Sensor-Based Online Control of Pickling Lines

The main aim of the project was the development of a new sensor-based control tech- KI-NA-23872-EN-S nology for operating pickling lines. Pickling is the most important step to remove surface scale layers and is strongly dependent on the exactly controlled pickling liquor composition. When the project started, there was no feasible system available for online control of pickling lines. Within this project, new methods for online analysis of the pickling liquors were tested and implemented into pickling process control. As a result of this, the pickling line staff will be enabled to control the process with all the up-to-date process knowledge they need. Work started with the assessment of actual operating situations of pickling lines and the definition of optimum pickling process parameters. Operational and laboratory trials were performed to determine suitable sensor applications. Data acquired from those trials with operational and artificial pickling solutions were investigated for mathematical modelling. Sensors were developed and the establishment of communication between different modules of process control was considered. In order to apply sensors on site, operational measurements were performed. Suitable processes and locations were fixed for future installations. Finally, the partners reviewed optimum pickling process parameters to programme the aspired process control. The results achieved were verified in comprehensive operational trials. Sensor-based

online Sensor-based online control

control of pickling lines

pickling

lines

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Contact: RFCS publications Address: European Commission, CDMA 0/124, B-1049 Brussels Fax (32-2) 29-65987; e-mail: [email protected] European Commission Research Fund for Coal and Steel

Sensor-based online control of pickling lines

H. Schmermbeck, R. Wolters, B. Schmidt VDEh-Betriebsforschungsinstitut GmbH (BFI) Sohnstr. 65, 40237 Düsseldorf, Germany W. Henning Rasselstein GmbH (TKS-RA) Koblenzer Str. 141, 56626 Andernach, Germany A. Giannetti, M. Gabriele, G. Zangari Centro Sviluppo Materialia S.p.A. (CSM) Via di Castel Romano 100, 00128 Rome, Italy H. Lopez, I. Machon Universidad de Oviedo (UniOvi) Plaza de Riego 4, Edificio Historico 3a planta, 33003 Oviedo, Spain A. Björk, S. Nilsson, M. Andersson, R. Bergström Svenska Miljöinstitutet AB (IVL) Vallhallavägen 81, 11427 Stockholm, Sweden

Contract No RFSR-CT-2004-00052 1 July 2004 to 31 December 2007

Final report

Directorate-General for Research

2009 EUR 23872 EN LEGAL NOTICE Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information.

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Luxembourg: Office for Official Publications of the European Communities, 2009

ISBN 978-92-79-11589-93

ISSN 1018-5593 doi 10.2777/48068

Printed in Luxembourg

Printed on white chlorine-free paper

Table of contents

Page

List of symbols 5

1. Final Summary 7

2. Scientific and technical description of results 15

2.1 Objectives of the project 15

2.2 Comparison of initially planned activities and work accomplished 15

2.3 Description of activities and discussion 15 2.3.1 WP 1: Analysis of actual operating situation of pickling lines, definition of optimum pickling process parameters 15 Involved Partners 15

Objectives of WP 1 15

Task 1.1: Assessment of pickling plants 16

Task 1.2: Preparation of operational measurements 19

Task 1.3: Performance of operational and laboratory measurements 22

2.3.2 WP 2: Sensor development and laboratory application tests 31 Involved Partners 31

Objectives of WP 2 31

Task 2.1 Preparation of initial operational measurements 31

Task 2.2 Determination of suitable sensor application 34

Task 2.3 Performance of laboratory trials 35

Task 2.4 Modelling, programming and prototype development 47

Task 2.5 Establishment of communication with modular process control tool (PCT) 55

Task 2.6 Documentation, Dissemination and Education 56

2.3.3 WP 3: Detailed on-site sensor-based analysis of operational pickling baths 56 Involved Partners 56

Objectives of WP 3 56

Task 3.1 Preparation of operational measurements 56

Taks 3.2 Performance of operational and laboratory measurements 58

2.3.4 WP 4: Review of optimum pickling process parameters, programming of process control tool 61 3

Involved Partners 61

Objectives of WP 4 61

Task 4.1 Preparation of process control tool development 61

Task 4.2 Programming efforts 65

Task 4.3. Setting up of graphical user interface 78

2.3.5 WP 5: Operational tentative implementation, testing and verification of sensor based PCT 88 Involved Partners 88

Objectives of WP 5 88

Tasks 5.1 and 5.2 Preparation and Performance of verification trials 88

Task 5.3 Assessment of achieved results 90

2.3.6 WP 6: Implementation of sensor bases online control in European pickling lines 91 Involved Partners 91

Objectives of WP 6 91

Task 6.1 and 6.2 Assessment of available analytical techniques, control devices and simulation tools 91

Task 6.3 Coupling and tailoring of PCT for the selected lines 93

2.3.7 WP 7: Co-ordination, reporting, documentation 93

2.4 Conclusions 93

2.5 Exploitation and impact of the research results 94

References 95

List of Tables and Figures 97

Annex I: Additional Figures

Annex II – Annex V

List of symbols A Index of Aggressiveness ANN Artificial Neural Network BP Backpropagation c concentration f factors for IAggr FLSOM Fuzzy Labelled SOM

IAggr Index of aggressiveness K constant in Fe-HCl equilibrium

λel electric conductivity P Index of Pickling Ability PT pickling time (time to pickle) SOM Self-Organising Map, a special type of ANN T temperature

⋅ V flow rate vLine (pickling) line speed vus ultrasonic velocity VSA Virtual Sensor of Aggressiveness VSP Virtual Sensor of Pickling Ability WL weight loss

Indices

Flow rates:

Acid fresh acid addition flow rate Cond condensate flow rate Total overall flow rate Concentrations:

Fe iron concentration H2SO4 sulphuric acid concentration

Factors for IAggr:

Aggr Aggressiveness factor Inhib Inhibition factor Line Pickling line factor General:

G Global Set set value

1. Final Summary

The main aim of the project was the development of a new sensor based control technology for operating pickling lines. Pickling is the most important step to remove surface scale layers and is strongly depending on the exactly controlled pickling liquor composition. Today there is no feasible system available for online control of pickling lines. Within this project new methods for online analysis of the pickling liquors will be tested and implemented into an overall pickling process control tool. Therefore the pickling line staff will be enabled to control the process really contemporary with all the process knowledge they need.

The partners followed different approaches to reach this goal. BFI (together TKS-RA and with their subcontractor ST) and IVL developed sensors to assess pickling liquor concentrations. CSM focussed on the definition of a new index, the Index of Aggressiveness, and a virtual sensor for its assessment. UniOvi finally uses a statistical approach basing on SOM technology to predict an optimum line speed for high productivity and minimum pickling defects. To achieve their individual goals and stick to the overall objective a work plan of seven work packages was set-up.

At first, in WP 1, suitable pickling lines, including rinsing and regeneration, have been defined and selected in task 1.1. This formed the basis for the development of new sensor applications in WP 2 and the conceptual design of process control in WP4. Both operational data and expert knowledge have been acquired. Therefore, operational measurements and data acquisition have been prepared (task 1.2) and performed (task 1.3) at different sites, e.g. at TKS-RA, Figure S1, by BFI and Aceralia, by UniOvi.

Accumulators

1 2 3 4 Source: http://www.rasselstein.de Source: Scale-breaker/unwind Pickling baths Rinse/Dry Rewind

addition of Regeneration fresh acid

FeSO 4

Figure S1 Schematic of pickling section at TKS-RA, Andernach

Furthermore, several pickling lines have been considered by CSM. Operational assessment was complemented by BFI and CSM laboratory trials and analyses, mainly using artificial pickling solutions. These were performed to determine optimum pickling parameters, concerning the minimisation of metals precipitation, enhancing pickling velocity and reducing surface pickling defects. UniOvi acquired an

initial data set from the plant. Data preparation was focused on the identification of the different defects and the establishment of the involved variables in each defect. In particular, the number of overpickling and scale residues on the edge of the strip (underpickling) were quantified using an automatic inspection system. CSM selected relevant parameters and prepared lab trials by means of literature, archive, intra-project questionnaire and on field data related to pickling process. Then a DOE factorial experimental plan of lab trials had been carried out measuring the time to pickling of scaled samples and the weight loss of scale free samples.

In WP 2 several trials both in laboratory and operational scale were performed (tasks 2.1 – 2.3). Results have been processed and some programming work was done to initially develop the two physical sensors of BFI and IVL and the virtual sensor of CSM (task 2.4). The results of BFI laboratory trials can be put together in a way that you receive a grid of lines representing constant concentrations of the two components (Fe and H2SO4) regarded at a given temperature, shown exemplarily in Figure S2.

Figure S2 Grid of lines of constant concentrations (Fe and H2SO4) at a given temperature in the plane set-up by physical parameters

IVL has developed, built and tested an acoustic measurement equipment that uses direct acoustic chemometrics (DAC) for measuring HNO3, HF and metal ion concentration. Pickling acid samples with varying HNO3, HF and metal ion content, provided by a Swedish mill, were run in the measurement equipment while collecting acoustic vibration data and thereafter calibration models were built utilizing Partial Least Squares (Regression), PLS. The validated models showed good ability to predict the total metal ion concentration, fair predictably for HF and fair/poor for HNO3, Figure S3.

