have low and not significant different average effectiveness from each-other. BRANDVIK ET AL. ON TESTING UNDER ARCTIC CONDITIONS 199

Table 3: Average effectiveness of the tested dispersants in both3.5% and 0.5% salinity water. The effectiveness values are average values for each dispersant with all four oil types (Ranking in brackets ).

0.5% salinity Water-free oils* 50% emulsions Grand average Inipol IPF 73% (1) 56% (1) 65% (1) Enersperse 700 59% (2) 40% (2) 48% (2) Finasol OSR-52 54% (2) 19% (3) 33% (3) Inipol IPC 35% (3) 31% (3) 32% (3) Dasic Fresh water 39% (3) 23% (3) 30% (3) 3.5% salinity Water-free oils* 50% emulsions Grand average Inipol IPF 25% (3) 24% (3) 25% (2) Enersperse 700 76% (1) 58% (2) 67% (1) Finasol OSR-52 53% (2) 25% (3) 39% (2) Inipol IPC 65% (1) 72% (1) 69% (I) Dasic Fresh water 43% (2) 32% (3) 38% (2)

* The Veslefrikk 150°C-\- results are not included in these averages because of the pour point problems. This is discussed later in the report.

At high salinity both Inipol IPC and Enersperse 700 showed a significantly higher average effectiveness than the other three products (table 3). For the w/o emulsions Inipol IPC also showed a significantly higher average effectiveness than Enersperse 700. Inipol IPF which showed the highest effectiveness at low salinity, was the dispersant with the lowest effectiveness at high salinity. This large difference in effectiveness between 0.5% and 3.5% salinity for Inipol IPF indicates a lack of dispersants with high effectiveness over a wide range of salinities. Enersperse 700 has the highest overall average effectiveness with a high to medium (76-40%) effectiveness at both salinities. This ranking is based on average values for all the four oil types (see table 3), but the ranking is relative consistent for the four different oil types (see figure 3 and 4).

For the water-free oils of the paraffinic Veslefrikk crude the effectiveness of all dispersants is very low (<5%), which is likely due to the high pour point (15°C) of the crude. Earlier studies have shown that if the test temperature is 10-15°C lower than the actual oil’s pour point the dispersability of the oil will be drastically reduce [9]. Surprisingly, this was not the case with the w/o-emulsion with the same crude where an effectiveness up to 80% was measured (figure 4a). This may be explained by the wax particles gathering at the water-oil interface to stabilize the water droplet in the w/o- emulsion. The ability to form wax crystal structures in the w/o-emulsion may be reduced since some of the wax particles are bound at the water-oil interface. 200 THE USE OF CHEMICALS IN OIL SPILL RESPONSE

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Figure 3: Effectiveness of five dispersants on four different water free oils at 0°C and both 0.5% and 3.5% salinity. Figure

Effectiveness (%) Effectiveness (%) Effectiveness (%) Effectiveness (%) 4: 100 120 100 ICO 120 120 40 20 20 40 20 40 60 60 80 120 100 20 40 0 0 60 0 0

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201 202 THE USE OF CHEMICALS IN OIL SPILL RESPONSE

The effectiveness of the five dispersants show some similar trends on both Oseberg and Gullfaks crudes in this study. The w/o-emulsion made from the viscous IF-30 bunker fuel had the lowest dispersability of all the w/o-emulsions, especially for the w/o-emulsion made from and tested in low salinity water. Only Inipol IFF of the tested dispersants was able to significantly break this high viscous w/o-emulsion and to some degree (38%) disperse the oil at low salinity.

Salinity effects To test the effectiveness of two different dispersants as a function of water salinity, Inipol IFF and Inipol IPC were tested with water salinities in the range from 0.5 to 3.5% (see figure 5). The effectiveness results in figure 5a show that neither of the two products have a high effectiveness over the total salinity range. Inipol IFF has an high effectiveness at salinities from 0.5 to 1.25 %, but the effectiveness drops down to 30-10% above a salinity of 2%. On the contrary Inipol IPC have a low effectiveness under 2% and a high effectiveness above a salinity of 2%.

High effectiveness dispersants form oil-in-water emulsions with small oil droplets. Large oil droplets (approximately above 60-70 microns) will have a larger tendency to resurface and increase the amount of non-dispersed surface oil and lower the effectiveness of the dispersants. This correlation between the effectiveness and the droplet size of the dispersed oil can clearly be seen from figure 5a-b.

Of the 14 tested dispersants Inipol IFF is the only dispersant that is significantly more effective at low salinities (both in the screening and in the extended study, see figure 1, 2, 3 and 4). Finasol OSR-52 and Dasic Fresh Water show in some cases a higher effectiveness at low salinity, but these observations do not represent significant trends. In recent studies at IKU (the DIWO-2 program) dispersants with high effectiveness at low salinity have been developed, but the real challenge is to develop dispersants with high effectiveness over a wide range of salinities and at low temperatures.

Possible theories for the different effectiveness at 0.5 and 3.5% salinity might be the fact that the surfactants behave differently when the salinity changes. Attwood and Florence f 141 claim that the surface activity of ionic surfactants depends on the water salinity, because the ions in the water change the electric field which always is generated on the oil/water interface. Another possible reason for the decrease in effectiveness could be different tendencies for the surfactants to leach from the oil into the water phase when the salinity of the water changes 1151. More fundamental work is needed to fully explain the different interfacial phenomena occurring when dispersants are used at varying sea water salinities. Figure Droplet size (microns) Dispersability (%) 5:

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203 204 THE USE OF CHEMICALS IN OIL SPILL RESPONSE

CONCLUSIONS AND RECOMMENDATIONS

The results from this study show that many dispersants which previously have shown a high effectiveness at high salinity (3.5%), may give a very low effectiveness under low salinity conditions (0.5%). This is of significant operational importance for an Arctic oil spill combat operations since the salinity of the surface water may vary in an ice melting situation.

Recently developed products especially designed for low salinity use (e.g. Inipol IFF) are very effective at low salinities, but suffer from a rather poor effectiveness at higher salinities. This study of dispersant effectiveness under Arctic conditions shows the need for further development of dispersants with high effectiveness at both low temperature (0°C) and over a wider range of salinities (e.g. from 0.5% to 3.5%). Our recommendation is that fundamental studies involving oil spill chemistry and applied surface chemistry could be undertaken to generate new dispersant formulations with higher effectiveness over both a wider salinity range and at low temperature.

In addition to high effectiveness it is also important that the physical properties for the dispersants are relevant for low temperature operations (e.g. in addition, high dispersant viscosity at low temperature may reduce the effectiveness due to application difficulties).

Also several logistic related subjects (e.g. turbulence levels required for dispersion in ice infested waters, application systems for low temperature operations) need further investigation before the potential of dispersants as an oil spill combat technique in Arctic areas can be fully evaluated.

ACKNOWLEDGEMENT

The authors would like to thank Fina Exploration Norway for the funding of an large Arctic oil spill research program at IKU (DIWO) which this study is a part of and the authors would also like to thank Norwegian Clean Seas Association (NOFO) for the funding of an earlier project were some of the data used in this study is compiled from. BRANDVIK ET AL. ON TESTING UNDER ARCTIC CONDITIONS 205

REFERENCES

[1] National Research Council, USA (NRC), Using oil spill dispersants on the sea. National Academy Press 1989, Washington D C., 250 pp.

[2] Baling, P.S. and Brandvik, P.J., Characterization and prediction of the weathering properties of oils at sea - a manual for the oils investigated in the DlWO-project. IKU-report no.: 02.0786.00/16/91, 1991.

[3] Brandvik, PJ. and Baling, P.S., Statistical experimental design optimization of dispersant's performance. Proc. 13th Arctic Marine Oil Spill Program Technical Seminar. 1990 Environment Canada pp. 243-254.

[4] l Byford. D.C., Green, PJ. and Lewis, A., Factors influencing the performance and selection of low temperature dispersants. Proc. 6th Arctic Marine Oil Spill Program Technical Seminar. 1983 Environment Canada, pp. 140-150.

[5] Lehtinen, C.M. and Vasala, A-M., Effectiveness of oil spill dispersants at low salinities and low water temperatures. In: Oil Spill Chemical Dispersants: Research, Experience and Recommendations. STP 840. Tom E. Allan (ed.) American Society for testing and Materials, Philadelphia 1984, pp. 108-121.

[6] Bocard, C., and Castaing, G., and Gatellier, C., Chemical oil dispersion in trials at sea and in laboratory tests: the key role in dilution process. In: Oil Spill Chemical Dispersants: Research, Experience and Recommendations, STP 840, ed. T.E. Allen. American Society for Testing and Materials, Philadelphia 1984, PA, pp. 125-42.

[7] Baling, P.S., Singsaas, I. and Hokstad, J.N., Testing av dispergeringsmidlers effektivitet under arktiske betingelser. (in Norwegian) IKU-report no.: 22.2008.00/01/91, 1991.

[8] Bocard, C. and Castaing, G., Dispersant effectiveness evaluation in a dynamic flow through system - the IFP dilution test. IKU-report no.:02.0706/04/87, 1986.

[9] Baling, P.S. and Brandvik, P.J., Characterization of crude oils for environmental purposes. Proc. 13th Arctic Marine Oil Spill Program Technical Seminar. 1990 Environment Canada pp. 119-138. riOl Baling, P.S. and Almas, I.K., Description of laboratoiy methods in part 1 of the DlWO-project - a technical report. IKU-report no.: 02.0786.00/2/88, 1988. fill Stiver, W. and Mackay, D., Evaporation rate of spills of hydrocarbons and petroleum mixtures. Environ. Sci. Technol. . Vol. 18, no.: 11 pp. 834-840, 1984.

1121 Mackay, D. and Zagorski, W., Studies of water-in-oil emulsions. Report EE-34: Environment Canada 1982, Ottawa, Ontario, Canada. 206 THE USE OF CHEMICALS IN OIL SPILL RESPONSE r 131 Brandvik, P.J., Baling, P.S. and Aareskjold, K., Chemical Dispersability testing of fresh and weathered oils - an extended study with eight oil types. IKU-report no.: 02.0786.00/12/90, 1990. f 141 Attwood. and Florence, A.T., Surfactant systems, their chemistry, pharmacy and biology. Chapman and Hall Ltd, London, 1983.. f 151 Mackay, D., Chemical dispersion, a mechanism and a model. Proc. 8th Arctic Marine Oilspill Program Technical Seminar. 1985 Environment Canada, pp. 260- 268 . (paper 3)

Statistical simulation as an effective tool to evaluate and illustrate the advantage of experimental designs and response surface methods

Submitted for review and publication in: Chemometrics and Intelligent Laboratory Systems, November 1996.

Per Johan Brandvik IKU Petroleum Research, SINTEL Trondheim, Norway

Dr. thesis Per Johan Brandvik, March 1997 Statistical Simulation as an effective Tool to evaluate and illustrate the Advantage of Experimental Designs and Response Surface Methods

PER JOHAN BRANDVIK

IKU Petroleum Research, SINTEF N-7034 Trondheim, Norway

ABSTRACT

The most illustrative way to evaluate the benefit of experimental designs is to use them in practice. This includes setting up the designs, generating data and performing statistical analysis on this data. However, testing several different experimental designs in the laboratory is in many cases both time consuming and expensive. As a cost-reducing alternative to this traditional testing, statistical simulations can be used. This paper shows how simulations can be used to evaluate three different experimental designs’ ability to describe the interaction between three process variables (surfactants in an oil spill dispersant) and a quality describing variable (the dispersant effectiveness). Two simplex- lattice designs and one traditional change "one-variable-a-time" (OVAT) approach are compared and the ability of the two first designs to find an optimum composition at a lower cost (fewer experiments) is demonstrated. Furthermore, simulations are used to evaluate the increased cost of replicate measurements against the increased accuracy. This paper shows that statistical simulation is a powerful tool to evaluate and illustrate the benefit of using designed experiments.

INTRODUCTION

It is often difficult to introduce other scientists and students to the field of experimental design and multivariate analysis. It is a question of obtaining their interest and to motivate them to do their first designed experiments within their own field. The advantage of experimental design and multivariate analysis is often difficult to illustrate without doing experiments. But, unfortunately, performing experiments is in many cases both time consuming and expensive. This could prevent new scientist from entering the fascinating and useful field of experimental design.

In many cases this problem can be solved by using statistical simulation to generate “artificial”, but realistic data sets. These simulations are based on statistical sampling after an experimental design from a model of a process. Analysis of these simulated data will then discover the ability of different experimental designs to describe the interaction between the process variables and the quality describing variable. This approach has been used by the author to introduce other scientists and students to the benefits of using experimental design and multivariate analysis. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 2

The model used can be an established, well proven model or a simplified empirical description of the process studied. Processes which can be adequately described by simplified models are for example spreading of a pollutant in the sea from an offshore platform or the yield from a chemical synthesis.

Simulated data have also previously been used for training or demonstration purposes within chemistry. Rosental and Arnold [1] developed a general model for chemical reaction kinetics and used it to generate data to show the potential of statistical analysis. Statistical simulations with established models have also been used to test alternative hypothesis concerning for example enzyme activation reactions [2], or protein binding sites [3], Ahmed [4] used a model describing a chemical reactor system for producing acrylic acid to statistically simulate the influence of 6 process variables on the predicted yield.