Figure S3 Presents the model predicted value (x-axis) of metal ions against the observed value (y- axis). A prediction error of 3.4 g/l were obtained

As to the index of aggressiveness IAggr, it expresses the ability of the pickling system to dissolve scale and base metal, using a combination of 3 factors which contribute to form the aggressiveness. Its most general form is as follows:

IAggr = f (fAggr,f Inhib, fLine) (1)

Within the project the partners originally aspired to develop a process control tool. It became clear (task 2.5) that there is no practical way to develop such tool here. Instead, communication interfaces in general have to be tailor-made for each specific plant. UniOvi, BFI and TKS-RA established necessary communication using interfaces given at the plants, according to task 6.3.

In WP 3, the main objective was to acquire operational data using the newly developed sensor at TKS- RA (task 3.1 and 3.2). This has been accompanied by further laboratory trials and analyses to review optimum pickling parameters. BFI and TKS-RA therefore installed an operational measurement section over there, Figure S4.

US sampling head

el. cond. sampling head

gates gates bypass

drain

sampling head

Figure S4 Installation of operational measurement device at TKS-RA in the feed pipe of the crystalli- zation section

Data obtained from the system has been recorded continuously. A graphical representation of the concentration obtained from the implied model implemented in the controller of the same period is given in Figure S5.

24 6,4

23 6

22 5,6

21 5,2

20 4,8 Concentration of iron in m-% H2SO4-concentration in m-% in H2SO4-concentration

19 4,4

18 4 01.12. 01.12. 02.12. 02.12. 03.12. 03.12. 04.12. 04.12. 05.12. 05.12. 06.12. 06.12. 07.12. 07.12. 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 Time H2SO4 concentration [m-%] Fe concentration [m-%]

Figure S5 Typical time series of concentrations from operational trials at TKS-RA

Further more BFI has inspected the sensors immersed into the pickling medium regularly after periods of one month up to nine month of immersion. No corrosive attack, in particular at the metallic ultrasound sensor, has been observed, even after such long periods of immersion.

In WP 4, the main objectives was the development of a virtual sensor for application to the pickling bath based on a previous model, whose training variables were established in WP1. This virtual sensor should predict some key parameters to improve the plant operation. In this way, programming of simulation modules and a graphical user interface are necessary.

A SOM and later a FLSOM network were developed and trained to obtain an estimation of the optimum line speed that minimizes as many overpickling defects as underpickling defects for each acid concentration, thickness and type of steel (task 4.1). Using FLSOM allows taking into account the available classification of the global defect of the coils. Uniovi developed the software to implement the suitable application in tasks 4.2 (Programming efforts) and 4.3 (Setting up of graphical user interface). The programming task designed the data acquisition procedures that are necessary to obtain the data set for model training. One trajectory on the output space is formed by the sequence of the best matching units varying the acid concentration, minimizing the defects and corresponding to a certain temperature is given exemplarily in Figure S6.

Figure S6 Trajectory to estimate the optimum line speed with 74

CSM in task 4.1 defined two indexes rather than only one, based on the predictive values calculated an ANN: Index of Pickling-ability (P) and Index of Aggressiveness (A). To develop the predictive tools for pickling-ability and aggressiveness in hydrochloric and sulphuric baths, purposely Artificial Neural Network (ANN) have been developed, taking into account the effect of inhibitor concentration. Finally

two virtual sensors have been realized: a virtual sensor of pickling-ability (VSP) and a virtual sensor of aggressiveness (VSA), suitable for predictive on line application, based on:

• the acquisition of the process and line parameters for each tank (bath temperature, H+, Fe++, inhibitor, incoming strip temperature, steel class etc.) • the predictive tools for WL and PT • a dedicated algorithm for A and P calculation in a single and a multi-tank system • a graphical user interface for demo and on line application of the two virtual sensors.

Both CSM and UniOvi developed GUIs for their applications in task 4.2.

In WP 5 the physical and virtual devices developed by BFI, CSM and UniOvi were implemented at the same selected processes as in WP 1, e.g. Aceralia and TKS-RA to verify the results achieved earlier, tasks 5.1 and 5.2. Finally the the achieved results were assessed, task 5.3. TKS-RA is convinced that about some part of their annual productivity increase during the past year origins in the introduction of the installed measurement system. At the newly introduced mixing tank at TKS-RA, prior to tank 4, concentration data is used to generate set-values for fresh acid and condensate addition, Figure S7.

Figure S7 Calculation of fresh acid and water addition at TKS-RA mixing tank

Finally, in WP 6, a general approach for the implementation of sensor-based online control in European pickling lines was presented. All partners are sure that their developments will be beneficial for the applications regarded in this research endeavour and transferability is given, tasks 6.1 and 6.2.

As mentioned above, it was not feasible to develop one single PCT. However, tailor made solutions have been found and implemented at Aceralia and TKS-RA to implement the developments of UniOvi and BFI, respectively, task 6.3.

Regarding the administrative WP 7 and the documentation tasks of the technical work packages, all partners have met regularly to intensively assess and discuss their results and to refine their further common work within this project. Reports and publications have been presented to the technical group members and to public. Documentation of the different GUIs are presented within this report.

As a conclusion it can be summarised that within the completed research project the partners have successfully developed several different approaches towards sensor based online control of pickling lines. The new systems and models have proved their reliability in operational trials and communication to plant-wide control has been established and a closed loop dosage of fresh acid has been realised.

The results of the completed research project are very promising towards later exploitation. To establish that in detail the partners of the completed research project BFI, who have patented their measurement system, TKS-RA and UniOvi have been granted funding for a pilot and demonstration project to apply both technologies and their combination for optimum pickling line control. Within the pilot project four new sensor systems shall be installed to enable control of four including the prototype five measurement locations.

2. Scientific and technical description of results

2.1 Objectives of the project

Main aim was the development of a new sensor based control technology for operating pickling lines. Pickling is the most important step to remove surface scale layers and is strongly depending on the exactly controlled pickling liquor composition. Today there is no feasible system available for online control of pickling lines. Within this project new methods for online analysis of the pickling liquors have been tested and implemented into an overall pickling process control tool. Therefore the pickling line staff will be enabled to control the process really contemporary with all the process knowledge they need.

2.2 Comparison of initially planned activities and work accomplished

All initially planned activities and work of the partner BFI, TKS-RA, CSM and UniOvi were successfully and completely accomplished.

2.3 Description of activities and discussion

2.3.1 WP 1: Analysis of actual operating situation of pickling lines, definition of optimum pickling process parameters

Involved Partners: BFI, TKS-RA, CSM and UniOvi have been working on all tasks.

Objectives of WP 1

Suitable pickling lines, including rinsing and regeneration, should be defined and selected, for the conceptual design of a new sensor application (WP2) and the development of process control (WP4).

Both operational data and expert knowledge should be acquired. Therefore, operational measurements and data acquisition should be done at different sites, e.g. at TKS-RA and Aceralia. Operational assessment should be complemented by laboratory trials and analyses, mainly using artificial pickling solutions. These should be performed to determine optimum pickling parameters, concerning the minimisation of metals precipitation, enhancing pickling velocity and reducing surface pickling defects.

It had to be considered whether additional SCADA equipment needs to be installed at the measurement locations or not.

Task 1.1: Assessment of pickling plants

TKS-RA H2SO4 pickling plant

BFI and TKS-RA carefully assessed the production, rinsing and regeneration process at the TKS-RA pickling line, Figure S1, for application. The pickling line that the partners BFI, TKS-RA and Sen- soTech worked at is the sulphuric acid pickling line at TKS-RA, which is part of the world’s largest thin plate production site located in Andernach, Germany. Any coil of the annual production of approximately 1.4 Mio t needs to pass the pickling line. It is operated in counter current mode consisting of four tanks of 50 m³ each and a regeneration plant that removes iron from the used pickling acid by crystallisation. The total hold-up of the pickling system is approximately 230 m³ circulated at a flow rate

⋅ V Total of 23 to 25 m³/h.

All pickling tanks are operated at a set temperature TSet of 98°C, each, which is close to boiling temperature (~ 102 – 103°C). The acid concentration cH2SO4 increases from tank 1 to tank 4 from about 18 to 20 m-% up to 22 to 24 m-%. In turn the iron concentration cFe decreases from tank 1 to tank 4 from around 5 m-% down to around 3 m-%. The used pickling acid coming from tank 1 is reduced in iron content down to about 3.5 %. Iron is removed in the form of FeSO4 ⋅ 7 H2O. The regenerate is filled up

⋅ with condensate amounting to V cond of approximately 3 m³/h to compensate for the volume loss, mainly coming from evaporation in the pickling tanks and by iron removal in the regeneration. Finally, in tank 4 the regenerate, filled up with condensate, is recycled and fresh acid is added at a flow rate

⋅ V Acid of approximately 600 to 800 L/h.

The pickling line is controlled by setting the line speed vLine to values of minimum 30 up to 220 m/min. Pickling quality is controlled at a surface inspection stand at the end of the pickling line. Entry and exit sections of the pickling line both are equipped with a horizontal strip accumulator, each, to allow for continuous pickling operation. Besides line speed only bath temperatures of tanks 1 to 4 are controlled continuously. Concentrations are monitored manually, only, by titration. Acid and iron concentrations of tank 1 are controlled most regularly at a time interval of 4 hours (two analyses per shift). These analyses are performed by regeneration plant operators. It is up to them to set both the overall circuit

⋅ ⋅ flow rate V Total and the fresh acid addition flow rate V Acid according to their analyses and considering the production program running and scheduled ahead.

HCl pickling plants

CSM has reviewed operational process parameters and elaborated coming from literature, archive, intra-project questionnaire and on field data from plants for hydrochloric and sulphuric pickling.

Three pickling campaigns have been monitored in an industrial pickling line for electric steel using fresh hydrochloric acid to feed the tanks. Main pickling process data as: fresh acid delivery, bath temperature, acid concentration and Fe++ concentration have been monitored and examined in the 3 tanks of the continuous pickling line, as shown in Table 1.

Table 1 Concentration values during CSM industrial campaigns

I campaign II campaign III campaign HCl V1 average g/l 74 58 81 range g/l 47-108 42-86 54 -105 HCl V2 average g/l 137 125 132 range g/l 119-158 112-144 118-151 HCl V3 average g/l 187 195 199 range g/l 169-197 173-206 178-208 Fe++ V1 average g/l 71 76 62 range g/l 61-89 66-100 45-83 Fe++ V2 average g/l 44 41 28 range g/l 32-61 33-56 19-37 Fe++ V3 average g/l 17 21 12 range g/l 12-20 17-27 10-16

Hydrochloric pickling has been examined of carbon and electric steels, and average values of main process parameters are shown in Table 2, coming from 5 industrial pickling lines. Line 3 uses fresh acid, others use regenerated acid. The range of HCl concentration in the five plants depends of use of regenerated or fresh acid to feed the tanks. Regenerated acid concentration is limited to about 170÷190 g/l, according to regeneration process efficiency, while fresh acid allows a higher incoming concentration into last tank.

Table 2 Concentration values from five industrial lines

I tank II tank III tank IV tank HCl Fe++ HCl Fe++HCl Fe++ HCl Fe++ g/l g/l g/l g/l g/l g/l g/l g/l Line 1 39 126 N.A. N.A. N.A. N.A. 122 62 Line 2 33 122 N.A. N.A. N.A. N.A. 124 54 Line 3 71 70 131 38 194 17 ------Line 4 56 117 80 97 101 74 ------Line 5 30 120 70 90 120 60 170 30

Temperature of the tanks ranges from 85° (first tank) to 60°C (last tank), related to HCl concentration because evaporation of acid is very sensitive to concentration and temperature enhancement.

Acid and iron concentrations are strictly linked due to base pickling reactions, so concentrations of intermediate tanks can be interpolated, if first and last tanks values are known. An existing model is available for process parameters interpolation in a multi-tank system with bath flowing in counter current. Available data show systems having 3 or 4 tanks are representative of existing lines. Further data for sulphuric pickling lines coming from partners has been used to better characterize such a system (see questionnaire in Annex I).

Aceralia HCl pickling line

The pickling plant of Aceralia (ArcelorMittal group) was chosen by UniOvi to acquire data that will be applied to further analyses of validation. The flow diagram of pickling plant is shown in Figure 1. A complete documentation was studied about the pickling line.

UN- SHEA WELDER INPUT LOOPING COILER R ACCUMULATION

SCALE BREAKER

OUTPUT LOOPING DRYER WASHING ACID ACCUMULATION TANKS TANKS

AUTOMATIC OILER SHEAR RECOILER INSPECTION

Figure 1 Schematic of Aceralia pickling line No. 2

Firstly the coil is loaded in the uncoiler to be processed. The head of the coil is cut with a shear to get a correct shape. This coil is welded to the previous coil because of the continuous strip requirement. Then the strip loop is accumulated to permit the central section of the pickling plant go on with an acceptable velocity whereas the input section is stopped due to the new coil replacement and the weld.

The central section, where the treatment really takes place, begins with a scale breaker to facilitate the scale removal by means of the acid treatment. This acid treatment is carried out in four tanks using hydrochloric acid (HCl). The loop in the tanks is regulated with a magnetic sensor. Moreover inhibitors are used to prevent from overpickling. Then the strip is cleaned in five tanks with cold and hot water. At the final of the washing process there are wringer rolls and a hot air dryer.

In the final section the strip loop is accumulated to allow the central section to go on during the coil removal from the recoiler and the next coil threading. Finally the strip is oiled, cut and got coil in the recoiler.

Task 1.2: Preparation of operational measurements

BFI-trials with H2SO4

BFI has constructed a laboratory set-up represented in Figure 2 to perform sequences of alternating heating and cooling of artificial pickling solutions. The artificial solutions investigated were mixed according to the matrix of concentrations given in Table 3, which has been considered to be a good range covering typical operational concentrations found at TKS-RA.

Table 3 Matrix for laboratory measurements with artificial H2SO4 pickling solutions x – “zero”-values; X – minor relevant values; XX – values with operational relevance

Fe(%) 0 1 3 4 5 6 7

H2SO4 (%)

1 x x x x x x x

14 x X XX XX XX XX XX

16 x XX XX XX XX XX XX

18 x XX XX XX XX XX XX

20 x XX XX XX XX XX XX

22 x XX XX XX XX XX X

26 x XX XX XX X X X

DasyLab System

EC controller LiquiSonic Controller SensorAO AI Sensors RS232 4-20mA RS232 RS232 COM1 COM2

EC US / T sensor sensors

Heater RS232

double-layer glass with pickling solution

Figure 2 BFI laboratory set-up

CSM-trials with H2SO4 and HCl

In Table 4 ranges of process parameters selected by CSM for HCl DOE trials are shown. Further data coming from intra-project questionnaire has been collected and examined to complete the parameters ranking and ranges and to correlate with partners operational conditions. In Table 5 ranges of process parameters selected for H2SO4 DOE trials are shown.

Table 4 Ranges of selected parameters for HCl baths

Process parameter min max temperature °C 50 90 HCl concentration g/l 40 200 Fe++ concentration g/l 7 130 inhibitor concentration ml/l 0 1

Table 5 Ranges of selected parameters for H2SO4 baths

Process parameter min max temperature °C 91 101 acid concentration g/l 170 290 Fe++ concentration g/l 20 60 inhibitor concentration ml/l 0 1

UniOvi data acquisition

Although an initial data set from the pickling plant was acquired by UniOvi during the first semester detecting some correlations and establishing the relative ranges of the process variables, the definition of the defects were a bit ambiguous. However, since the second semester a better definition of the defects was achieved by means of an automatic inspection system. The inspection technology that was applied is the standard Parsytec system that is designed for automatic inspection of fast moving materials and allows recognition of material flaws during the production. The system consists on a surface sensor that is integrated into the inspection machine to check the coil surface and a recognition server on which the images from the sensor are processed with suitable software. The surface sensor uses infrared LEDs as a light source and records the images with matrix video cameras. Defect data are stored in the online database and the corresponding defect images are either storage in the local disc of the individual camera PC or centralized on the archive PC. An overview of the system is shown in Figure 3, which makes an automatic defect detection and classification over the entire strip surface possible. An overview of the Parsytec inspection system is shown in Figure 4.

Figure 3 Surface Inspection system at Aceralia pickling line No. 2

All of the recorded images are inspected in real-time for potential defects. An analysis of detected defect formations is performed in this processing step, e.g. point shapes, line shapes, etc. After this stage even the smallest adjacent defects are detected here and assembled into surface defects. This enables entire defect structure complexes, such as reel slips, rust and so on, to be captured and detected as a whole.

Figure 4 Lightning and cameras of the surface inspection system at Aceralia pickling line No. 2 (1) diffuse infrared LEDs light source; (2) diffuse light cameras; (3) direct light source; (4) direct light cameras.

Defects are described analytically in this step, i.e. their exact position on the steel surface is registered together with their significant characteristics, Figure 72, Annex I.

A subsequent classification of individual defects is done by means of determination of the type, size and significance for each defect and then displayed directly in production terms such as depressions, roller impressions, reel slips, etc. The adaptation of classifiers to equipment and material specific defects can be handled simply by creating a suitable defect image catalogue. New defect types can also be quickly integrated later.

The defect data are stored in the online database, which is handled by SQL. Each defects data set contains the defect characteristic, together with the reference to the reel/coil data an the defect images, Figure 73, Annex I.

Considerations on Mixed Acid Pickling

The key parameters for a mixed acid pickling line control are the acid concentrations (HF and HNO3) and the total metal ion content. There are already manual equipments [1] for measuring these parameters intermittently by an operator.

Refer also to task 2.1, in paragraph 2.3.2.

Task 1.3: Performance of operational and laboratory measurements

BFI / TKS-RA measurement with H2SO4

BFI has performed measurements in the laboratory set-up represented in Figure 2. In the double layer glass tank artificial pickling solutions were heated and cooled in turn with controlled temperature gradi- ent to avoid errors due to heat capacity differences of the sensors and solutions. Actual temperature T,

absolute electric conductivity λel and ultrasound velocity vUS were measured throughout the trials. A typical curve obtained from those experiments is given exemplarily in Figure 5.