The process studied in this paper is the quality of a chemical product versus its composition. The product is a dispersant used in oil spill cleanup operations to enhance the rate of the natural dispersion of an oil spill at sea. The dispersant is sprayed onto the oil slick from boats, helicopters or fixed wing aircraft and the oil is dispersed into the water column as small droplets. The large increase in the oil-water interface due to oil droplet formation increases the biodegradation of the oil by naturally occurring micro-organisms. The dispersant consists of different surface active components (surfactants) dissolved in a solvent or a blend of solvents. The effectiveness of the dispersant in this study is tested by standard laboratory methods [5] and is defined as the percentages of the oil which is dispersed into the water column.

In this paper, “artificial” laboratory data is generated from a simplified empirical model describing the influence of surfactant composition on the dispersant effectiveness. Three different mixture designs and the change "one-variable-a-time" or "OVAT" design are used to generate laboratory data from this simplified model. Response surface analysis on this data reveal the different experimental designs ability to estimate the known optimum surfactant composition. Also the benefit of doing different numbers of replicate measurements on the precision in the estimated optimum is evaluated against the increased cost.

This multivariate approach, which is new within optimising of oil spill dispersants, has been verified and used to laboratory data to optimise dispersants for different oil types and weathering degrees. This work is described in two separate papers [6] and [7],

MATERIALS AND METHODS

Only limited laboratory work was performed as a part of this simulation study. The model used was based on earlier effectiveness testing performed using the “IFF procedure ” with the surfactants and the oil type described below.

Dispersant effectiveness testing The effectiveness of the dispersants was tested in the laboratory with a French test apparatus from Institut Francais du Petrole, Paris (EFP). The basic principle for this IFF dispersant effectiveness test is to apply a certain amount of oil (4 g) in a container with sea water (6 1) and then dispersant (200 (Xg or 5% compared to the amount of oil) is added. Low energy is then applied to the system by a submerged beater simulating wave action or turbulence at sea. The amount of oil dispersed into the water after 1 hour is quantified by extracting the oil from an aliquot of the water. The effectiveness is calculated as the percentage of oil removed from Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 3 the surface. All experiments were performed in a climate room at 13°C and the sea water salinity was 3.5%. Further details concerning the dispersant effectiveness testing is presented in several earlier publications [8] and [5],

Surfactants used The surfactants tested in the laboratory work initially performed in this simulation study included surfactants with both large structural differences and only smaller hydrophilic to lipophilic balance (HLB) modifications. The exact identity of the surfactants are not revealed due to ongoing patent registration of the developed dispersants, but their structure is described in the next papers in this series [6] and [7].

Oil type used The oil type use for the dispersant testing was a North Sea crude from the Oseberg field. This oil was artificially weathered (evaporation of the lightest components) to approximately simulate an oil slick which has been on the sea surface for 3-6 hours. Further details concerning this oil type and the weathering procedure have been published earlier [5],

Description of the simulated process To be able to perform simulations it must be possible to describe the process we want to simulate. This description can vary from very sophisticated models which are based on a solid theoretical foundation and later verified with laboratory measurements or rather simple empirical models which still give a realistic and adequate description of the process for example, the model used in this study.

The modelled process can be approximately described by an equation of the form:

Equation 1 General model or response function. y = f(fi,x) + € y: The quality describing variable(s), which we want to simulate ft: Vector of constants for the model x: Vector of input variables for the model e: Vector of residuals (difference between model and measured data )

The process studied in this paper is the interaction between the surfactants in an oil spill dispersant and how this influence on the dispersants effectiveness to disperse an acute oil spill at sea. The molecular interactions between the different surfactants in these blends are known to be very complex [9]. The mechanism of the surfactants is to orient themselves on the interface between oil and water and lower the interfacial tension. The lowest interfacial tension is measured when the packing of the surfactant molecules at the interface is most effective. By using surfactants with different molecular structure and size, the packing efficiency of the surfactant can be increased. For this reason the ability of the experimental design to describe the interactions between the process variables (the surfactants) is very important.

Simulation of laboratory data The response surface described in figure 1 was used as the model surface for the simulations performed in this study. This surface is based on prior experimental data and, although it is a simplification of the "true" surface, it has proved to be realistic with a low lack-of-fit [12]. For Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 4 every value of xj, x2 and x3 (surfactant concentrations) a response function (e.g. equation 4) was used to give a corresponding y-value (dispersant effectiveness).

The data set we want to simulate is the dispersant effectiveness measured in the ten experimental points given by the design marked in figure la. The simulation will then produce a data set which will consist of x,,x2 and x3 from the design and the corresponding y-values. An example of such a simulated data set is given in table 1.

Table 1: Composition of the dispersants and the corresponding effectiveness, example of simulated data set.

Expr Xl x2 x3 y no

i 80.0 10.0 10.0 23 2 10.0 80.0 10.0 45 3 10.0 10.0 80.0 22 4 45.0 45.0 10.0 34 5 10.0 45.0 45.0 67 6 45.0 10.0 45.0 45 7 56.7 21.6 21.6 19 8 21.6 56.7 21.6 78 9 21.6 21.6 56.7 69 10 33.3 33.3 33.3 66

The process variables involved are:

Process variables: xj, Relative amount of surfactant 7 (70-80%)

X2, — 2 (70-8096) X3, 3 (70-80%) Quality describing or response variable: y, Dispersant effectiveness (0-700%)

To be able to perform statistical simulations the natural variation in the process must be known and built into the simulation algorithm. To obtain as realistic simulations as possible the distribution of both the process variables (xj, x2 and x3) and the quality describing variable (y) must be known. Prior experiments had shown that both process and response variables were approximately normally distributed and quantified the variances in both types of variables. The standard deviation of Xj, x2 and x3 was 0.02 or 2% and was mainly estimated from weighting and blending related errors. The standard deviation of y was 8 (IFP%) and was caused by the variation in the experimental equipment used to determine the dispersant effectiveness [20]. These values, describing the natural variation in the process, were regarded as conservative estimates and were probably somewhat higher than the real values.

The simulation of a single measurement of dispersant effectiveness was done as follows (y s is a simulated value for y and so on): Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 5

(1) Determine the values of x„ x2 and x3 from the first point in the selected experimental design. (2) Generate simulated value x,s from a normal distribution with known standard deviation (0.02) and mean value (x,). This is repeated for x2s and x3s. (3) Generate simulated value y s from a normal distribution with known standard deviation (8) and mean value y= f(p, x,\ x2s, x3s).

From the starting values for the composition (Xi,%2 and X3) we have now obtained a simulated value y s. This simulation is done from the realistic surface described in figure 1. If this simulation were repeated (e.g. 1000 times) with the same xi, X2 and x3 values an estimated distribution of y (N(f(p,xi,x 2,x3), 64)) would be obtained.

Confidence regions for optimum composition A response surface, as shown in figure 1, can be used to find the optimum composition for a dispersant. The optimum composition is described by the position of the highest point in the surface. The prediction of such a surface is based on a data set of the type shown in table 1. When such a surface is evaluated a logical question would be, what is the reliability of this surface and the predicted optimum composition? If new measurements were done, what would be the optimised composition predicted from this second surface? A confidence region for the predicted optimum would describe this variation between several replicate data set. To establish even a rough estimate of a two dimensional confidence region several data sets (minimum 10-12) had to be generated in the laboratory. In our case this was not possible due to the high cost of the experiments, but we still wanted to get an idea of the variation in the predicted optimum composition. The response surfaces estimated from the simulated laboratory data were used to estimate these confidence regions.

Experimental designs used In the blending experiments performed in this study, the constituents are ingredients of a mixture, and their levels are not independent. For example, a dispersant that consists of a mixture of surfactants dissolved in a solvent, the total amount of surfactants and solvent has to add up to 1.0 (or 100%). If the amount of one surfactant is changed, then the relative ratio of all surfactants is changed. When mixture design is used to optimise dispersant formulation, the measured response (effectiveness) is assumed to depend only on the relative proportions of the components in the dispersant. The mixture design used in this work is called a simplex- centroid design [10] and [11], and is shown in figure la.

The design in figure la is constructed with a limiting and equal constraint of 10 per cent for all three surfactants. This was done because all three surfactants were expected to be essential for the dispersion process. Dispersants consisting of only one or two surfactants (located along the outer side of the triangle in figure la) were expected to show a very low effectiveness. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 6

Figure la-b : The composition of the surfactant blends (xt, x2 and X3) and their dispersant effectiveness are shown as a contour plot in la. For each experimental point both the measured and estimated effectiveness are given, (estimates are in brackets ). The effectiveness is illustrated as a response surface in lb. The surfactant blend which gives the highest effectiveness is marked with •.

Multivariate statistics - response surface methods The combination of designed experiments and response surface methods is a powerful tool in product optimisation, aiming towards as few experiments as possible. If the effectiveness for all possible combinations in figure lb was known, the effectiveness could be related to the composition by introducing a third dimension in the triangle plot. This third dimension, called the response surface, is shown in figure la-b. The dispersant effectiveness is visualised, for every possible blend of the three surfactant, by the height of the response surface. This response surface, in figure 1, is estimated by response surface methods and is based on prior experimentation and earlier published results [9] and [12].

In mixture designs, the process variables (x„ x2 and x3) are not independent, but proportions of a total amount and the sum of all the process variables is constant. Analysis of mixture data necessitates a special model to eliminate this mixture constraint. Both Scheffe and Cox developed canonical polynomial models with constraints on the coefficients which are widely used to analyse mixture data. Scheffe [13] and [14] was one of the first to publish response models for mixture problems and his work has been of great importance for the development of mixture designs.

To solve the problem with correlated variables, since the constituents add up to a fixed sum, he developed the Scheffe canonical polynomials by eliminating some terms from the complete polynomial model. The simplex-lattice designs are boundary designs with the same number of experimental points as coefficients estimated in the corresponding Scheffe model. The coefficients in Scheffe models can be calculated analytically [10]. Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 7

Equation 2: An example of Scheffe models, the quadratic model

y = P,x, + P2x2 + P3x + P,2x,x2 + p i3x,x3 + P23x2x3

Scheffes models, e.g. as shown in equation 2, are widely used because they are simple, coefficients are easy to calculate, contain no extra terms and are symmetrical, but there are several problems using these models. Scheffes models are easy to interpret if we are working with pure components; then Pj is the effect of the pure component x, and P,2 is the combined effect of x, and x2 and so forth. However, in most applications we are not working with pure components and the interpretation of the coefficients becomes complicated. Models without a constant term, also have no normalisation or centering of the data, before the coefficients are estimated. This produces different estimates for the coefficients if a constant value is added to each of the data points.

Cox recognised these and other problems with Scheffes models [15] and introduced several improvements and established what is later know as the Scheffe-Cox models. The new proposed models are only reparameterisations of Scheffes models, but offer several advantages over them. The introduction of the constant term P0 represents a centering of the model and increases the interpretability of the coefficients [10].

Different types of models were tested initially in this multiple regression approach; linear, quadratic, special cubic and special quartic were tested and the lack-of-fit calculated. Since the degree of complexity of this system was known to be high, a special quartic equation [10] with a constant term was used (equation 3) to describe the response surface in figure 1. This complexity of the model was necessary, due to the strong molecular synergy between the surfactants.

Equation 3: The special quartic equation describing the response surface in figure 1.

y = Po + P,x, + p 2x2 + 03x3 + P,2x,x2 + p i3x,x3 + p 23x2x3 + p 1123x,2x2x3 + P,223x,x22x 3 + p i233x,x2x32+ e

The coefficient in the Cox-Scheffe models is in this study estimated with multiple regression (MLR), instead of using the exact analytical approach. Since we used replicate measurements the total number of measurements would exceed the number of coefficients. MLR was used without problems due to the low degree of constraints in the experimental designs. The constraint is equal for all three components and limits the minimum content of each component to 10%. Projection methods like Partial Least Squares (PLS) are also effective for analysing mixture problems, since PLS can be used to analyse dependant or highly correlated variables. PLS is especially useful with large and heavily constrained mixture designs where the experimental space is a irregular polyhedron with a high number of extreme vertices. In such large and heavily constrained mixture designs both the data analysis and the interpretation of the coefficients are better described by using PLS instead of MLR [16]. On the other hand, both MLR and PLS will give the same analytical solution and offer the same Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 8 interpretability of the coefficients with the relative simple mixture designs used in this study [16]. PLS is extensively described and compared with other projection methods in the literature [17] or [18] and will not be further described here.

Finding the optimum of the established response surface When a response surface in established (figure lb), finding the optimum composition is limited to finding the highest point on the surface described by the response function (marked with • in figure lb). The estimated response function was combined with a "steepest ascent" algorithm [19] to find the highest point on the response surface.

RESULTS

The results from this study are presented in three different sections. First, simulations of the basic laboratory data are presented, then the simulations showing the variation in the estimated optimum and the influence of performing replicate measurements. Finally, the results from the simulations performed with different experimental designs are presented.

By using the simulation algorithm described in the previous section, the generation of data sets of the form given in table 1 could be repeated. Simulation was repeated with realistic variation and from each of the simulated data sets a response surface and the corresponding optimum composition were estimated. From a large number of such simulations a confidence area for the optimum dispersant composition was calculated. Figure 2 shows the result from 1000 simulations where only the predicted optimum compositions are marked together with the 95% confidence region. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 9

Figure 2: The results from statistical simulation. 1000 individual data set simulated and optimum composition calculated from the estimated response surface. Only the predicted optimum compositions and the 95% confidence region are marked. The experimental design used is a simplex centroid design with only one replicate measurement.