1610 1400

1600 1300

1590 1200

1580 1100

1570 1000 ultrasound velocity

1560 electric conductivity 900

1550 800 ultrasoundin velocity m/s

1540 700 conductivityelectric in mS/cm

1530 600

1520 500

1510 400 20 30 40 50 60 70 80 90 100 T in oC

Figure 5 Example of typical curve obtained from BFI laboratory trials

BFI has taken samples at the pickling circuit operated at TKS-RA plant in Andernach, Figure 6. The samples were analysed at the BFI laboratory, Table 6.

Addition

H2SO4

Sampling Point Strip X Strip 1234 234

X Sampling Point X Sampling Point

Regeneration Pickling liquor (()Crystallisation ) circuit FeSO * 7 H O 4 2 Figure 6 Schematic of process section of the TKS-RA pickling line, Andernach

Table 6 BFI-analyses of samples taken at TKS-RA

Sampling point m-% H2SO4 Fe as

m-% Fe m-% FeSO4 m-% FeSO4*7 H2O Tank 1 21.6 4.7 12.7 23.2 Tank 4 23.6 3.7 10.1 18.4 Regeneration 21.6 2.7 7.3 13.4

CSM laboratory measurements

A campaign of comparative tests on an industrial hydrochloric pickling line has been pursued by CSM with the aim of verifying the coherence of results among lab and real baths. This was a 3 pickling line having no HCl recovery, at all. The same steels used in the lab have been dipped into the pickling tanks and bath composition analysed. Lab trials, simulating the same bath compositions and conditions, have been used to compare with on line results and verify their coherence.

Weight loss values into the 3 process tanks using steel A and B had been found to be quite similar to lab values into synthetic solutions purposely prepared. A maximum deviation of about 4% had been detected, with the highest negative value into the first tank (low acid and high iron), may be due to high level of impurities brought by dissolved scale into the liquor, which could reduce the expected aggressiveness. A slight positive deviation, less than 2%, has been detected into the last tank (high acid and low iron) may be due to fresh acid added into the tank.

Analytical results (UniOvi)

UniOvi needed to identify the different defects in the data set as well as the establishment of the process variables that are involved in each defect. Two kinds of defects were taken into account: overpickling defects due to an excessive acidic attack to the steel strip and underpickling defects that consist on scale residues on the edge of the strip. By means of the better definition of the defects using the automatic inspection system described in the previous section, a correlation analysis between process variables and defects were done in task 1.3 and some analytical and graphical results were obtained. These results are presented in the following two subsections.

The first analyses comprise a checking of the correlation rates between process variables. Correlation measures how values of one variable change when the values of another variable change. Linear correlation measures the closeness to linear of the relationship . If two variables vary in such a way that the relationship is exactly linear, then, knowing of linear relationship, and knowing the value of one of the variables, a 100% confident prediction can be made of value of the other.

Correlation is expressed as a number ranging between +1 and –1. A correlation equal to +-1 indicates perfect predictability. When positive, a linear correlation of 1 says not only that the two variables are 24

completely linearly related, but also that they move in the same direction. A linear correlation of –1 indicates a perfectly predictable relationship, but the values of variables move in opposite directions. A correlation of 0 means that knowing the value of one variable tells nothing about the value of the other variable. The interpretation of the correlation coefficient depends on the context, purposes and the number of data, so in this case, as a general guide, until the correlation gets outside the range of between +0.3 to -0.3, [2]-[3], any connection is tenuous at best,; all ranges are summarized in Table 7.

Table 7 Correlation coefficient interpretation.

Correlation Negative Positive Slight −0.29 to −0.10 0.10 to 0.29 Medium −0.49 to −0.30 0.30 to 0.49 Strong −1.00 to −0.50 0.50 to 1.00

The correlation coefficient can be used to estimate the correlation of a set of pairs of quantities, written as xi and yi where i = 1, 2, ..., n. This coefficient is equal to the sum of the products of the deviation of each quantity in the pairs from its respective mean, divided by the product of the number in the set and the standard deviations. That expression is equation (2).

n * ∑ xi * yi − ∑ xi * ∑ yi rxy = (2) 2 2 2 2 n * ∑∑xi − ( xi ) * n *∑∑yi − ( yi )

The significant values of the resulting analysis are summarized in Table 8. Some remarkable correlation appear in this analysis: line speed bears a indirect correlation with overpickling defects, besides, a quite clear correlation is evident for line shutdown time, supported by a quite high correlation coefficient.

Table 8 Correlation values between overpickling defects and line speed

Overpickling defects with: Results Line Shutdown time 0.697 ( Strong positive correlation) Line SpeedMax -0.364 ( Medium negative correlation) Line Speed -0.371 ( Medium negative correlation)

Other values of the resulting analysis are shown in Table 9. Coefficients indicate that there is medium negative correlation.

Table 9 Correlation values between thickness strip and line speed

Thickness with: Results Line SpeedMax -0.381 ( Medium negative correlation) Line Speed -0.363 ( Medium negative correlation)

Graphical results (UniOvi)

Table 10 shows the process variables taken into account in the analysis.

Table 10 Key process variables

Variable name Description Line speed Average speed for each coil, in percentage Line shutdown time Time measured during the strip stop for each coil Defect severity ratio Index of defect severity for each coil Acid concentration Acid concentration in a specific tank Length Length of the pickling coil Thickness Thickness of the pickling coil Number of overpickling defects Number of overpickling defects for each coil Number of bad pickling defects Number of bad pickling defects for each coil

The number of overpickling defects in function of the line speed is shown in Figure 7.

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

number of overpickling defects (normalized) defects overpickling of number 0

-0.1 0 20 40 60 80 100 120 140 line speed (%)

Figure 7 Number of overpickling defects versus line speed

All these coils have been classified as overpickling coils considering the overpickling as global defect. The number of defects has been normalized using length of coil and the defect severity ratio by means of equation (3).

N _ defects× Defect _ severity _ ratio N _ defects _ normalized = (3) Length where the values of the defect severity ratio are between 1 and 6.

In Figure 7 there is a higher density of samples and a higher number of overpickling defects with lower speeds. In Figure 8 the defect severity ratio and the line shutdown time for each overpickling coil are shown in function of the line speed. Therefore, only the coils whose global defect is overpickling are displayed. The line shutdown time affects the defect severity ratio and higher line shutdown times take place at lower speeds. This fact verifies Table 8.

0 0 20 40 60 80 100 120 140

overpickling defect severity ratio severity defect overpickling line speed (%)

500

400

300

200

100 line shutdown time shutdown line 0 0 20 40 60 80 100 120 140 line speed (%)

Figure 8 Defect severity ratio and line shutdown time for each overpickled coil vs. line speed

The number of bad pickling defects in function of the line speed is shown in Figure 9. All these coils have been classified as bad pickling coils considering the bad pickling as global defect. Here, the bad pickling is considered as scale residues on the edge of the strip. The number of defects has been normalized using the length of the coil and the defect severity ratio by means of equation (3). The density of samples and the number of bad pickling defects should be higher with higher speeds.

0.7

0.6

0.5

0.4

0.3

0.2

0.1 number of bad pickling defects (normalized) defects pickling bad of number

0 0 20 40 60 80 100 120 140 line speed (%)

Figure 9 Number of bad pickling defects versus line speed

The temperatures in the tanks are key variables but they are not taken into account because of their nar- row range of values due to the temperature control. On the other hand, also the acid concentrations in tanks are important variables but only one of them (acid concentration in tank 3) is considered because the four concentrations (one for each tank) are absolutely correlated as can be seen in Figure 10.

50 acid 40 concentr. in tank 1 30

20 0 500 1000 1500 2000 2500 80 acid concentr. 60 in tank 2

40 0 500 1000 1500 2000 2500 120

acid 100 concentr. in tank 3 80

60 0 500 1000 1500 2000 2500 140 acid concentr.120

in tank 4 100

80 0 500 1000 1500 2000 2500 number of coil

Figure 10 Acid concentrations in the tanks

The higher the thickness the lower the line speed due to operative conditions. This can be observed in, Figure 11, Figure 12 and Table 9.

overpickling coils 5.5 average line speed

4.5

3.5 thickness 3

2.5

1.5 0 20 40 60 80 100 120 140 line speed (%)

Figure 11 Thickness versus line speed with overpickling coils

bad pickling coils 5.5 average line speed

4.5

3.5 thickness 3

2.5

1.5 0 20 40 60 80 100 120 140 line speed (%)

Figure 12 Thickness versus line speed with bad pickling coils

Also the average line speed is lower with higher thickness than with lower one and the range of line speed are lower with overpickling coils than with bad pickling coils as could have been expected. The same can be seen in Figure 13 and Figure 14. Moreover, the higher the acid concentration the higher the line speed to keep the residence time of the strip in the acidic baths constant.

overpickling coils 140

average line speed 120

100

60 line speed (%) speed line

0 40 50 60 70 80 90 100 110 acid concentration in tank 3

Figure 13 Line speed versus acid concentration with overpickling coils

bad pickling coils 140

average line speed 120

100

60 line speed (%) speed line

0 40 50 60 70 80 90 100 110 acid concentration in tank 3

Figure 14 Line speed versus acid concentration with bad pickling coils

Figure 15 shows two line speed ranges of overpickling (seriously defective and slightly defective). Each box has lines at the lower quartile, median and upper quartile values. The whiskers are lines ex- tending from each end of the box to show the extent of the rest of the data. Outliers are data with values beyond the ends of the whiskers.