Figure 2 shows that the true optimum point, from figure 1, is within the estimated confidence region described by the simulations. The average SD in the optimum surfactant blend is estimated to be ± 4.7%, see table 2.

The distribution of the optimum compositions and the size of the confidence region in figure 2 is dependent on:

1. The nature of the process (the shape of "true" response surface) 2. The random noise in the process 3. The selected experimental design

If we still accept the model surface in figure 1 to be adequate for this simulation purpose (term 1 above), then we can study the effect of the two other terms (random noise and selected design) on the distribution of the predicted optimum. Questions to be answered by this type of study are:

1. What can be gained by doing two or three replicate measurements instead of only one? How much does the precision in the predicted optimum composition increase compared to the increased cost? 2. What will be the consequences of using another and maybe more simplified experimental design? Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 10

Similar simulations as shown in figure 2 were performed, with 2 and 3 replicates in each experimental point in the design, to quantify the benefit of doing replicate measurements. The results from these simulations, showing the smaller confidence regions obtained with the increased number of replicates, are given in figure 3a-b.

Figure 3a-b: The results from statistical simulation. 1000 individual data set simulated and optimum composition calculated from the estimated response surface. Only the predicted optimum compositions and the 95% confidence region are marked. This simulations are based on the same experimental design (simplex centroid design) as in figure 1, but with two (a) and three (b) replicate measurements. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 11

Table 2 Mean optimum and SDfor the simulated optimum surfactant composition (wgt. %)in figure 2, 3a and 3b.

One replicate measurement Two replicate measurements Three replicate measurements figure 2 figure 3a figure 3b Average ± SD Average ± SD Average ± SD xj: 36.0 ±5.7 xj: 36.5 ± 3.5 xj: 36.5 + 2.8 %2' 42.2 ±5.0 x2: 41.8 ± 3.5 x2: 41.8 ±2.8 x%: 21.7 ±3.5 X3: 21.7 + 2.0 X3: 21.7 ± 1.5 Mean SD: 4.7 Mean SD: 3.0 Mean SD: 2.4

The effect of using different experimental designs What type of experimental design do we select for our application and if several good candidates exist, how do we take a final decision? Several good textbooks exist that give excellent advice, or general rules, for the selection of experimental designs [19] and [21]. Examples of specific applications, or references to such applications, of experimental designs for a large variety of areas can also be found in the these text books.

The power of a specific design or several candidates can be evaluated on a relevant example by using statistical simulations as performed in this study. Figure 4 a-b shows the results from simulations with two alternative simplex lattice designs, the [3,3] and the [3,2] simplex designs [10]. In figure 4a the ten experimental points are rearranged compared to the design in figure 1 and figure 4b shows a simplification of the design from figure 1. The [3,3] simplex lattice design in figure 4a can support the fitting of a quadratic model [10] (equation 4), which was used in the simulations with this design. The reduced design in figure 4b contained only seven experimental points and for this reason only seven coefficients in the model equation can be estimated (equation 5).

Equation 4: Model equation used together with the design in figure 4a (both (3 and 8 are used for estimated coefficients).

y = PA + P2x2 + P3X3+ P,2X,X2 + P,3X3X3 + p23x2x3 + p,33X3X3X3 + 5,2x,x2(X i-x2) + 833X3X3 (x,-x3) + 833X3X3 (x2-x3)

Equation 5: Reduced model equation used together with the design in figure 4b.

y = Pa + p2x2 + P3X3+ Pi2x,x2 + p,3X3X3 + p23x2x3 + p,33X3X3X3

Figure 4c shows a design following the previously well established change "one-variable-at-a- time" or OVAT-strategy, which still is the only approach accepted among many experimenters. This optimisation strategy is performed in two steps, first the ratio of X1-X3 is held constant and x2 is varied, then the X1-X3 ratio is varied with the "optimal" setting of x2. We can also see from figure 4c that 20 experimental points are used in the “OVAT” design and that a smaller part of the total experimental area, within the triangle, is explored. No surface modelling was performed since the aim with an OVAT approach is to locate the optimum conditions directly. The variation in the optimum points from the OVAT approach (figure 4c) reflects weighting and blending related errors of the surfactant blends. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 12

Figure 4a-c: The results from statistical simulation. 1000 individual data set simulated and optimum composition calculated from the estimated response surface. Only the predicted optimum compositions are marked. The simulations are based on three different experimental designs. Two alternative simplex-lattice designs (a-b) and one design illustrating the OVATapproach, (c). All simulations are based on two replicate measurements.

Table 3 Mean optimum and SDfor the simulated optimum surfactant composition (wgt.%) in figure 4a-c.

Design from figure 4a Design from figure 4b Design from figure 4c Average ± SD Average ± SD Average ± SD Xj: 33.5 ± 18.0 x j: 37.7+12.2 X,: 26.0±2.1 x2: 47.8 ± 16.0 x2: 39.2 ± 10.7 x2: 56.7 ±2.1 xv 18.7 ± 13.8 X,: 23.1 ±13.8 xv 17.3 ±2.0 Mean SD: 15.9 Mean SD: 12.2 Mean SD: 2.1 Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 13

DISCUSSION

The effect of increasing the numbers of replicates A study of the influence of performing replicate measurements simulations with one, two and three replicates was performed, see figure 2 and 3a-b. The size of the confidence regions in figure 2 and 3a-b reflects the uncertainty reduced by performing replicate measurements. The mean standard deviation (SD) for the three surfactants in the estimated optimum points are 4.7, 3.0 and 2.4 for one, two and three replicates, respectively (see table 2). How this reduced uncertainty is evaluated against the increased cost will vary from application to application, but these figure helps the researcher to evaluate this cost/benefit dilemma. For our further work described in two later papers [6] and [7] we decided to use two replicate measurements as a compromise between reduced uncertainty and increased cost. This approach has also been used by others e.g. Passing and co-workers [22] used statistical simulations to determine the number of replicate analysis needed in a clinical analysis of blood serum.

Evaluation of different experimental designs To study the potential of using other experimental designs, simulations with three additional designs were performed, see figure 3a and 4a-c.

The alternative simplex-lattice design in 4a does not have the same power to estimate the higher order interactions (e.g. (3,,23) that are needed to describe the shape of the model response surface. The relative amount of the surfactants are varied pair-wise for nine of the ten experimental points and the relative amount of the third is held constant. If the second order interactions (e.g. P12 or P22) had been more dominant in our model, this design (figure 4a) could have been a better choice than the design in figure 2. Earlier Cornell published a study comparing the {3,3} simplex and simplex centroid design based on experimental data, where he drew similar conclusions [23]. The differences in the mean optimum point, compared to the correct optimum point for the model (figure la) are -2.5, 5.6 and -2.5 (x,, X2 and x3), see table 3. These differences (2.5 to 5.6%) are not regarded as particularly large for this application, but the uncertainty (SD = 14 to 18) in this optimum point is very large. It can also be shown by comparing figure 3a and 4a that this design gives a broad confidence region.

The design in 4b is a simpler design with seven experimental points and the equation used to estimate the response surface, equation 5, has not the same complexity as equation 4. The consequence of the reduced complexity is a lower ability to describe a more "banana" shaped optimum area compared to a circular "bell" shaped area. This is shown in figure 4b where the optimum region is more circular compared to figure 3a. The difference in the mean optimum point compared to the real optimum point (figure la) is smaller for this design, 1.0, -1.8 and 0.9 (xi, X2 and x3), but the uncertainty is still three to seven times larger than the standard deviation in the individual surfactants in figure 4a. This can also be seen by comparing the size of the confidence regions in figure 3a and 4b.

It is illustrative in figure 4c to observe how the traditionally OVAT-approach fails to give a correctly positioned confidence region for the predicted optimum point, even though 20 experimental points are used instead of only ten. The difference in the mean optimum point compared to the real optimum point for the model (figure la) was largest with this design, - 10, 14.5 and -4.4 (xj, x% and x3), compared to the designs described in figures 3a and 4a-b. The uncertainty in the optimum is not large only reflecting the weighting and blending errors. The selection of the initial xj-x3 ratio is also critical and require detailed prior knowledge Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 14 concerning this process. It is also important to notice the limited experimental space which was explored by the 20 experimental points with the OVAT-approach in figure 4c.

CONCLUSIONS

This paper shows how statistical simulations can efficiently be used to illustrate the power of experimental design and response surface methods. It has been shown in this study how different types of experimental designs and different numbers of replicate experiments can influence the predictions from a response surface model. The main advantages of such simulation studies are the low cost and the immediate response to the changes in the assumptions for the model.

The main disadvantage with simulation studies is that we need prior knowledge about the process to establish a model. However, relatively simple models, empirical or theoretical based, can often be used as an approximation to the process. Also, the different sources and the magnitude of the random noise in the process have to be known, but estimates can often be used with good results.

The simplex-centroid design was selected as most powerful for this specific type of applications. The special quartic equation was also found to give the best description of the model surface. This model surface was also regarded as representative for this blending problem with a high complexity of the variable interactions. From the results with one, two and three replicate measurements it was decided to use two replicate measurements for future work [6] and [7] as a compromise between reduced uncertainty and increased cost.

It is also important to observe, that the change “one-variable-a-time ” or “OVAT” design lacks the ability to describe the model surface and to locate the correct optimum even if twice as many experiments were performed.

This paper has shown that simulation can be used as an efficient and low cost tool to illustrate the use of experimental design and response surface methods. The approach proposed in this paper has later been used with real laboratory data to optimise oil spill dispersants for use on weathered crude’s and bunker fuels. This work is described in the next two papers in this series [6] and [7].

ACKNOWLEDGEMENTS

The authors wish to thank the following persons and institutions: Fina Exploration Norway a.s for funding this study at as a part of a seven year research program at IKU called "Dispersability of weathered oils - a laboratory study - DIWO". Olaf Gram, Fina Exploration Norway and Alain Charlier, Fina Research Brussels, Belgium for valuable assistance and discussions. Professor Steinar Engen, Norwegian University of Science and Technology, NTNU, is thanked for introducing the author into this fascinating field of simulations. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 15

REFERENCES

1. D. Rosenthal, and D. Arnold. Simulation of experimental data, the design of experiments, and the analysis of results. Kinetics of catalyzed ester hydrolysis, J. Chem. Educ., 54(5), 323-5, (1977). 2. A. Vanzquez, R. Varon, J. Tudela, F. Garcia-Canovas. Kinetic characterization of a model for Zymogen activation: an experimental design and kinetic data analysis, J. Mol. catal., 79(1-3) 347-63, (1993). 3. A.Z. Khan and L. Aarons. Design and analysis of protein binding experiments, J. Theor. Biol., 140(2), 145- 66. (1989 ). 4. F. Ahmed Design and analysis of simulated chemical reactor experiments, J. Bangladesh Acad. Sci 9(1) 11- 18, (1985). 5. P.S. Dating, P.J. Brandvik, D. Mackay, 0. Johansen, Characterisation of crude oils for environmental purposes: Oil & Chemical Pollution 7, 1990/91, pp. 199-224, (1991).

6. P.J. Brandvik and P.S. Dating. Optimisation of oil spill dispersant composition by mixture design and response surface methods, Chemometrics and Intelligent Laboratory Systems, (submitted for publication). 7. P.J. Brandvik and P.S. Dating. Optimising oil spill dispersants as a function of oil type and weathering degree - a multivariate approach using partial least squares (PLS), Chemometrics and Intelligent Laboratory Systems, (submitted for publication).

8 . C. Bocard, G. Castaing and C. Gatellier. Chemical oil dispersion in trials at sea and in laboratory tests: the key role of dilution processes. In: Oil Spill Chemical Dispersants: Research. Experience and Recommendations . STP 840, Tom E. Allen, Ed., American Society for Testing and Materials, Philadelphia, pp. 125-142,(1984). 9. C. Brochu, E. Pelletier, G. Caron and J.E. Desnoyers. Dispersion of crude oil in seawater: The role of synthetic surfactants. Oil and Chem. Pollut. 3 (1986/87): 257-279, (1987). 10. J.A. Cornell. Experiments with mixtures. Wiley, New York, 2nd Ed., (1990). 11. A.L. Khuri, J.A. Cornell. Response surfaces. Design and analyses . Marcel Dekker Inc., ASQC Quality Press, New York, (1987). 12. P.J. Brandvik and P.S. Dating. Statistical experimental design in the optimisation of dispersant ’s performance. Proceedings of the 13th AMOP-seminar, Environment Canada, Edmonton, (1990). 13. H. Scheffe. Experiments with mixtures. J. R. Stat Soc., B, 20, No. 2, 344-360, (1958). 14. H. Scheffe. Simplex-centroid design for experiments with mixtures. J. R. Stat. Soc., B, 25, No. 2, 235-263, (1963). 15. D.R. Cox. A note on polynomial response functions for mixtures, Biometrica, 58(1), 155-159, (1971). 16. N. Kettaneh-Wold. Analysis of mixture data with PLS. Chemometrics and Intelligent Laboratory Systems, 14: 57-69,(1992). 17. H. Martens, T. Naes. Multivariate Calibration . John Wiley and Sons, Chichester, (1989). 18. O.M. Kvalheim. Interpretation of direct latent-variable projection methods and their aims and use in the analysis of multicomponent spectroscopic and chromatographic data. Chemometrics and Intelligent Laboratory Systems 4 (1988) 11-25. 19. G.E.P. Box, W.G. Hunter. J.S. Hunter. Statistics for experimenters. An introduction to design, data analysis and model building. John Wiley & Sons, New York, (1978). 20. O.0. Knudsen and P.S. Daling. Proposed procedure for dispersant testing - new Norwegian regulations for approval of dispersants, IKU Petroleum Research, Trondheim, Norway, Report no.: 94.147 (in Norwegian), (1994).

21. S.N. Deming, S.L. Morgan. Experimental design: a chemometric approach, In: Data handling in science and technology - Volume 3. (1987). Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 16

22. H. Passing, W. Bablok and M. Glocke. The establishment of assigned values in control serums. IV. an optimized design for the establishment of assigned values in control serums, J. Clin. Chem. Clin. Biochem., 19(12), 1167-79,(1981).

23. J.A. Cornell. A comparison between two ten-point designs for studying three-component mixture systems. J. Qual. technol., 18, 1-15, (1986). (paper 4)

Optimisation of oil spill dispersant composition by mixture design and response surface methods

Submitted for review and publication in: Chemometrics and Intelligent Laboratory Systems, November 1996.

Per Johan Brandvik and Per S. Baling IKU Petroleum Research, SINTEF Trondheim, Norway

Dr. thesis Per Johan Brandvik, March 1997 Optimisation of Oil Spill Dispersant Composition by Mixture Design and Response Surface Methods

PER JOHAN BRANDVIK and PER S. BALING

IKU Petroleum Research, SINTEF N-7034 Trondheim, Norway

ABSTRACT

Oil spill dispersants are used to enhance the rate of natural dispersion of an oil spill at sea. Dispersants removes the oil slick from the sea surface and dilute the oil as small droplets in the water column. The large increase in the oil-water interface due to oil droplet formation increases the biodegradation of the oil by natural occurring micro-organisms.

Mixture design (simplex-centroid) and response surface methods have in a earlier simulation study [1 ] proved to be an effective tool to enhance optimisation of oil spill dispersant and to reduce the number of experiments needed for development of new products. This proposed multivariate method is representing a new approach within the development of oil spill dispersants. The main objective for the work presented in this paper was to verify the performance of this new approach on real laboratory data.

This combined technique using mixture design and response surface methods has been verified to be a powerful and cost reducing approach in dispersant optimisation. New dispersant formulations for both crude oils and bunker fuels have been formulated and verified by measurements to have high effectiveness.

INTRODUCTION

This paper is a continuation of a previous paper in this journal entitled "Use of statistical simulations to evaluate the advantage of designed experiments and response surface methods" [1]. In the previous paper, statistical simulations were used to show the potential of using mixture design and response surface methods in optimising the effectiveness of oil spill chemicals. Several different designs and surface methods were tested and the number of replicate measurements needed where determined as a part of a proposed new optimisation approach. This paper presents results from a laboratory study were this new approach has been tested and verified in dispersant development.

Combined use of simplex lattice designs and response surface methods has previously been used for effective product optimisation. For example, several authors have used mixture designs to optimise solvent properties in liquid chromatography. Wieling and co-workers [2] and Gladjch and co-workers [3] used this combined technique to optimise the solvent selectivity in HPLC systems. Coenegracht and co-workers [4] used the same approach to simultaneous optimise resolution and analysis time with reversed ion-pair liquid chromatography. Wang and Yan [5] worked with high-performance thin-layer Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 2

chromatography and used an approach based on mixture designs to optimise the solvent selectivity.

The potential for use of modern experimental design and data analysis is large in the development of new dispersants, due to strong interactions among the surface active components (surfactants) used in these products. Oil spill dispersants are used to enhance the rate of natural dispersion of an oil spill at sea. Dispersants remove the oil from the sea surface and dilute the oil as small droplets in the water column. The large increase in the oil-water interface due to oil droplet formation increases the biodegradation of the oil by naturally occurring micro-organisms [6], The removal of the oil from the sea surface also decreases the possibility of oil stranding on beaches and damage to birds on the sea surface. Dispersants consist of different surface active components (surfactants) dissolved in a solvent or a solvent system. The surfactants are the active components which reduce the interfacial tension between water and oil and enhance the natural dispersion of the oil.

Several laboratory studies performed both in Norway and abroad have shown that the variation in performance of existing commercial dispersants is large [7], [8] and [9]. Experience from application of dispersant during the Sea Empress oil spill in February 1996, also showed varying effectiveness of different dispersants [10]. When a decision has been made to use dispersants in a given oil spill situation, it is crucial to select a dispersant that is formulated to give high effectiveness for the actual oil type and environmental conditions (temperature and salinity). For example an oil spill of a bunker fuel in a temperate climate would require a different type of dispersant than a crude oil spilled in a cold environment [11].

The amount of literature on optimisation of oil spill dispersants is not large due to the strong commercial interest connected to such product development. Previously performed work within this field is mainly performed or funded by oil companies or chemical suppliers. Most of the available literature is for this reason patents, describing the composition (surfactants and solvents) of these products in a rather broad terms, e.g. [12] and [13]. However, a publication from MAFF (1995) describing the surfactants used in approved dispersants in UK gives an overview covering the most commonly used surfactants [14].

The Hydrophilic-Liphophilic-Balance (HLB) has traditionally been used to characterise both single surfactants and blends of surfactants. The original definition is given by Griffin [15] and is still used today within surface chemistry [16]. The HLB value of a surfactant is calculated based on the contribution from the lipophilic and hydrophilic groups of the surfactant, but does not reflect important properties as molecular shape of the lipo/hydrophilic part or the size of the total surfactant. The HLB value of a surfactant blend is calculated assuming additive properties only. Surfactant blends to promote oil-in-water emulsions, such as oil spill dispersants, should traditionally be in the HLB range of 9-11 [17]. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 3

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Figure 1: A response surface describing the dispersant effectiveness together with isolines showing the HLB values for the surfactant blends. The shaded area indicated blends giving an effectiveness above 50%.

Figure 1 indicate that the traditional use of the HLB value as a guideline for dispersant formulation is not adequate, since a high effectiveness (50-80%) is obtained with surfactant blends covering a large HLB range (4-13). The shaded area in figure 1 shows surfactant blends with an estimated effectiveness above 50%. These surfactant blends have HLB values over a much wider area than the traditional accepted 9-11.

This shows the need for a better approach to describe the interactions in a blend of surfactants to optimise the effectiveness of this blend used as an oil spill dispersant. The traditional optimisation approach based on the HLB concept and experimentation where variables are varied separately is both unnecessarily complicated and time consuming [1]. These univariate approaches will rarely give correct answers, due to the complex synergistic/antagonistic interactions between the surfactants.

This paper shows how a multivariate approach earlier verified by simulation [1], is used to optimise oil spill dispersants. This approach combines the use of experimental design and response surface methods to describe the important interactions in the surfactant blends. The objective for this study was to use the proposed multivariate approach and prove its capability to optimise dispersant formulations. The effectiveness of the new optimised dispersants is verified on different weathered crude oils and a bunker fuel. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 4

MATERIALS AND METHODS

Dispersant effectiveness testing The basic principles for the dispersant effectiveness testing are explained in the first paper in this series [1]. Further details concerning the dispersant effectiveness testing is presented in several earlier publications [18] and [19].

Malvern droplet size analyser In addition to measurement of the mass balance (% of oil dispersed) the volume median diameter (MVD) of the dispersed oil droplets was also measured by a laser particle analyser. An aliquot of the water with dispersed oil was used to determine the mean droplet size distribution. The equipment (Malvern 3600 Ec sizer) and the procedure is further described earlier [19].

Surfactants In this optimisation work several surfactant combinations were tested. All the selected surfactants had a low toxicity and were biodegradable. These included surfactants with rather large structural differences and surfactants with only smaller hydrophilic to lipophilic ratio (HLB) modifications. The exact identity of the surfactants is not revealed due to ongoing patent registration of the developed dispersants. However, non-ionic surfactants like sorbitan esters and ethoxylated sorbitan esters, with different alkyl chains and ethoxylation degree, were used. Anionic surfactants like alkyl sulfosuccinates, with different length and structure of the alkyl chains and different counter ions were also used. The surfactants and solvents used in this study had a purity of 95% or better and were used as supplied.

Oil types and oil weathering The oils used were different North Sea crudes (Statfjord, and Oseberg) together with a medium bunker fuel (IF-30). The oils had previously been artificially weathered to a weathering degree corresponding to 6-12 hours at sea. This involved both evaporation and water-in-oil emulsion formation. The evaporation was performed by distillation and the w/o- formation was performed by mixing oil and water. Further details concerning this standardised procedure for artificial weathering are published earlier [19].

Composition of dispersants In this study the dispersant blended all consisted of three different surfactants. The relative composition of the three surfactants in the dispersants can be visualised in a mixture-triangle. Each position inside this triangle corresponds to a mixture of the three surfactants Xj, x2 and x3. The position in the middle of the triangle corresponds to a dispersant consisting of equal amounts of the three surfactants. Since the effectiveness of a dispersant is dependent on the types of surfactants used and their relative concentrations, the effectiveness can be plotted as a contour plot or a response surface in this triplot. An example of such a response surface is given in figure 2. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 5

Figure 2: Response surfaces showing the effectiveness of the different blends of the surfactants Xj, x2 and x2. The height of the response surface over the triangle illustrates the effectiveness, while the positions in the triangle give the relative composition of the surfactants. The highest point in the response surface represents the optimum composition (point A).

Experimental design and response surface methods used A more detailed description of our approach combined use of mixture design and response surface methods is presented in the first paper in this series, where this approach was tested by simulations [1]. The mixture design used in this work is called a simplex-centroid design [20] and [21] and the experimental points are marked in e.g. figure 3. Both Scheffe [22], [23] and Cox [24] developed canonical polynomial models with constraints on the coefficients which are widely used to analyse data from mixture experiments. The coefficient in the Cox-Scheffe models is in this study estimated with multiple regression (MLR), instead of using the exact analytical approach. Since we were using replicate measurements, the number of measurements would exceed the number of coefficients.

In this study three process variables or design variables (relative amounts of the three surfactants x]( x2 and x3), and two response variables (dispersant effectiveness and mean volume diameter - MVD of the dispersed oil droplets), were measured in the laboratory. The dispersant effectiveness is the percentage of the oil which is dispersed into the water column in the laboratory measured with the IFP-test and should be as high as possible (60-90%). The droplet size (median volume diameter or MVD) of the dispersed droplet should, on the other hand, be as small as possible (10-30 pm) to stay stable in the water column. The experimental data and the response surface techniques were used to estimate the distribution of dispersant effectiveness and droplet size as a function of the composition. The estimated response function was then combined with a "steepest ascent" algorithm [25] to find the optimum composition for the dispersant. An optimum composition should give both high dispersant effectiveness and lowest possible droplet size (MVD) of the dispersed oil. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 6

The response function If the response surface is dependent on the surfactant composition, then there should exist a function of X], x2 and x3, which describes the response surface:

y - f(p0> xt, x2, x3) Equation 1.

This function is called the true response function and is assumed to be a continuous function of X], x2, and x3. |30 is a vector of constants. By using response surface techniques we try to approximate a response function based on the experimental data to this unknown true function [26].

Lack-of-fit in response surfaces The model complexity was selected based on lack-of-fit calculations in a sequential model fitting from simple linear models to special quartic models. The lack-of-fit calculations are based on a comparison of the Residual sum of squares to the sum of squares due to pure error (F-test). For most of the mixture-triangles (a total of 20 were performed) the special quartic model proposed by Cornell [21] showed the best fit. This rather high complexity of the response surface model was necessary due to strong interactions between the surfactants [1],

The estimated response surfaces were only used to study how the composition of the surfactant blend influenced or correlated with the dispersant effectiveness, if the response surface showed a sufficient fit to the experimental data. The lack-of-fit criteria used in this study was that the significance of lack-of-fit tested with a F-test should be less than 5%. This means that the possibility of getting the established response surface just by chance, assuming no correspondence between dispersant composition and effectiveness, should be lower than 5 percent. The fit of the data to the selected model was sufficient for most of the surfactant combinations tested.

RESULTS AND DISCUSSION

Result from several optimisation experiments were this multivariate approach was tested with real laboratory data are presented in this section. Examples are shown from experiments with both water-free and emulsified oils (figures 4a and 7). Examples are also given were different surfactant blends are optimised for the same oil type (figure 3a-b) and experiments were the same surfactant blends are optimised on different oil types (figure 4a-b). Optimisation of two response variables (dispersant effectiveness and dispersed oil droplet size) are also demonstrated (figures 4a and 5). How a region of special interest can be explored with a higher resolution by using a new mixture design in this region is shown in figure 6.

Optimised dispersant for water-free oils This section presents results from the experiments to optimise the effectiveness of oil spill dispersants for water-free oils. The response surfaces (dispersant effectiveness in %) are presented as two dimensional contour maps, were the distance between the contour lines is 10%. The thickness of the contour lines indicates also the steepness of the response surface, where thin and close lines indicate a steep area on the response surface.