Overpickling Ranges

120

100

60 line speed(%) line

0 1 2 seriously defective slightly defective N defects normalized > 0.1 N defects normalized < 0.1 Figure 15 Overpickling and bad pickling ranges

2.3.2 WP 2: Sensor development and laboratory application tests

Involved Partners: BFI, CSM and IVL have been working on all tasks except for CSM in task 2.2.

Objectives of WP 2

In this work package, the major work is to design and develop two different types of new sensors. The two sensors are based on active ultrasonic (AU) and passive audible and low frequency ultrasonic emis- sion from a constricted flow (DAC). The harsh environment the sensor systems will face in normal production sets strong demands on material selection and mechanical design. Another important part of the work is to establish a common interface/protocol towards modular process control (WP 4). Another objective is to develop an index of aggressiveness by making laboratory trials.

Task 2.1 Preparation of initial operational measurements

DAC –system (IVL)

An initial case study was performed by IVL to identify system components and settings that affects the quality of the acoustical signal measured. For instance, the damping of vibrations created in the pump in the system should be improved. This can be done by building a heavy weight foundation, that is with at least the same mass as the pump, preferably large than so. The pump foundation should be put on chemical resistant elastic dampers to further improve the vibration isolation. To isolate the equipment as much as possible from the surrounding, the foundation for the complete measurement system should be

considered. However, this might be difficult in the harsh environment. The size of pump and selection of pump type influences these factors.

AU-system (BFI)

This task is highly related to task 1.2 in WP1. Key parameters that should be measured are defined as well as some additional constraints on measurement system design and sample flow and handling to the sensors.

Considering the given pickling plant configuration the partners BFI, TKS-RA and SensoTech decided to monitor acid and iron concentration in tank 1 to obtain continuous concentration data for quality

⋅ assurance and to support regenerating plant personnel with their task to set the flow rates V Total of the

⋅ overall circuit and V Acid fresh acid addition.

Relevant parameters coming from WP1 and selected for further laboratory trials (BFi and CSM) are

• temperature • Fe+++ concentration • acid concentration • inhibitor concentration • Fe++ concentration • strip speed

CSM-Trial planning

A two-dimensional space has been selected in the plane [HCl]-[Fe++] vs. temperature, taking into account the correlation between Fe++ and HCl for a continuous multi-tank line. Due to the equilibrium between iron dissolution and acid consumption, [Fe++] and [HCl] are strongly linked, and the values can be obtained by means of a simple equation:

[HCl] + 1.3 . [Fe++] = K (in g/l) (4)

The equation allows to treat [Fe++] and [HCl] as interdependent variables so, from a statistical point of view, only a part of the plane [HCl]/[Fe++] has to be considered, in the neigh borough of the reference line coming from equation (4). The reference line shown in Figure 16 concerns a K value of 210, chosen according to operational values of existing hydrochloric lines, taking into account the very low concentration value of Fe++ coming into the tanks with the regenerated acid.

HCl/Fe++

150

100 Fe++ Fe + 20 Fe - 20 concentration (g/l) concentration ++ ++ Fe 50

0 0 50 100 150 200 HCl concentration (g/l)

Figure 16 Domain of investigation in the plane [HCl]/[Fe++]

The investigation domain within the dotted lines has been obtained according to the operational ranges of variables of the industrial lines (40÷200 g/l for [HCl] and 7÷130 g/l for [Fe++]), with a fluctuation of ±20 g/l for [Fe++]. A two-dimensional matrix of experiments, based on DOE approach, has been selected. A five levels experimental plan, shown in Figure 17, has been selected for main variables: temperature and [HCl]-[Fe++].

130 99 69 38 7 Fe++ 40 80 120 160 200 HCl 50 60 70 80 °C 90 Figure 17 Matrix of conditions for HCl DOE trials

A different approach has been used for sulphuric system. The main differences with the hydrochloric system concern the management of pickling process due to bath temperature and regeneration. Opera- tional range of temperature into the pickling tanks is higher and narrower (85÷105°C) compared with ++ HCl, due to lower evaporation of H2SO4. Fe concentration may change into the incoming regenerated due to incomplete removal of iron from spent liquor because [Fe++] changes according to the regeneration efficiency and plant Operational Practices. This implies no univocal correlation between [H2SO4] and [Fe++].

++ A DOE approach has been used in a three-dimensional space [H2SO4] vs. [Fe ] vs. temperature. The investigation domain has been selected in Task 1.2, according to the operational ranges of the industrial ++ line (170÷290 g/l for [H2SO4], 20÷60 g/l for [Fe ] and 91÷101°C). The three-dimensional matrix of

experiments, shown in Figure 18, has been selected for main variables: temperature, [H2SO4] and [Fe++].

91/96/101 °C 170 210 250 290 H2SO4 20 40 60 ++ Fe (g/l) Figure 18 Matrix of conditions for DOE trials with H2SO4

An existing RCE had been modified to make an evaluation of the role of strip speed into the tanks and, generally speaking, effect of turbulence on pickling speed and over-pickling, due to strip motion. A new plastic cylinder is used as specimen holder for 2 steel samples to be pickled, Figure 19. Sample rotation will simulate strip motion and appropriate correlation was applied, if necessary; to transfer results to full scale strip/tank system.

samples

threaded hole cylinder

bottom view side view

Figure 19 Existing Rotating Cylinder Electrode and new sample holder for hot-rolled samples

Task 2.2 Determination of suitable sensor application

Suitable application at TKS-RA pickling line

BFI and TKS-RA decided to take advantage from the temperatures at the regeneration plant behind the heat exchangers of about 30°C at which the metallic material Hastelloy C2000 can be used as sensor material. According to experiences of ST with H2SO4 production, “Hastelloy” seems suitable at tem-

peratures below 60°C. The installation, however, still needs to be constructed in a way to ensure that temperatures higher than 60 °C are avoided. Therefore, a bypass must be installed for operational reasons. A schematic of the installation is given in Figure 20.

bypass

el. cond. gate / valve drain pipe socket measuring head valve / gate main pipe

US measuring head sampling head blind flange Figure 20 Schematic of aspired measurement device at TKS-RA

Consideration on suitable mixed acid application

For a mixed acid pickling system many mills use manual measurements of total metal ions, HF and

HNO3 concentration. The DAC-system of IVL would increase the sampling rate and reduce the need for manual labour.

Task 2.3 Performance of laboratory trials Results of BFI trials

The curves obtained from BFI laboratory trials, such as given in Figure 5, can be put together in a way that you receive a grid of lines representing constant concentrations of the two components regarded at a given temperature, Figure S2.

Results of CSM trials

Extended trials of laboratory experiments have been carried out at CSM for hydrochloric and sulphuric baths. Two two hot-rolled steels have been used during hydrochloric trials (steels A and B), further two hot-rolled steels have been used during sulphuric trials (steels C and D), and a cold rolled material as a reference for aggressiveness (SAE1010). Steels A and B are hot-rolled low carbon steel for drawing/deep drawing, coming from production of an industrial hydrochloric pickling line. The amount of scale on hot rolled surface is about 41.5 g/m² for steel A (FEP03) and about 43.5 g/m² for steel B (Nb micro-alloyed). An aspect of pickled samples after dipping trials to evaluate time to pickling is shown in Figure 21.

Steel A

Steel B

Figure 21 Surface of hydrochloric pickled samples

The pickling temperature behaviour is quite similar for both steels, as shown in Figure 22. Time-to- pickling data are normalized to the lower value (steel A at 90°C).

Time to pickling 4,0

3,5

3,0

2,5 y = 0,0003x2 - 0,1013x + 8,0089 R2 = 0,9922

A 2,0 B

1,5 normalized pickling time (a.u.)

1,0 y = 0,0003x2 - 0,0944x + 6,95 R2 = 0,9994 0,5 45 50 55 60 65 70 75 80 85 90 95 temperature (°C) Figure 22 Normalized time to pickling time for steel A and B vs. temperature

Examples of results of dipping tests to measure bath aggressiveness are shown in Figure 23, after a 90 s dipping into the pickling bath. Steels A and B show similar behaviour into the weaker bath, while steel A is much more attacked in the stronger bath, mainly at high temperature. As a rule the best interpolation curves are exponential form with the correlation coefficient R² >0.98.

STEEL A - HCl

120

100

80 40 80 60 120 160 200 40 weight loss (g/m2 strip) (g/m2 loss weight

0 40 50 60 70 80 90 100 temperature (°C)

STEEL B - HCl

25 40 80 20 120 160 15 200 weight loss (g/m2 strip) loss (g/m2 weight 10

0 40 50 60 70 80 90 100 temperature (°C)

Figure 23 Weight loss vs. temperature for steel A and B

Steels C and D are hot-rolled low carbon steel, coming from production of the industrial sulphuric pickling line of partner TKS-RA. The amount of scale on hot-rolled surface is about 39.5 g/m² for steel C and about 43.5 g/m² for steel D.

An example of results of time to pickling tests is shown in Figure 24, after a 90 s dipping into the pickling bath for both steels. Both steels show a time to pickling behaviour regularly decreasing with temperature, steel C having faster pickling than steel D, in all baths.