Two examples of the response surfaces are shown in figures 3 a-b. The specific identities of the surfactants are not revealed, in this and other figures, due to ongoing patent registration of Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 7 the developed dispersants. The figures show the estimated response surface for a medium bunker fuel (IF-30) with two different surfactant combinations. The most effective surfactant combination can be estimated from the response surface (the highest point). The estimated effectiveness for these optimum compositions were verified by experiments to be true optima.

Figure 3 a-b shows that the two different surfactant systems give very different distribution of effectiveness (the response surface) when used as a dispersant, but both of them have areas with high effectiveness (close to 90%). Both surfactant combinations in figure 3 are promising candidates for a commercial dispersant.

Figure 4 a-b shows the distribution of effectiveness as a function of composition of the same surfactant system on two different oil types. This illustrates a realistic situation where we have to respond to oil spills of different oil types (crude and bunker fuel), but we only want to stock one type of dispersant. Oil spills at sea occur from many sources and, for this reason the oil type spilled will vary. The surfactant combinations in figure 4 giving the highest effectiveness are different for the two oil types (point A and B). However, by studying the response surfaces it is possible to find a single surfactant composition giving a high effectiveness (80-85%) with both oil types (point B). This shows the importance of utilising the information in the response surface to find regions satisfying several criteria. It is not only the optimum point which is of interest, but the distribution of dispersant effectiveness as a function of dispersant composition (x,, x2, x3). Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 8

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Figure 3 a-b: Response surfaces for the same oil (Oseberg crude) with two different surfactant combinations. The estimated optimum points are marked (•) and the measured optimum effectiveness are noted. For each experimental point both measured and estimated effectiveness are given, estimates are in brackets. Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 9

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Figure 4 a-b: Response surfaces for two different oil types; IF-30 bunker fuel (A) and Statfjord crude (B), with the same surfactant combination. The estimated optimum points are marked (a and b) and the measured optimum effectiveness are noted. For each experimental point both the measured and estimated effectiveness are given, estimates are in brackets. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 10

Both the dispersant effectiveness and the droplet size distribution of the dispersed oil was measured during this optimisation work. The surfactants decrease the interfacial tension between water and oil and lowering the energy barrier for creating smaller droplets. This is also favourable in the nature since smaller droplet increases the biodegradation by organisms natural occurring in the sea. Figure 5 shows the response surface for the mean volume diameter (MVD) for the dispersed oil droplets created when the oil is dispersed in the laboratory apparatus. This response variable is also important for the evaluation of the effectiveness of an oil spill dispersant and should be as small as possible, at least below 10 -

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Figure 5: The response surface for the droplet diameter for the dispersed oil droplets. The estimated minima position is marked (•) and the measured minimum diameter is noted. For each experimental point, both the measured and estimated values are given, estimates are in brackets.

Figure 5 can be compared with figure 4a because the same oil and surfactants are used. It can be seen from these two figures that the maximum in dispersant effectiveness (figure 4a) and the minima in droplet size (figure 5) are located to the same region in the triangle. This means that both the two criteria for a successful product can be satisfied in this region of the triangle plot i.e. with the same surfactant composition. Several optimisation experiments were performed including both response variables as illustrated in the figures 4a and 5 and they led to the same conclusions as above.

Other important properties for these surfactant blends can also be plotted together with the response surface describing the effectiveness distribution. Such properties could, for example, be; dispersant viscosity, density, toxicity or even the price of the surfactant and solvent blend. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 11

Response surfaces combined or overlaid with other quality describing variables can be used to select specific regions were several important variables are within acceptable values.

Fine tuning of optimum product composition To determine a more accurate optimum composition for the most promising surfactant combination, a new “high resolution ” design was constructed around the first estimated optimum composition.

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Figure 6: Showing a new design to estimate a response surface with better resolution covering a small area around the optimal surfactant composition. The estimated optimum points are marked (•) and the measured optimum effectiveness are noted. For each experimental point both the measured and estimated effectiveness are given, estimates are in brackets. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 12

In the example shown in figure 6, the first attempt to predict the optimum composition was not successful due to generally low effectiveness in most of the area covered by the first experimental design. The predicted optimum point had a lower effectiveness than a nearby design point and the lack-of-fit was also relatively high. Both this first unsuccessful attempt and the “high resolution ” design used afterwards is shown as an example of this approach, in figure 6.

From figure 6 it can be shown that the estimated optimum point is better described with the second design and the measured effectiveness is higher for the new design with better resolution. For many purposes the accuracy of the first design would probably be sufficient. In our study these new designs were used to estimate response surfaces with better resolution for different North Sea crudes. The aim was to find the best compromise surfactant composition for a broad selection of North Sea crudes.

Optimised dispersant for w/o-emulsions An oil spill at sea will, in most situations rapidly take up water due to the wave action and form a viscous and persistent water-in-oil emulsion. Using dispersant to enhance the natural dispersion of w/o-emulsion is more difficult than with the water-free oil. The surfactants have to break the w/o-emulsion first, and then disperse the water-free oil as droplets into the sea water. There is uncertainty as to whether dispersant optimized for a water-free oil also would also be optimal for a w/o-emulsion.

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Figure 7: Response surface for an emulsified oil (IF-30 150°C with 50% water). The response surface for the corresponding water-free oil is shown in figure 4a. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 13

In order to compare the optimised surfactant composition for a water-free oil and the corresponding w/o-emulsion, similar optimisation experiments were performed for both water-free and emulsified oil. Figure 7 shows the response surface for the corresponding w/o- emulsion (50% water) to the oil in figure 4a. The viscosity of this emulsified oil is relatively high, 5400 cP (measured at a shear rate of 10 s'1), but the effectiveness of the optimised dispersant is the same as for the water-free oil (above 80%). The optimum compositions for the water-free oil and the corresponding emulsion are very similar, and for all practical purposes, the same surfactant blend could be used as a dispersant for both water-free and emulsified oils. Several corresponding water-free oils and emulsions were tested in this manner and they led to the same conclusions as above.

CONCLUSIONS

This work shows the importance of using proper statistical experimental designs combined with multivariate data analysis in product development. An proposed approach from an earlier simulation study [1] has been verified with real laboratory data. In this case this combined approach was used to develop oil spill dispersants consisting of three active constituents. This technique can, however, be easily adapted for more complex products. As the complexity of a product increases, the need for a proper statistical experimental design will increase even more.

This work has shown that the traditional blending rules based on the Hydrophilic-Lipophilic- balance (HLB), stating that a dispersant should have a HLB between 9-11, is not a useful tool in dispersant optimisation. The weakness of the “HLB-concept” is caused by the failure to take into account the strong molecular interactions between the surfactants, not described by this univariate concept.

Not only is the optimum composition determined, but the total distribution of the effectiveness is also described by the response surface. The knowledge of how the effectiveness is distributed helps the experimenter to select a suitable compromise surfactant composition when other variables such as different oil types, dispersant viscosity, density, toxicity or price are also taken into account.

As a part of this study, dispersants have been optimised both for water-free oils and for water- in-oil emulsions. Such emulsions are rapidly formed at sea with most oil types during an oil spill situation. No significant difference in optimum composition for the water-free and emulsified oil was observed. This implies that a dispersant which is optimised for a water-free oil also seems to be optimal for the corresponding w/o-emulsion, which is important from a operational point of view.

Several of the individual results presented in this paper had the potential to be utilised as commercial products, but these results together with others have been combined in a new experimental design as a part of a larger investigation in the next paper in this series [27]. The main objective with the study described in this third paper was to develop an oil spill dispersant with high effectiveness on a broad selection of oil types and oil weathering degree. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 14

ACKNOWLEDGEMENTS

The authors wish to thank the following persons and institutions:

Fina Exploration Norway a.s for funding this study at as a part of a seven year research program at IKU called "Dispersability of weathered oils - a laboratory study - DIWO". Olaf Gram, Fina Exploration Norway, Alain Charlier, Fina Research Brussels, Belgium and Halfdan Akre-Aas, Akre-Aas Miljpkjemi a/s, Norway, for valuable assistance and discussions.

REFERENCES

1. PJ. Brandvik. Statistical simulation as an effective tool to evaluate and illustrate the advantage of experimental designs and response surface methods, Chemometrics and Intelligent Laboratory Systems, (submitted for publication). 2. J. Wieling, J. Schepers, J. Hempenius, C.K. Mensink and Jonkman. Optimization of chromatographic selectivity of twelve sulfonamides in reversed-phase high-performance liquid chromatography using mixture designs and multi-criteria decision making. J. Chromatogr., 545(1), 101-14, (1991). 3. J.L. Glajch, J.J. Kirkland, J.M. Minor. Optimization of selectivity in high-performance liquid chromatography using mixture-design statistical techniques: overview and software for data analysis. J. Liq. Chromatogr., 10(8-9), 1727-47,(1987). 4. P.M.J. Coenegracht, Nguyen Van Tuyen, H.J. Metting, P.J.M CoeneGracht-Lamers. Application of a mixture design technique to the simultaneous optimization of analysis time and resolution in reversed-phase ion-pair liquid chromatography using a minimal resolution plot. J. Chromatogr., 389(2), 351-67, (1987). 5. Wang, Qinsun; Yan, Bingwen,. Computer-assisted optimization of multicomponent solvent selectivity in high-performance thin-layer chromatography using a mixture-design statistical technique, Chromatographia, 28(9-10), 473-6, (1989).

6. GESAMP: Impact Of Oil and Related Chemicals On The Marine Environment. London, International Maritime Organisation, 1993. 7. P.J. Brandvik, P.S. Dating and K. Aareskjold. Chemical dispersability testing of fresh and weathered oils - an extended study with eight oil types, IKU report no: 02.0786.00/12/90, (1990).

8 . C. Bocard, G. Gastaing, J. Ducreux, C. Gatellier, J. Croquette and F. Merlin. Protecmar: The French experience from a seven-year dispersant offshore trials program. Proc. (1987) Oil Spill Conference. Washington, D.C.: API. pp. 225-229. 9. M.F. Fingas, M.A. Bobra and R.K. Velicoga.: Laboratory studies on the chemical and natural dispersability of oil. Proc. (1987) Oil Spill Conference. Washington, D C.: API. pp. 241-246. 10. T. Lunel. Dispersion measurements at the Sea Empress Oil Spill. Procedings of the 19th Arctic and Marine Oilspill Program (AMOP), Environment Canada, Ottawa, (1996). pp 100-110. 11. P.J. Brandvik, 0.0. Knudsen, M.0. Moldestad and P.S: Dating: Laboratory testing of dispersants under Arctic conditions, In: The use of Chemicals in Oil Spill Response. ASTM STP 1252. Peter Lane, Ed., American Society for Testing and Materials, Philadelphia, USA, (1995). 12. US patent no. 5,502,962 (1985) from Exxon 13. US patent no. 469,603, (1985) from Fina 14. MAFF (Ministry of Agriculture, fisheries and Food, UK). Testing, approval and use of Oil Dispersants - Final report of the Governmental Review. MAFF London, UK, (1995). 15. W.C. Griffin. Classification of surface active agents by “HLB”. J. Soc. Cosmetic Chemists, 1,311, (1949). 16. D.J. Shaw. Introduction to colloid and surface chemistry. Butterworth-Heinemann Ltd., fourth edition, reprinted (1994), Oxford, England. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 15

17. R.J. Fiocco, R.R. Lessard, G.P. Canevari, K.W. Becker and P.S. Baling. The Impact of Oil Dispersant Solvent on Performance, In: The use of Chemicals in Oil Spill Response. ASTM STP 1252. Peter Lane, Ed., American Society for Testing and Materials, Philadelphia, USA, (1995). 18. C. Bocard, G. Castaing and C. Gatellier. Chemical oil dispersion in trials at sea and in laboratory tests: the key role of dilution processes. In: Oil Spill Chemical Dispersants: Research. Experience and Recommendations. STP 840, Tom E. Allen, Ed., American Society for Testing and Materials, Philadelphia, pp. 125-142,(1984). 19. P.S. Baling, P.J. Brandvik, D. Mackay, 0. Johansen, Characterisation of crude oils for environmental purposes: Oil & Chemical Pollution 7, 1990/91, pp. 199-224, (1991). 20. A.L. Kurhi, J.A. Cornell. Response surfaces. Designs and analyses . Marcel Dekker Inc. ASQC Quality Press, New York, (1987). 21. J.A. Cornell. Experiments with mixtures. Wiley, New York, 2nd Ed., (1990). 22. H. Scheffe. Experiments with mixtures. J. R. Stat Soc., B, 20, No. 2, 344-360, (1958). 23. H. Scheffe. Simplex-centroid design for experiments with mixtures. J. R. Stat. Soc., B, 25, No. 2, 235-263, (1963). 24. D.R. Cox. A note on polynominal response functions for mixtures, Biometrica, 58(1), 155-159, (1971). 25. G.E.P. Box, W.G. Hunter, J.S. Hunter. Statistics for experimenters. An introduction to design, data analysis and model building. John Wiley & Sons, New York, (1978). 26. H. Martens, T. Naes. Multivariate Calibration. John Wiley and Sons, Chichester, (1989). 27. P.J. Brandvik and P.S. Baling. Optimising oil spill dispersants as a function of oil type and weathering degree - a multivariate approach using partial least squares (PLS), Chemometrics and Intelligent Laboratory Systems, (submitted for publication). (paper 5)

Optimising oil spill dispersants as a function of oil type and weathering degree - a multivariate approach using partial least squares (PLS)

Submitted for review and publication in: Chemometrics and Intelligent Laboratory Systems, November 1996.

Per Johan Brandvik and Per S. Baling IKU Petroleum Research, SINTEF Trondheim, Norway

Dr. thesis Per Johan Brandvik, March 1997 Optimising Oil Spill Dispersants as a Function of Oil Type and Weathering Degree a Multivariate Approach using Partial Least Squares (PLS)

PER JOHAN BRANDVIK and PER S. BALING

IKU Petroleum Research, SINTEF 7043 Trondheim, Norway

ABSTRACT

This is last of three papers concerning multivariate optimisation of oil spill dispersants. Dispersants are used in oil spill response operations to enhance the natural dispersion of an oil slick at sea as small oil droplets in the water column. The first paper in this series propose a multivariate approach for dispersant optimisation based on simulations with different experimental designs. The second paper verifies the usefulness of this approach using real laboratory data. This multivariate approach is based on designed experiments and response surface methods and represents a new approach within dispersant development.

The work described in this third paper shows how the PLS (Partial Least Squares) algorithm can be used to predict optimised dispersant composition as a function of oil type and degree of weathering. This is done by characterisation of the oil type and weathering degree by principal component analysis (PCA). Score values from the first and second principal component are used to select oil type and weathering degree for the calibration samples. Together with selected surfactants are the score values used as parameters for a new 2 5'1 fractional factorial design. The data from this factorial design are used as a calibration set for predicting optimal dispersant composition as a function of oil type and weathering degree.

The experimental design used in this study (simplex-centroid for response surface modelling and fractional factorial design) combined with PLS modelling has made it possible to gain new basic knowledge concerning optimal dispersant composition for different oil types and degrees of weathering.

The final optimised dispersant was verified to have a high effectiveness on a broad selection of oil types and a low toxicity. It had also the highest effectiveness and the lowest toxicity when compared to a selection of commercially available products.

INTRODUCTION This is the last of three papers describing a new approach to optimising oil spill dispersants. The other two papers have been published earlier in this journal [1] and [2]. The first two papers in this series proposed and verified a multivariate approach new within dispersant development based on designed experiments and response surface methods.

In the study described in this paper, the authors have selected surfactants, oil types and weathering degree of the oils as a part of a new fractional factorial design. The results from Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 2 these experiments are used as a calibration set to predict optimised dispersant composition with the PLS algorithm as a function of weathering degree for new oil types.

Dispersants are used in oil spill response operations to enhance the natural dispersion of a surface oil spill as small droplets into the water column. Dispersants consist of different surface active components (surfactants) dissolved in a solvent or blend of solvents. Further background concerning use of dispersants is given in an earlier paper [1], Use of effective aerial application techniques (aircraft and helicopters) makes it possible to apply dispersant rapidly and more efficiently than earlier. Such improved application techniques and more knowledge concerning the net environmental benefit of using dispersants [3], actualise the use of dispersants in many oil spill scenarios.

Building calibration sets where the process variables (design variables) are varied according to an experimental design is an established method. Especially within the area of optimising organic synthesis, where the University of Umea have made several important contributions [4], [5] and [6]. In these applications, sub-spaces e.g. solvent or substrate properties have been explored and characterised by PCA and score values have been used as design variables. Predictions of optimised synthesis conditions, such as with new solvents or substrate types, have then been performed by utilising the properties of the PLS algorithm.

This combination of describing sub-spaces by PCA and utilising the orthogonal properties of the principal components as design variables has not been used earlier in dispersant optimisation. In this study, physical and chemical properties of a large number of artificially weathered oil samples have been used to describe the latent variables “oil type” and “weathering degree ”. Using these two variables, and a descriptor for each of the three different surfactants defining the dispersant composition, a fractional factorial design was used to define the experiments in the calibration set. PLS was then used with this calibration set to predict optimised dispersant composition for other oil types and weathering degrees than those included in the calibration set.

The main objective of this study has been to develop a new dispersant with better performance than existing products, especially on weathered Norwegian crude oils. To fulfil this objective it was necessary to gain further understanding about how the surfactants in a dispersant interact during the dispersion process, and how this was influenced by the chemical composition of the oil.

MATERIALS AND METHODS Dispersant effectiveness testing Most of the effectiveness testing of the new dispersant blends was performed using the official French effectiveness test (the IFP method). The final verification of the optimised product was performed with the Warren Spring Laboratory test (the WSL method), the official test method for governmental approval in UK and Norway. All experiments were performed in a climate room at 13°C and with sea water salinity of 3.5%. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 3

The IFP method The basic principle for this IFP dispersant effectiveness test is described in the first paper in this series [1] and further details are presented in several earlier publications [7] and [8].

The WSL method The WSL test is performed with oil, sea water and dispersant in small separation funnels (250 ml with sea water). Oil (5g) is applied onto the water and dispersant (250 fig or 5% compared to the amount of oil) is added. The mixing energy is supplied by rotating the flask for 2 minutes, which gives a relatively higher energy input compared to the IFP test. The mixing period is followed by a settling period of 1 minute before sampling. The amount of oil dispersed into the water, is quantified by extracting the oil from the sample taken after 1 minute. The effectiveness is calculated as the percentage of oil removed from the surface. Further details concerning the dispersant effectiveness testing is presented in earlier publications [8] and [9].

Malvern droplet size analyser In addition to measure the mass balance (% of oil dispersed) the median volume diameter (MVD) of the dispersed oil-in-water emulsion was also measured by a laser particle analyser. The equipment (Malvern 3600 Ec sizer) and the procedure is further described earlier [8],

Surfactants In this optimisation work, several surfactant combinations were tested. Description of their basic structure and properties are given in the second paper in this series [2].

Oil types The oils used in this study were the North Sea crudes; Statfjord, Oseberg and Gullfaks together with a medium bunker fuel (IF-30). In addition, data from many other crude oils were used in the multivariate characterisation of the oil properties. These data are taken from earlier studies and are presented there [11], [12], [13], [14] and [15].

Weathering of oils The oils had previously been artificially weathered to a degree corresponding from 6-12 hours up to one week at sea. The different weathering degrees and their abbreviations used in this paper are; 1. Fresh oil (Fresh) 2. Slightly weathered oil - corresponding to approx. 6-12 hours at sea (150°C+) 3. Moderately weathered oil - corresponding to approx. 1 day at sea (200°C+) 4. Heavily weathered oil - corresponding to approx. 5-7 days at sea (250°C+) 5. Photo-oxidised oil - corresponding to approx. 5-7 hours at sea (ph.ox.)

Water-in-oil emulsions was also made from some of these weathered residues. Further details concerning the procedure for artificial weathering are published elsewhere [8],

Experimental design and response surface methods used A description of our combined use of mixture design and response surface methods is given in the first paper in this series, where this approach was tested by simulations [1]. This approach was also tested and verified on experimental data in the second paper in this series [2] and further details can be found there. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 4

In this study, three design variables (relative amounts of the three surfactants xh x2 and x3) were used to define the experiments, and two response variables (dispersant effectiveness and median volume diameter - MVD of the dispersed oil droplets), were measured in the laboratory. The dispersant effectiveness is the percentage of the oil which is dispersed into the water column in the laboratory (IFP method) and should be as high as possible (70-90%). The droplet size (MVD) of the dispersed droplet should, on the other hand, be as small as possible (10-30 pm).

Principal component analysis (PCA) Principal component analysis (PCA) is in its basic form a transformation of a set of variables into a new set of variables which are uncorrelated with each other. This transformation often implies that a large number of correlated variables are transformed to a small number of uncorrelated variables which describes the most important trends in the original data material. These new variables, called eigenvalues or principal components, often account for a large number of the total variance in the original data material. The statistical basis behind PCA is available in several text books e.g. [16] and from more basic tutorial publications e.g. [17]. PCA is well established as a method for data analysis and will not be further discussed or explained here. The software used in this study for the PCA and PLS work were SIRIUS and Unscrambler.

Partial least square regression (PLS) The uncorrelated principal components from the PCA analysis can be used as new variables in regression analysis against a corresponding y variable. New values for y may then be predicted from this calibration set. This approach is known as Principal Component Regression or PCR and is well described in the literature [16].

However, PCR was not used in our case since we have several y variables, and separate models for each y variable had to be established with PCR. We also wanted to utilise the correlation structure in the y variables during the calculation of the PLS component for the X matrix and for this purpose we used the PLS algorithm. PLS uses the covariance matrix from Y to find structures in X which correlate with Y. Since PLS often extracts fewer and more relevant components compared to PCR, PLS components often are easier to interpret and give better predictions.

The basic mathematics behind the PLS algorithm are described in text books [16] and several excellent tutorial publications [18] or [19]. PLS is well established as a method for data analysis and will not be further discussed or explained here.

RESULTS AND DISCUSSIONS Selection of surfactants The molecular interactions between different surfactants in a blend can be very complex [20]. The mechanism of the surfactants is to orient themselves on the interface between oil and water and lower the interfacial tension. The lowest interfacial tension is measured when the packing of the surfactant molecules at the interface is most effective. By using surfactants with different molecular structure and size, the packing efficiency of the surfactant can be increased. For this reason the ability of the experimental design to describe the interactions between the design variables (the surfactants) is very important [1] and [2], Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 5

In the initial phase of this study we tried to characterise the surfactant properties by a multivariate approach. Two different data sets were analysed. The first consisted of surfactant physical properties, such as molecular weight, cloud point, saponification point, HLB, density etc. PCA analysis of this data set showed that most of the variation in these variables was explained in a single principal component (84%) and that this component had a very high correlation coefficient with the HLB value (r=0.79). The second attempt to obtain a multivariate characterisation of the properties of the individual surfactants was to perform a PCA of IR spectra of 49 surfactants. Chemical interpretation of the principal components revealed that the main latent variables, did not describe surfactant properties relevant for this application. Relevant information for this application were surfactant properties, such as effective molecular size in solution, length and shape of alkyl chains, total ethoxylation degree and shape and length of ethoxy-chains and general “polarity ”.

The approach used in this study to select surfactants was based on the more traditional philosophy that the dispersant should consist of three surfactants; one with high HLB (SI), a second with low HLB (S2) and the third should be an anionic “wetting agent ” (S3). The exact identity of the surfactant is not revealed in this paper, due to ongoing patent registration.

High HLB non-ionic surfactant (SI): These surfactants consisted of a ethoxylated sorbitan ester, with fatty acid side chains. The ethoxylation results in a surfactant with a relatively high HLB value (HLB=9 to 15). In the factorial design presented later in this study two different surfactants were selected from this group with a HLB values of 11 and 15. The difference between the two surfactants selected (SIa and Sib) is the type and number of fatty acid chains in the sorbitan ester. The ethoxylation degree of the surfactants is kept constant.

Low HLB non-ionic surfactant (S2): These surfactants consisted of a sorbitan ester with fatty acid side chains without ethoxy groups added to the molecule. The alkyl chains in the fatty acids results in a surfactant with a relatively low HLB values (HLB=l-5). In the factorial design presented later in this study two different surfactants were selected from this group with a HLB values of 1.8 and 4.3. The difference between the two surfactants selected (S2a and S2b) is the type and number of fatty acid chains in the sorbitan ester.

Anionic surfactant (S3): These surfactants consisted of sulfosuccinates salts. The structure and length of the alkyl groups of these sulfosuccinates were varied. HLB values are preferably used for non-ionic surfactants, but the literature also quote HLB values for anionic surfactants [10]. The HLB values of these surfactants are estimated to be lower than usual for anionic surfactants, due to their relatively low ionic nature caused by the strongly conjugated ionic charge between the carbonyl and sulphur double bounds in the sulfosuccinates. The difference between the two surfactants selected from this group (S3a and S3b) is the length and structure of the two alkyl chains. HLB values of 8 and 10 were estimated for the two surfactants selected from this group. However, the exact HLB values are of minor interest since they are used as a binary variable (-, +) in the factorial design and HLB is not used for predictions in a later stage.

Selection of solvents The primary function of the solvent of a dispersant is to transport the surfactants into the oil, but in some cases the solvents may also have surface active properties. To test if different Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 6 types of solvent influenced the optimum surfactant composition, three solvents were tested with the same surfactant combinations. These selected solvents were different with respect to relevant properties e.g. polarity, viscosity and density. The three solvents were decaline, 2-ethylene-glycol-mono-buthyleter and 2-ethyl-hexyl-acetate. A simplex-centroid design was used, and the response surfaces describing the effectiveness as a function of surfactant composition for the three different solvents were estimated. The oil used in these experiments was IF-30 Bunker Fuel (150°C+). The response surfaces and the estimated optimum compositions are given in Figure la-c.

Figure la-c: Response surface describing dispersant effectiveness as a function of surfactant composition with three different solvents: a: 2-ethylene-glycol- mono-buthyleter, b: decaline and c: 2-ethyl-hexyl-acetate.

Figures la-c show that the differences in the optimum composition are quite small compared to the rather large differences in the physical/chemical properties of the solvents. The predicted optimum composition and effectiveness for the same surfactants in these three Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 1

solvents are quite similar. For our purpose, the influence from the solvent on the optimum composition was negligible. The testing of the different oils and surfactants was therefore performed with the same solvent (2-ethyl-hexyl-acetate) and the final solvent blend was be optimised afterwards. Another solvent, with inherent surface active properties, would probably interact more with the surfactants in the dispersant. In such a case, the compositions of both the solvent system and surfactants have to be optimised simultaneously.