Steel C - H2SO4

170/20 22 210/20 250/20 20 time to to pickling (s) time

14 90 92 94 96 98 100 102 temperature (°C)

Steel D - H2SO4

24 170/20 210/20 22 250/20

timepickling to (s) 20

14 90 92 94 96 98 100 102 temperature (°C)

Figure 24 Time to pickling time vs. temperature for steels C and D

Examples of results of HCl pickling tests are shown in Figure 25.

Pickling time for steel B Pickling time for steel A

250 250 200 200 150 150 100 pickling time (s) 100 pickling time (s) time pickling 50 50 50 50 0 ) 70 °C 60 ( 40/130 re 0 ) 80/99 tu 70 °C a ( 120/69 er 40/130 re 90 p 80 tu 160/38 m 80/99 H te era Cl co 200/7 120/69 p nc (g 90 m /l) H 160/38 te Cl con 200/7 c (g/l)

0-50 50-100 100-150 150-200 200-250 0-50 50-100 100-150 150-200 200-250

Figure 25 Time to pickling for steel A and B in HCl

A monotonous decreasing trend of pickling time is observed with increasing temperature and HCl concentration for both steels, with some delaying effect of inhibitor. The shape of the two surfaces is quite similar for the two steels, showing a slight delay for steel B.

Examples of results of HCl over-pickling trials are shown in Figure 26.

WL for steel A WL for steel B

40 105

90 30 75

60 20 45 weight loss (g/m2) weight loss (g/m2) weight loss 30 10

15 90 90

.. .. 70 t. 70 t. ra a 0 e 0 er p p 40 m 40 m 80 te 80 te 120 50 120 50 160 160 H 200 H 200 Cl con Cl con c (g/l) c (g/l)

0,0-15,0 15,0-30,0 30,0-45,0 45,0-60,0 60,0-75,0 75,0-90,0 90,0-105,0 0,0-10,0 10,0-20,0 20,0-30,0 30,0-40,0

Figure 26 Weight loss vs. temperature for steel A and B in HCl

A monotonous increasing trend of weight loss is observed with increasing temperature and HCl concentration for both steels, with a strong decreasing effect of inhibitor on weight loss.

The shape of the two surfaces is quite similar for the two steels, showing a lower sensitivity to overpickling for steel B.

Examples of results of pickling tests for H2SO4 are shown in Figure 27.

Pickling time for steel C Pickling time for steel D

35 35

30 30 ) )

25 25

30-35 20 25-30 20 k

20-25 (s time pickling pickling time (s 15-20 15 10-15 15

91°C 91°C 10 10 ) 170/20 ) 170/20 C 96°C °C 96°C (° ( e e 210/20 r 210/20 ur tu H t H2 a 2SO4 ra SO4 er con 250/20 e conc 250/20 101°C p c (g/l) 101°C p (g/l) m m te te 290/20 290/20

Figure 27 Time to pickling for steels C and D in H2SO4

A monotonous decreasing trend of pickling time is observed with increasing temperature and H2SO4 concentration. The shape of the two surfaces is quite similar for the two steels, showing a slight delay for steel D.

Examples of results of over-pickling tests for H2SO4 are shown in Figure 28, after a 90 s dipping of scale-free samples into the pickling baths.

WL at 20 g/l of Fe++ (Steel C) WL at 20 g/l of Fe++ (Steel D)

100 170 90 150 80 ) ) 130 70 110 60 90 50 70 40 weight loss (g/m2 loss weight 50 (g/m2 loss weigth 30 30 20 101 10 101 10 ) 170/20 C ) 96 (° C e 170/20 (° 210/20 r 96 e tu r H2S ra 210/20 tu O4 c e ra onc ( 250/20 91 p H2S e g/l) m O4 co 250/20 p te nc (g 91 m 290/20 /l) te 290/20

++ Figure 28 Weight loss for steels C and D in H2SO4, with 20 g/l of Fe

A monotonous increasing trend of weight loss is observed with increasing temperature and H2SO4 concentration for both steels, with a strong decreasing effect of inhibitor on weight loss and a sligth effect of Fe++ concentration in the range 20-60 g/l.

The shape of the two surfaces is quite similar for the two steels, showing a lower sensitivity to overpickling for steel D.

Direct effect of each independent variable on output parameters (pickling time and weight loss) and their reciprocal interactions had been evaluated by using the results of DOE:

Examples of elaboration for hydrochloric and sulphuric baths are shown in Figure 29 for steel A and in Figure 30 and Figure 31 for steel C.

Figure 29 Effect of single variables on weight loss for steel A in HCl

Effects of single variables for steel C (range 210-290) Effects of single variables for steel D (range 210-290) 30 30

25 25

Fe Temp 20 Temp 20 Fe H2SO4 H2SO4 pickling time(s) pickling time (s)

15 15

10 10 L3 L2 L1 L1 L2 L3 level level

Figure 30 Analysis of DOE results: pickling time for steel C and D in H2SO4

Effects of single variables for steel C (range 170-250)

120 110 100 90 80 70 60 weigth loss (g/m2) 50 40 L1 L2 L3 Level

Temp Fe H2SO4

Effects of intersection H2SO4/ Fe for steel C

130 120 110 100 170 90 210 80 250 70 60 290

weigth loss (g/m2) 50 40 30 L1 L2 L3 Level

Figure 31 Analysis of DOE results: weight loss for steel C in H2SO4

In general, for HCl, effects are of the same order for the couple of variables in not inhibited baths, but strong reduction of the acid effect occurs in the presence of the inhibitor. This confirms a likely interaction of Fe++ with the inhibitor.

The strongest direct effect on pickling time is due to temperature, comparable effects of acid and Fe++. No significant interactions has been detected among the three variables: T, acid and Fe++.

The strongest direct effects on weight loss are due to temperature and acid, with a slight effect of Fe++. No significant interactions has been detected among the three variables: T, acid and Fe++.

The elaboration of all the experimental results of the DOE confirms, for both acid systems:

• monotonous trend of WL and PT vs. main independent variables for both acid systems • no significant interaction among independent variables in both acid systems • strong effect of inhibitor at standard operational concentration in both acid systems, and non linear trend of WL and PT vs. inhibitor concentration • quantitative differences in WL and PT for each couple of steel but very similar trends.

Further to the evident considerable effect of inhibitor, a few WL or PT vs. inhibitor concentration

(Cinib) curves have been produced for both acidic baths to explore the effect of the intermediate concentration of the inhibitor. Two curves are shown for HCl and H2SO4 baths in Figure 32.

WL vs inhibitor concentration in HCl and H2SO4

90,0

80,0

70,0

60,0 sup) 2 50,0 H2SO4 steel C HCl steel A 40,0

weight loss (g/m loss weight 30,0

20,0

10,0

0,0 0 0,2 0,4 0,6 0,8 1 1,2 inhibitor concentration (ml/l)

Figure 32 Weight loss vs. inhibitor concentration for HCl and H2SO4 baths

As expected, a non linear trend emerges of WL and PT with both the inhibitors, showing a very sharp variation at lower concentration and a sort of asymptotic trend towards the higher values.

Definition of the Index of Aggressiveness

Passing to the index of aggressiveness IAggr, it expresses the ability of the pickling system to dissolve scale and base metal, using a combination of 3 factors which contribute to form the aggressiveness. Its most general form will be:

IAggr = f (fAggr,f Inhib, fLine) (5) with

- fAggr (aggressiveness factor), an expression of the intrinsic aggressiveness of the bath calculated, according to bath conditions, by the model to be developed in Task 2.4 as a function of :[H+], [Fe++] and temperature. It is independent of steel to be pickled and its range of variation will be chosen after the completion of sulphuric data elaboration;

- fInhib (inhibition factor), an expression of the intrinsic ability of inhibitors to reduce the aggressiveness of the pickling bath. It depends of the type and concentration of inhibitor.

- fLine (line factor) is an expression of the pickling line peculiarities and could be used to tailor IAggr according line characteristics (e.g. turbulence, strip speed, use of additives, Fe+++, pre-heating).

The first and simplest approach to IAggr will include just the two main factors acting in the pickling bath:

IAggr = f (fAggr, fInhib) (6)

representing the intrinsic aggressiveness of the pickling bath (the same in all plants) plus the moderating action of inhibitor (peculiar of the product used), independent of the steel. If local effects and peculiarities of each line are wanted to be taken into account, fLine had to be introduced.

Results of IVL trials

After IVL has tested the pumping and piping system as well as pressure and temperature sensors, the next step was to test all systems including pump-control, pressure/temp sensor and the vibration sensors. Thus, the work at IVL was focused on testing the complete system, testing the system with water, make calibration trials with mixed acid and perform calibration modelling. In the water test, pressures from 4 to 7 bars were investigated, with several measurements on each pressure level and an optimal pressure level was chosen to improve the spectral quality. Principal Component Analysis (PCA) [4] was performed on the acoustic spectra, using SIMCA-P from Umetrics AB, Umeå, Sweden. The first principal components are shown in Figure 33.

20 Series (Variable Pressure_MV)

10 Outside Below Range t[2] 3,75 - 4,25 0 4,25 - 4,75 4,75 - 5,25 -10 5,25 - 5,75 5,75 - 6,25 -20 6,25 - 6,75 6,75 - 7,25 -30

-40

-50

-100 -50 0 50 100 150 t[1]

R2X[1] = 0,714139 R2X[2] = 0,18209 Ellipse: Hotelling T2 (0,95) SIMCA-P+ 11 - 2006-07-11 18:23:54 Figure 33 Principal component analysis of water test data. It can be seen that lower pressures seem to spread less than high pressure and that the constrictions seem to produce almost the same pattern except for the rotation. Note that the selected pressure 5 bar have larger marker (filled triangle), than the others pressures

From further analysis of the model producing Figure 33, 5 bar was selected for the calibration trial. For the calibration trial industrial solutions were mixed to yield an experimental domain with satisfactory coverage. The experimental designs for HF, HNO3 and total metal ion concentration are shown in Figure 34. For more information on multivariate analysis, calibration and modelling see Annex II.

The calibration was done using Partial Least Squares, PLS [5] in the software SIMCA-P from Umetrics AB, Sweden. We used both cross-validation and permutation validation (the data used for validation

were stacked in blocks and each block relating only to one unique test-run) to estimate the right model, the number of PLS-components.

HF vs Metal Metal vs HNO3

45 45 40 40 35 35 30 30 25 25 20 20 Metal [g/dm3] 15 Metal [g/dm3] 15 10 10 5 5 0 0 1,6 2,1 2,6 1,7 2,2 2,7 HF [M] HNO3 [M]

Figure 34 Overview of experimental domain in calibration trial

Below the possibility to predict total metal-ions, HF- and HNO3 content by using the DAC-system will be presented. The calibration solutions were analysed by using a SA70 Acid Manager [1] from Scana- con AB, Arlandastad, Sweden.

Model for total metal-ions with DAC

The DAC-system has a great potential to predict the metal ion content in a pickling acid. Figure S3 presents the model predicted value (x-axis) against the observed value (y-axis). The validations of the model by permutation also strongly indicate that the model is valid.

The metal ion model has a potential to work very well for control. The data the model is based on are recorded during a period of 5 seconds, which is fast enough for a quick and efficient control strategy in any acid pickling plant.

Model for HF-acid with DAC

The DAC-system can predict HF content, though the model is not as good as for the metal ion content. The permutation validation implies that the model is valid (see annex 2). Figure 35 presents the model predicted value (x-axis) against the observed value (y-axis), and it can clearly be seen that the model has reasonable prediction capacity.

Figure 35 Presents the model predicted value (x-axis) of HF acid against the observed value (y-axis). A prediction error of 0.08 M was obtained

The HF model has the potential to work well for control. The data the model is based on are recorded during a period of 30 seconds, which should be fast enough for a quick and efficient control strategy in any acid pickling plant.

Model for HNO3 acid with DAC

Figure 36 Presents the model predicted value (x-axis) of HNO3 acid against the observed value (y- axis). A prediction error of 0.09 M was obtained

The DAC-system can predict HNO3 content, though the model is not as good as the models for the metal ion and HF content. The permutation validation implies that the model is valid (see Annex III).

Figure 36 presents the model predicted HNO3 value (x-axis) against the observed value (y-axis), and it can be seen that the model has a fair/poor prediction capacity.

The HNO3 model has the potential to work for control. The data the model is based on are recorded during a period of 30 seconds, which should be fast enough for a quick and efficient control strategy in any acid pickling plant.

It can be concluded that the possibility to predict the metal-ion content and acid content in a mixed pickling-acid system run in a laboratory is fair compared to industrial conditions.

In addition to earlier modelling IVL has tried to apply the multivariate filtering technique O-PLS [4] to improve the models. The models were not significantly improved. In addition, wavelet (time-frequency analysis) de-noising [7] was done, but no improvement could be seen.

For further details and results refer also to task 1.3.

Task 2.4 Modelling, programming and prototype development BFI measurement model

BFI has designed a model to calculate two concentrations from three physical parameters (T, λel and vUS) measured that can be described as a multi input multi output (MIMO) model. A general type graphical representation is given in Figure 37. Therefore, a set of graphical representations, as given exemplarily in Figure S2, has been transformed into a mathematical model and implemented in the controller of the measurement system. Input Output

cFe

λel

cH2SO4

vUS

Figure 37 General type graphical representation of a MIMO model with three input and two output parameters

CSM modelling Index of Aggressiveness with ANN

Further to such effect of inhibitor, ANN models have been considered to more suitable to predict aggressiveness (as WL) and pickling ability (as PT) into the two acid baths, with the aim to carry out a single, not deterministic, model for inhibited and not inhibited baths. At the same time, ANN allow to 47

describe the inhibited system by means of a limited numbers of trials in comparison with the introduction of a new variable into the already developed multilevel statistical plan (5x5 for HCl and 3x3x3 for

H2SO4) and try to predict PT and WL at the intermediate concentration of the inhibitor.

Artificial Neural Network (ANN) are widely used in many and different application field. ANN is a black box because the input/output relationships extracted from process data are coded in a not compre- hensible manner on its internal structure and weights. No mathematical model can be extracted once the ANN is trained in order to provide an explanation of the physical law, Figure 38.

Figure 38 Comparison between ANN and deterministic approach The conditions for a suitable application of an ANN model are the following:

- hard definition and calibration of a deterministic mathematical model; - unsatisfactory performance of other techniques or model (i.e. Linear and Not-Linear multiple regression.

The requirement for the development of ANN models are the following:

- availability of a set of process observation to cover the range of variability of the variables.

The characteristic of ANN models are the following:

- sufficient experience about the physical context - low amount of time to carry out the model - calculation speed - reliability (meaningful and uncertainty).

The ANN models developed in this activity mainly belong to the Multi Layer Perceptron family with standard Back Propagation training algorithm (BP). The learning procedure is carried out by using the standard back-propagation algorithm which finds the best combination of weights and biases of the nodes by trial and errors. This procedure generally uses two sub-sets of the original database, one for 48

training, the other one for testing the current set of coefficients and increasing the efficiency of the learning process. A particular attention must be paid in avoiding the so-called over-fitting condition, where the network is so much flexible that tends to fit the noise in the original data, loosing the capability of generalizing and correctly reproducing the underlying phenomena.

The values of WL and PT coming from the previous curves at variable inhibitor concentration, together with all the values inhibited and not inhibited from DOE trials, had been used as an input to make a first approach to the Neural Network, developing separate ANNs for WL and PT of each steel: in H2SO4 and HCl.

Being all the DOE experiments made in double o triple repetition, one experimental data set has been used to train the network and the remaining to test and to drive the definition of the best ANN model.

The performance of the ANN models developed first is promising but do not satisfy all the specifica- tions for an exhaustive predictive tool for PT and WL. The determining factor is the low amount of experiments carried out with inhibitor at the intermediate concentrations (between 0 and 1 ml/l), and few amount of sample. In order to solve such limitation, a uniformly distributed concentration set is needed to describe WL and PT in the range of variation, to obtain a reliable description of the Cinib effect on the two independent variables.

So some extra sets of inhibited trials had been carried out for both acids, with variable Cinib, to obtain further input data for the ANN, to improve their reliability. The process condition for the new inhibited trials had been chosen, within the former DOE plan, in order to be representative of different operational situation. A uniformly distributed set of intermediate Cinib has been introduced within the original

0-1 ml/l range, to explore the effect of its concentration in both HCl and H2SO4 baths.

The bath conditions for the new inhibited trials had been chosen within the former DOE plan, in order to be representative of operational situation, as shown in next Table 11.

Table 11 New sets of trials with inhibitor

acid conc (g/l) Fe++ conc (g/l) temp (°C) Inhibitor conc (ml/l) HCl 120 70 80 80 90 90 160 38 70 0 - 0,2 - 0,4 - 0,6 - 0,8 - 1 200 7 60 200 7 80

H2SO4 250 60 96 290 20 101 250 40 96 0 - 0,3 - 0,6 - 0,9 - 1,2 210 60 101 170 40 91 210 20 96

The new results have been added to the all the previous sets and used to develop new ANNs for: WL in

H2SO4, WL in HCl, PT in H2SO4 and PT in HCl.

At first being available more experimental data, inhibited and not, an attempt had been made to compare and try to correlate the results of the trials for each couple of steels: A-B for HCl baths and C-D for

H2SO4. The correlations for each couple are shown in Figure 39, here and in Figure 74, to Figure 76 in Annex I. Significant R2 values had been obtained for WL and PT in the two acid systems.

Figure 39 Correlation of the pickling time between steel A and B

These correlations allowed merging the results of each couple of steels (A-B and C-D) to develop just a single ANN for each acid system, after the transformation according with the shown correlation. So,

four new ANNs have been developed: for WL in HCl, for PT in HCl, for WL in H2SO4 and for PT in

H2SO4.

Artificial neural networks (ANN) are composed of many simple processing units, the so-called nodes, arranged in layers. The first layer represents the input data, the last one the outputs. One or more “hidden” layers can be placed in between. Each node in one layer is linked to all nodes in the next layer by weighted connections which are adapted during the learning process carried out to fit the set of data provided in the training stage. In addition, non-linear transfer functions are used to calculate the output signal from each node, thus enhancing the predictive capabilities of the whole neural structure. Artifi- cial neural networks are classified in family by considering the learning algorithms and the internal structure.