Selection of oils The aim was to develop a dispersant with a high effectiveness on a wide range of oils and it was important to use several types of oils in this optimisation study. Ideally, a wide variety of both crude oils and bunker fuels should be used, but how should these oil types be selected and how many are needed to give a sufficient span in oil properties?

Our approach was to select oil types systematically so that the results obtained, with a limited number of oils, would be applicable to other oil types. To cover such a wide range of oil types and weathering degrees (topping and photo oxidation), nine physical and chemical variables were used for the principal component analysis. The objects used in the analysis were 55 oil samples, consisting of 11 different oil types with 5 different degrees of weathering.

Table 1: The nine physical and chemical variables used in the principal component analysis. Abbreviations used in the heading are explained at the end of the table.

Oil type Sat Aro Res Asph Wax Vise Interf Pourp Dens ANS 28.7 33.0 34.1 3.8 3.0 27 21.5 -30 0.884 ANS150 25.1 28.8 41.1 4.5 3.6 110 24.9 0 0.919 ANS200 23.7 27.3 43.8 4.8 4.6 290 26.0 3 0.930 ANS250 20.0 23.0 51.0 5.6 4.4 850 30.9 9 0.947 ANSoks 26.0 20.0 49.0 5.0 3.4 2140 1.5 12 0.942 ArH 25.2 53.7 13.5 7.1 4.7 41 20.0 -28 0.887 ArHISO 24.3 51.8 15.3 8.1 5.3 240 14.0 -23 0.920 ArH200 23.6 50.4 16.6 8.8 5.7 700 18.0 -18 0.935 ArH250 23.0 49.0 18.0 9.5 6.2 2300 17.0 -5 0.951 ArHoks 24.0 41.0 24.0 11.2 6.2 4400 1.1 -8 0.954 BB 53.1 30.5 15.7 1.0 3.6 6 20.7 6 0.839 BB150 50.5 29.0 19.6 1.3 4.5 29 22.0 18 0.876 BB200 48.6 28.0 22.5 1.4 6.4 100 22.5 24 0.893 BB250 47.0 27.0 25.0 1.6 5.7 440 25.5 24 0.903 BBoks 46.0 28.0 25.0 1.4 5.2 550 2.0 27 0.900 DUG 47.2 39.5 11.9 1.2 2.1 7 18.0 -30 0.894 DUC150 45.7 38.3 14.4 1.4 2.5 35 18.0 -18 0.887 DUC200 44.4 32.7 16.8 1.7 3.5 92 21.0 0 0.901 DUC250 43.0 36.0 19.0 1.9 3.3 280 22.0 9 0.913 DUCoks 37.0 24.0 36.0 2.4 3.8 220 2.1 6 0.914 Gull 39.8 54.1 5.2 0.60 1.6 20 13.0 -40 0.882 GulllSO 39.6 53.8 5.6 0.65 1.7 33 13.0 -30 0.893 Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 8

Oil type Sat Aro Res Asph Wax Vise Interf Pourp Dens Gull200 39.3 53.5 6.2 0.7 1.9 72 15.0 -9 0.905 Gull250 39.0 53.0 7.0 0.8 2.1 241 17.0 0 0.914 Gulloks 40.0 43.0 16.0 1.8 2.1 282 1.1 -9 0.916 Hei 39.1 41.3 18.7 0.60 0.90 19 13.0 -50 0.883 HeilSO 38.1 40.3 20.7 0.66 1.0 48 15.0 -40 0.904 Hei200 37.3 39.4 22.3 0.7 1.2 84 15.0 -30 0.914 Hei250 36.0 38.0 25.0 0.8 1.2 200 15.0 -21 0.924 Heioks 41.0 39.0 19.0 1.6 1.3 230 6.7 -9 0.927 IF-30 19.3 64.2 10.9 5.0 2.5 240 26.0 -6 0.936 IF150 19.3 64.2 10.9 5.0 2.5 240 26.0 6 0.936 IF200 19.3 64.0 11.1 5.1 2.5 310 26.0 0 0.941 IF250 19.0 63.0 12.0 5.5 2.7 970 30.0 6 0.954 IFoks 21.0 51.0 23.0 5.9 3.2 1000 3.6 9 0.950 Mur 57.6 24.3 17.5 0.4 3.6 5 14.8 -24 0.829 MurlSO 53.4 22.5 23.3 0.5 4.8 23 20.4 9 0.869 Mur200 50.5 21.3 27.3 0.6 7.5 79 19.4 27 0.887 Mur250 45.0 19.0 35.0 0.8 7.2 290 24.7 21 0.897 Muroks 57.0 18.0 25.0 0.9 6.1 350 1.6 18 0.889 Oseb 49.3 38.6 10.0 1.7 3.4 14 23.0 -9 0.857 OseblSO 47.8 37.4 12.2 2.1 4.1 43 24.0 12 0.887 Oseb200 46.5 36.6 14.1 2.5 5.8 130 24.0 18 0.903 Oseb250 46.0 36.0 15.0 2.6 5.0 190 24.0 18 0.909 Oseboks 47.0 34.0 17.0 2.1 4.8 360 3.7 21 0.908 Stat 51.4 44.2 3.6 1.4 4.2 7 23.0 0 0.834 StatlSO 50.9 43.8 4.3 1.6 4.8 20 16.0 21 0.867 Stat200 50.5 43.5 4.9 1.8 5.8 57 15.0 24 0.882 Stat250 50.0 43.0 5.8 2.2 6.7 220 16.0 27 0.895 Statoks 51.0 31.0 16.0 2.2 6.7 310 1.0 27 0.896 SB 53.9 36.9 8.4 0.91 3.5 10 23.0 -3 0.847 SB 150 53.0 36.3 9.8 1.0 4.1 25 23.0 6 0.877 SB200 52.2 35.8 11.1 1.2 5.4 65 23.0 15 0.892 SB250 51.0 35.0 13.0 1.4 5.4 350 25.0 18 0.907 SBoks 55.0 24.0 20.0 1.0 4.4 320 3.2 21 0.907

Abbreviations and units used in table 1 Variable name Unit Abbreviation saturates wt. % Sat aromatics wt. % Aro resins wt. % Res asphaltens wt. % Asph wax wt. % Wax pour point °C Poup specific density kg/1 Grav interfacial tension mN/m Interf viscosity cP Vise Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 9

The different oil types used were, (abbreviations used in figure 2a in brackets): Alaska North Slope (ANS), Arabian Heavy (ArabH), Brent blend (BB), DUG (DUG), Gullfaks (Gull), Heidrun (Hei), EF-30, Murban (Mur), Oseberg (Oseb), Statfjord (Stat) and Sture blend (SB). The abbreviations used for the weathering degree in e.g. figure 2a (fresh, 1500°C+ etc.) are explained earlier in this paper (Materials and Methods).

The principal component analysis gives a description of the main variance in their physical/chemical data set, suppressing the random noise. A plot of the scores and loadings of the first and second principal component from this data is given in figure 2a and b.

*taft50 • Os9bf5§UC200

•" B

■ Me#

•S..

•w“

• Res

•Poi. T

Resuttg, X-ex&t: 28%.23%

Figure 2a-b: Score (a) and loading plots (b) for the 1st and 2nd principal component explaining respectively 28 and 23% of the total variance, from the characterisation of the different oil types. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 10

Figure 2a visualises the main trends or latent variables in the matrix of chemical/physical variables. Along the horizontal axes (first component describing 28% of the total variance), the “light ” oils (e.g. Murban, Statfjord) are located on the left while the heavier oils (e.g. IF-30 and Arabian Heavy) are located on the right. Along the vertical axes (second component describing 23% of the total variance), the fractions are ordered according to their degree of weathering.

The influence of the chemical/physical variables, influence on the distribution of the objects is shown in the loading plot in Figure 2b. The physical/chemical variables, which are located far out on the axes are most important for the "effect" or latent variables described by the actual principal component, and variables located on opposite sides of the origo are negatively correlated. Variables located close to each other are positively correlated. The variables important for the first latent variable “oil type ” are the content of saturates (Sat), in relation to the asphaltene and resin content, viscosity and density. This means that the samples located on the left side are regarded as light oils with e.g. a high content of saturates, low content of resins and asphaltenes and with a low density. The variables which are important for the second component or the latent variable “weathering ” are variables like aromatics, wax and viscosity. The objects with a low degree of weathering (low on the second component) have a low viscosity while the objects positioned high on this axis have a higher viscosity.

To cover a wide range of oil types and degrees of weathering with as few oils as possible, four oils were selected from Figure 2a. These four oils are marked with circles in the figure. The four selected oils should be representative for the two properties or latent variables we want to study; the "oil type" and the "degree of weathering". A "light" oil type was represented by the Statfjord or Oseberg crude oil while "heavy" oil type was represented by the bunker fuel IF- 30. A "low" degree of weathering was represented by 150°C+ fractions while a "high" degree of weathering was represented by the photo oxidised fraction. This selection of only four oils to describe how "oil type" and "weathering" affects the optimal composition of dispersants, involves several simplifications, but should be adequate as a first simplified approach to this complex problem.

Another four oil samples could have been selected from figure 2a to cover a larger span, but the selection is also based on more "operational" considerations e.g. pour point problems (due to high wax content) with some of the oils (Statfjord, Ekofisk), availability, batch to batch variations etc. The four oils selected represent a sub-sample set of the total 55 oil samples and span a large portion of the total variance on the two axes; "oil type" and "degree of weathering" in Figure 2a.

Experimental design Since we decided to perform all the optimisation experiments with the same solvent, we only needed to select an oil (of a specific oil type and weathering degree), and a surfactant from each of the three surfactant groups (SI, S2 and S3), to perform the 10 experiments in the simplex- centroid design.

If we decided to run simplex lattice designs with all four oils and all six surfactants this would give 32 "triangles". To avoid such a large number of tests we took advantage of the factorial structure in the selection of oils and surfactants and used a half-factorial design [21]. This half factorial design give 25"1 = 16 "triangles ”. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 11

The five design variables which describes the oil and the surfactants are:

Variables Description Coding Selections

1 Oil tvne CPC1): - "Light” oil type (-) (Statfjord or Oseberg crude) - "Heavy ” oil type (+) (IF-30 Bunker fuel) 2 Weathering (PC2): - Low weathering degree (-) (IF-30 150°C+ and Statfjord 150C+) - High weathering degree (+) (IF-30 ph.ox. and Oseberg ph.ox.) 3 Surfactant 1 fSl): Ethoxylated - long alkyl chain (HLB=11) (+) Surfactant Sla sorbitan esters - short alkyl chain (HLB=15) (-) Surfactant Sib

4 Surfactant 2 (S2): Sorbitan esters - long alkyl chain (HLB=1.8) (+) Surfactant S2a - short alkyl chain (HLB=4.3) (-) Surfactant S2b

5 Surfactant 3 ('S3'): - long and straight Anionic “wetting alkyl chain (“low ” HLB) (-) Surfactant S3a agent ” - branched and short alkyl chain (“high ” HLB) (+) Surfactant S3b

The two possibilities for each variables are coded for simplicity e.g. for oil weathering degree, low weathering degree is coded and high To select the 16 of the total 32 possible "triangle" combinations of the 5 design variables, a half-fractional factorial design was used [21]. The 16 "triangle" designs carried out are given in table 2.

Each of the 16 "triangle"-designs contain 10 experimental points. With 2 parallel IFF measurements in each point and a verification measurement of the predicted optimum point, this design gave approximately 25-28 IFF tests for each "triangle" design. The total number of tests required for the 16 "triangle"-designs described in the above table would be approximately 450 IFF measurements. To avoid systematic errors occuring in the data both the sequence of the 16 "triangles" and the internal sequence of the IFP-measurements in each "triangle" were randomised [21].

Results from the 25'1 half fractional design The results from the sixteen different combinations of the design variables are presented in table 2.

Two examples of response surface describing dispersant effectiveness as a function of surfactant composition from the 16 individual simplex-centroid designs in the total fractional factorial design are given in figure 3a-b. Both the response surfaces in figure 3 shows that the predicted values have a good fit to the measured values. Both the predicted optimum compositions were verified to be true optima by performing effectiveness testing of the predicted formulations. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 12

Figure 3a-b: Two examples of 16 individual response surfaces in the fractional factorial design. If-30 bunker fuel (photo oxidised and 150°C+ fraction) with two different surfactant combinations. Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 13

Table 2: The oil type (PC-1 and PC-2) and surfactant combination (SI, S2 and S3) used in the sixteen simplex-centroid designs, together with the predicted optimum composition (x„ x2, x3) and the measured optimum effectiveness.

Description of the simplex-centroid experiments Results from experiments Exper Oil properties Surfactant HLB IFF Optimised composition no PC-1 PC-2 SI S2 S3 % x, X, x. i -1.750 0.400 15 4.3 10 85 24.0 0.0 36.0 2 3.560 -0.876 15 4.3 10 90 20.0 20.0 20.0 3 -1.750 0.400 15 1.8 8 40 28.0 4.3 26.8 4 3.560 -0.876 15 1.8 8 69 30.0 13.2 16.8 5 -0.493 -1.180 11 1.8 8 33 36.0 24.0 0.0 6 2.710 1.560 11 1.8 8 58 34.8 25.2 0.0 7 -1.750 0.400 11 4.3 8 56 60.0 0.0 0.0 8 3.560 -0.876 11 4.3 8 69 48.0 6.0 6.0 9 -0.493 -1.180 15 1.8 10 88 13.9 26.9 19.2 10 2.710 1.560 15 1.8 10 82 12.7 26.3 21.0 11 -0.493 -1.180 15 4.3 8 32 34.0 13.0 13.0 12 2.710 1.560 15 4.3 8 62 32.4 0.0 27.6 13 -1.750 0.400 11 1.8 10 70 34.3 9.2 16.5 14 3.560 -0.876 11 1.8 10 88 29.8 18.1 12.1 15 -0.493 -1.180 11 4.3 10 80 45.2 2.4 12.4 16 2.710 1.560 11 4.3 10 84 39.9 7.7 12.4

Optimal dispersant composition versus oil type and weathering degree The effects of the design variables on the effectiveness (IFP%) and the composition of the optimised dispersant (x„ x2, x3) are presented in a normal probability plot, see figure 4a-c. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 14

Legend: A: PCI B: PC2 C: SI D: S2 E: S3

Figure 4a-c: Normal probability plot indicating the significance of the effects of the design variables PCI, PC2 SI, S2 and S3 (A-E) on the optimised dispersant composition x„ x2 and x3 (4a-c) and the measured effectiveness, IFP% (4d). Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 15

Figure 4a-c gives valuable information concerning the importance of the different design variables (oil type, weathering degree and surfactant composition) and their cross terms in predicting the dispersant effectiveness (IFP%) and the composition of the optimised dispersant (x,, x2, x3). The largest influence on the effectiveness IFP% (see figure 4d) is caused by surfactant S3 (E), this can also be seen in table 2, where the blends using surfactant S3a generally have a higher effectiveness than the blends containing S3b. Other important effects on the effectiveness (figure 4d) are the oil type PCI (A) and the interactions between oil type and PCI and surfactant S3 (AE). Several other interaction terms are also probably significant AB, BD, BE and CD.

The composition of the optimised dispersant (x,, x2, x3) was strongly influenced by the oil type PCI (A) and weathering degree PC2 (B). The effect of the oil properties is largest for surfactant x2, see figure 4b, were both the main factors PCI (A) and PC2 (B) and the interaction (AB) are significant. Also interactions between the oil properties and the surfactants are significant; PC1*S3 (AE) and PC2*S 1 (BC) and interactions between the surfactants; S1*S3 (CE). Significant effects were also identified for the other design variables, see figure 4a and c.

We wanted to utilise more of the information in the data set in table 2, and primarily our interest was focused on the composition of the optimised formulations and not the measured effectiveness itself. We wanted to study the variation in optimised composition (x,, x2, x3) as a function of oil type and weathering degree (PCI, PC2) with the same surfactants as used in table 2 (SI, S2 and S3). The data generated by these designed experiments (table 2), was used as input to establish and calibrate a multivariate regression model by using the PLS algorithm. PLS was used to utilise the correlation structure within the Y matrix when decomposing the X matrix.

Validation of designed experiments without replicate measurements can not be performed with cross validation. Removing experiments from the calibration set will in most cases destabilise the model and cross validation errors will be high. The normal probability plots in figure 4 were used instead to estimate the significance of the effects. Similar probability plots, as figure 4, were also produced for the regression coefficients and similar conclusions were found. Leverage correction was also carefully used to estimate prediction errors during the validation and prediction stage [16].

Since the effects from the design variables were different on the three responses describing the optimised dispersant (x„ x2, x3), all main and second order interactions were used for further modelling. Since PLS can handle a higher number of variables than samples also the interaction term S1*S2*S3 and PC22 were included since they had high loadings on the PLS components. This gave a total of 18 variables in the X-matrix. In this way the five original variables in the X-matrix were extended with cross terms to describe the important interaction between the surfactants and the variables describing the oil type and weathering degree. A model using these 18 x variables, consisting of 3 PLS components gave the lowest Root Mean Square Error of Prediction (RMSEP) for the three y variables and were used for the predictions [16]. This model explained 61% of the variance for the X-matrix and 71% for the Y matrix. The Y matrix consisted of the composition of the optimised dispersant (x„ x2, x3).

The predictions with the established PLS model were performed for the following oil types: Oseberg, Statfjord, Gullfaks, Murban, Arabian Heavy, Alaska North Slope and IF-30 and for Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 16 the following degrees of weathering: fresh oil, 150°C+, 200°C+, 250°C+ and ph.ox. Examples of these predictions, with the four most promising surfactant combinations, are given in figures 5a-d.

S1a/S1a v Sla/Sla

S1a, S2b,S3b S1a, S2a, S3b Sib.S2b.S3b S1b.S2a.S3b

Figure 5a-d: Predicted optimum surfactant compositions for different weathering degrees of Statfjord (A), Gullfaks (B), Arabian Heavy (C) and 1F-30 Bunker fuel (D). Surfactant combinations are given in the legend.

Some of the formulations in figure 5a-d were expected to give low effectiveness (e.g. Statfjord 250°C+ and ph.ox.), due to the high pour point of these oils caused by the high wax content. The high wax content causes the oils to be solid or semi-solid at the test temperature, which reduces their dispersability in the laboratory apparatus [22]. Oils with high pour point were, as earlier mentioned, avoided when selecting the calibration set and this effect was not built into the model.

The main trend in figures 5a-d is that the optimum dispersant compositions for lighter Norwegian crudes (Statfjord and Gullfaks) are more to the right in the "triangle"-plots (higher Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 17

HLB values), than for the heavier Arabian Heavy crude oil and IF-30 bunker fuel. Figure 5a-d also reveals the trend that optimum dispersant composition, within the same oil type, is shifted to the left (lower HLB) with increased oil weathering. These findings give valuable information concerning dispersant formulation for treating weathered oil slicks at sea.

The predictions presented in figures 5a-d, were made with four of the most promising surfactant combinations from the calibration set, but included new oil types. These predictions give important information for developing dispersants with high effectiveness on a broad selection of weathered oils. The uncertainty in the estimated optimum dispersant composition is dependant on the fit of the new oil types to the established PLS model. The uncertainty is lowest for the oils with the best fit to the model. Statfjord, Gullfaks and IF-30 had a good fit to the model and a low estimated uncertainty (2-5 %), because they were similar to the four oil samples used in the calibration set (see figure 2a). Arabian Heavy, on the other hand had a lower fit to the model and a larger estimated uncertainty (4-15%), because they are less similar to the oils used for calibration. The most weathered fraction of this oil (250°C+ and photo oxidated fraction) were classified as outliers. The uncertainty in these predictions was larger (10-15 %) and these results should be interpreted with caution.

S1b

Oseberg Heidrun Gullfaks Statfjord

Brent Blend

Arabian heavy

Alaskgn North Slop! Murban

adm4100/tegner/pjb/dr-grad/fig-9.ai

Figure 6: Predicted optimum composition for the same weathering degree (250CC fraction) for 10 different oils (Gullfaks, Heidrun, DUC, Oseberg, Statfjord, Brent Blend, Arabian Heavy, Alaska North Slope, Murban and IF-30 Bunker fuel).

Estimated optimum dispersant composition for the same weathering degree (250 °C+) of different oil types are given in figure 6. It can be seen that the difference between the light Norwegian crude oils (Gullfaks, Heidrun, Oseberg and Statfjord) is relatively small. The differences in dispersant composition are larger between the light crudes and the more heavier crudes (Arabian Heavy, Alaska North Slope and Murban). Brent blend and the bunker fuel Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 18

IF-30 have an intermediate position between those two groups. These observations are valuable information for formulating dispersants with high effectiveness on a broad selection of oil types. The estimated uncertainty in the optimum composition, based on distance from the PLS model, for the 250 °C+ fractions in figure 9 varied from 2-10 %.

Validation of the predictions by experimentation Several oil types and their corresponding w/o-emulsions were selected to validate the predictions from the multivariate model. The predicted optimised dispersants were blended and the dispersability were measured by the IFF test. The results for one of these oils are presented in figure 7. These measurements were performed with the 200°C+ residue of the Gullfaks crude and the corresponding 75% water-in-oil emulsion. The emulsions had viscosities of 4000 cP (measured at a shear rate of 10 s'1). One commercial product, Finasol OSR-5, was also tested for comparison.

□ Gullfaks waterfree oil □ Gullfaks emulsion

S1b S2b S3a Sla S2b S3a S1bS2a S3a Sla S2a S3a Finasol OSR-5 Predicted optimised surfactant blends

Figure 7: Measured effectiveness for the predicted optimum dispersants with Gullfaks (200CC+) and the corresponding 75% w/o-emulsion.

Figure 7 shows the effectiveness of the four most promising surfactant combinations predicted from the multivariate model for the Gullfaks oil and the corresponding 75% emulsion. This oil type was not included in the calibration set and all four surfactant formulations have a high effectiveness, 75 to 89% on water-free oil. Surfactant combination number two (Sib, S2a and S3a) has, in addition, the highest effectiveness on the emulsion. Figure 7 show that the predicted formulations have higher effectiveness than the commercial product Finasol OSR-5 and that the multivariate model predicts potential formulations for new dispersants as a function of oil type and weathering degree. Submitted for publication in Chemometricsand Intelligent Laboratory Systems, November 1996 - page 19

Final effectiveness testing This new basic knowledge concerning different oil types and weathering degree and their influence on oil spill dispersant formulation (figures 5a-d and 6), was used to formulate a new dispersant with generally high effectiveness on a broad selection of oil types.

We also determined more accurate optimum compositions for the most promising surfactant combinations, by constructing new “high resolution ” designs around the first estimated optimum compositions. These experiments were performed for some of the oil types to find the best compromise formulation for both light and heavy crudes and bunker fuels. Examples of these experiments are presented in the second paper in this series of three papers [2],

In the final dispersant optimisation several different types and blends of solvents were tested, both with regard to effectiveness but also with regard to dispersant viscosity and density at temperatures down to -15°C. The total surfactant content compared to solvent was varied to give the best compromise between effectiveness and content of active material. Other criteria taken into account were availability of the components (surfactants and solvents), toxicity, bioacummulation, biodegradation and price of the final product. The final optimised product called IKU-9 was formulated to give a good performance on a broad selection of weathered North Sea crude oils and for medium bunker fuels like the IF-30. To compare the effectiveness of the optimised dispersant with other commercially available products, effectiveness was tested with the Warren Spring laboratory apparatus (WSL), which is used for government approval of dispersants both in the UK and Norway. The results from this final verification are presented in figure 8.

0 am\ \ \ \ ' \ \ \ \ \ \ \ \

Figure 8: Effectiveness (%) and mean droplet size (pm) for the optimised dispersant (IKU-9) and some commercially available dispersants. Test oil is Stafford I50CC+ and the tests were performed with the WSL method. Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 20

Figure 8 shows that, compared to this collection of commercial dispersants, the newly developed dispersant (IKU-9), has both the highest effectiveness and formed the smallest oil droplets of the dispersed oil. In addition to dispersant effectiveness, the toxicity of the final product, is the most important criterion for governmental approval of dispersants. The toxicity of IKU-9 and a selection of other commercial available dispersants are given in figure 9.

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Corexlt Enersperse Flnasol Dasic Dispolene Shell IKU-9 9527 1037 OSR-5 Slickgone 36S Dispersant LTS VDC

Figure 9: Toxicity (ECso)for the salt water algae Skeletonema Constatum (ISO/DP 10253) for the new dispersant IKU-9 and a selection of commercial dispersants.

The toxicity of the new product IKU-9 was low giving a high EC5q value, compared to other dispersants. The EC50 for the salt water algae Skeletonema Constatum was 250 ppm after 96 hours exposure. The EC50 value is the dispersant concentration which retards the growth of the algae population by 50% over 96 hours. This value should be as high as possible. Further details concerning the toxicity testing are given by Kallqvist [23]. CONCLUSIONS The experimental design used in this study (simplex-centroid for response surface modelling and fractional factorial design) has made it possible to gain new basic knowledge concerning optimal dispersant composition for different oil types and degrees of weathering.

The data from these experiments was used to calibrate a PLS model which was used to estimate optimal dispersant composition for a broad selection of oil types and weathering degrees. The degree of fit for the new oil types to the PLS model was also determined, Submitted for publication in Chemometrics and Intelligent Laboratory Systems, November 1996 - page 21 together with an estimate of the uncertainty of the predictions. Some of these predictions were also verified by experimentation.

The final optimised dispersant was verified to have a high effectiveness (80-90%) on a broad selection of oil types and a low toxicity (EC50 > 250 ppm). It had also the highest effectiveness when compared to a selection of commercial available products.

ACKNOWLEDGEMENTS The authors wish to thank the following persons and institutions: Fina Exploration Norway a.s for funding this study at as a part of a seven year research program at IKU called "Dispersability of weathered oils - a laboratory study - DIWO". Olaf Gram, Fina Exploration Norway, Alain Charlier, Fina Research Brussels, Belgium and Halfdan Akre-Aas, Akre-Aas Miljpkjemi a/s, Norway, for valuable assistance and discussions. REFERENCES

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