The ANN models developed for the prediction of Weight Loss (WL) and Pickling Time (PT) values belong to the Multi Layer Perceptron family with standard Back Propagation training algorithm. The network is fed by inputs linearly normalized between 0 and 1 with respect to their range in the data set. Similarly, the normalized outputs are de-normalized after the calculation to obtain the network prediction.

For a generic node “j” in the hidden or output layers, the output value (uj) is calculated by summing up all the outputs from the previous layer (ui), multiplied by their respective weights (wji) adding a constant bias term (θj) and then using the transfer function “S” with sigmoidal shape as follows:

uj = S[∑(wji* ui) +θj]

In the present case the logistic function

S(x) = 1/(1+e-x) has been used as transfer function, which operates in the range between 0 and 1.

The learning procedure for each ANN was carried out by using the standard back-propagation algorithm which finds the best combination of weights and biases of the nodes by trial and errors. The root mean squared (RMS) error had been used as indicator of the consistency of the network with the experimental data. A special attention had been paid in avoiding the over-fitting condition, where the network is so much flexible that tends to fit the noise of the original data, loosing the capability of generalizing and correctly reproducing the underlying phenomena.

Four independent ANNs had been realized to constitute the core of VSA and VSP:

• WL_AB and PT_AB to predict, respectively, weight loss and pickling time into hydrochloric baths • WL_CD and PT_CD to predict, respectively, weight loss and pickling time into sulphuric baths.

According to the plan of experiments carried out, four independent variables had been considered as input parameters: bath temperature (T), acid concentration (H), Fe++ concentration (Fe), inhibitor concentration (I) and two dependent variables had been considered as outputs: weight loss (WL) or pickling time (PT). To each group of four values of the input variables is associated one value of the output variable PT or WL.

From the data base containing the results of the experimental trials, four sets of input/output data had been prepared for each ANN to be developed. Each set has been divided into two sub-sets, one for training the ANN, the other for testing the current set of coefficients and increase the efficiency of its learning process.

Half the experimental data had been included into the training set, the reminder into the test set. Finally all the available input/output data had been used for further training and improving ANN performance, maintaining the same structure (nodes) and cycles of iteration (epoch).

In Annex IV a detailed description of the neural networks used to develop the virtual sensors is given.

The predicted values coming from the new four ANNs are shown in Figure 40 and Figure 41, here and in Figure 77 to Figure 82 in Annex I, as correlation and comparative curves among measured and calculated values of WL and PT.

Figure 40 Correlation between measured and calculated PT values in HCl baths (steel A and B)

Figure 41 Comparison among measured and calculated PT values in HCl baths (steel A and B)

Generally speaking, the correlations among measured and calculated values are significant. Neverthe- less same shift among measured and predicted values, the ANN can be usefully applied to predict the bath behaviour in terms of WL and PT.

The predictive tools (four ANNs) for aggressiveness and pickling-ability of the pickling baths are propaedeutic to the operational Indexes of Aggressiveness and Pickling ability to be developed in WP4.

IVL measurement model

The findings discussed by IVL under task 2.1 were considered in the design and construction of the DAC measurement system. Major efforts have been made in the measurement system design phase to select materials that withstand the harsh pickling solution. Mechanical drawings of an improved measurement design have been done, where the ability for easy transportation is considered. Computer and electronics casing suitable for the environment has been considered. Another option is to place com- puters and electric equipment in a regular SCADA connectivity room. In Annex II a schematic drawing of the measurement chain for the DAC is shown.

First the design of the key components in the DAC-system has been developed. The schematic piping was set, which also included the functionality of the valves. The pickling system that has been used is mixed acid with HNO3/HF. The piping was system in polypropylene; this includes the tank in the system. The only metal part in contact with the pickling acid is the constriction has been tailored in Hastel- loy C22 [8]. Several pump manufactures were contacted in order to obtain a pump that is resistant enough to corrosion, gives a low pulsation value and fairly good pressure range. Common for most centrifugal pumps in plastic materials is that their maximum pressure is 0,5-0,6 MPa. Another objective was to find a pump that works with different pickling acid types. One of the first suggestions from pump manufactures was a large centrifugal pump. However we did not want a large flow and large

electric motor that may cause additional vibration. Such a pump would require a pulsation damper and finally the price was high. After extended inquires we decided to order a membrane piston pump in suitable plastic material and ceramic valves and valve springs in Hastelloy 276 [9]. It was the best compromise between the complex objectives. The only point we were not fully convinced on was the dura- bility of the valve-springs. Therefore we ordered spare springs to have in our own storage. Figure 42 shows the schematic drawing of the piping system and sensors.

6 To pilot plant / Indus- trial plant Inlet for laboratory trials and rinsing VT32 5

VT31 7 LT1

PT22 4 VT2

TT22 From pilot plant / Indus- 3 1 trial plant

2 0 TT21

SC21

Figure 42 Schematic drawing Pickling Acid DAC-system

Valve 0-2 and 4-6: Manual valves, designed to be open or closed Valve 3: Throttle valve TT: Temperature sensor PT: Pressure sensors SC: Speed control VT: Vibration sensors LT: Level sensor (not installed)

The tank presented in Figure 42 is made in plastic and has volume of 10 dm3. The normal flow is 1-2 l/min measured at valve 5. In the system built, valve 3 is only a safety valve. The plastic piping system and the system foundation was built by firm specialised in plastic piping. The design of the constriction was made by IVL and manufacturing by a mechanical workshop in Hastelloy C22. Additional software was programmed for control of the pump. For an overview of built system see Figure 43. In the laboratory the system was mounted on a tray with rubber wheels. Pressure sensors, temperature sensors, vibration sensors and transmitters were mounted. A first test of the DAC equipment was performed by pumping water.

Figure 43 DAC Equipment at IVLs laboratory, sensors where mounted after this image was taken

IVL has further developed the software, written new program modules to efficiently control the measurement equipment for fast measurements and changed data file format for easy use of data in multivariate analysis. Some program modules were tested separately before inclusion in the main program while others were directly included. The software was tested in total by running the equipment, both by looking for flaws in the performance during the run and afterwards by checking the files created (see Annex II). The computer and data acquisition platform has been modernised from PII400 with NT to P4 3200 with XP SP2, which substantially increased the computing power as well as the system stability. Additionally we have installed two hard drives in SATA Raid configuration, which makes the data storage more secure. The platform is remote controllable over regular network or if needed over mo- dem.

Task 2.5 Establishment of communication with modular process control tool (PCT)

From a systems view one of following mechanism could be used for data exchange:

- Using a database in combination with open connectivity like OPC [10]. - Using a database with one master database importing data from different system by means of text-files and file-transfer. - Using a database where data are collected by a DAQ-card as electrical signals.

The last point is probably easiest to implement fast in this project, were needed. However, if the solutions in this project would be commercialised, the first point would be the recommended to be the first to implement.

From a BFI/TKS-RA point of view on data that needs to be processed, following requirements are given:

- Five channels are necessary for pickling process (concentration) data during development. - Later only two (concentrations) or three (temperature if not available from other sources) are required. - Further read-only channels for production data processing will be necessary. - Considering pickling process dynamics, rates of about 1 sample/min will be sufficient. However, SCADA equipment is capable of recording rates up to 1 sample/sec.

Within the project the partners originally aspired to develop a process control tool. It became clear that there is no practical way to develop such tool here. Instead, communication interfaces in general have to be tailor-made for each specific plant, e.g. Acceralia and TKS-RA. Every plant has its grown industrial IT infrastructure and therefore has different needs when connecting new systems to old systems. Un- iOvi, BFI and TKS-RA established necessary communication using interfaces given at the plants, according to task 6.3.

Task 2.6 Documentation, Dissemination and Education

During the entire project dissemination and education has been performed. BFI has presented part of the project results at the CETAS conference, held on 16.-18.05.2006, in Luxembourg, [11]. IVL has presented their technology mainly to Swedish SMEs, Steel manufactures and other industries. This has been done through direct talks and conferencing/lectures. These activities have continued till the end of the project and will continue after the project.

2.3.3 WP 3: Detailed on-site sensor-based analysis of operational pickling baths

Involved Partners: BFI, TKS-RA and IVL worked an all three tasks.

Objectives of WP 3

In this work package, the main objective was to acquire operational data using the newly developed sensors. This has been accompanied by further laboratory trials and analyses to review optimum pickling parameters.

Task 3.1 Preparation of operational measurements

Measurements at TKS-RA

This task is highly related to task 2.2 in WP 2. According to considerations over there, the partners have installed a measurement section as represented in Figure 20, including a bypass installed for operational reasons to avoid temperature peaks. Besides, a bypass assures comfortable maintenance access to the

sensors. BFI and TKS-RA therefore installed an operational measurement section over there, Figure 44.

SonicWork System

LiquiSonic controller Power/AI Sensors RS232 RS232 COM1

EC controller Sensor Power/AO 4-20mA 24Vdc

EC US / T sensor sensors

Pickling solution Flow: 25m³/h

Pipeline with pickling solution

Figure 44 Installation of operational measurement device at TKS-RA in the feed pipe of the crystalli- zation section

Trials at IVL

IVL operational measurements using SA70 [1] were made in order to measure the raw metal-ion content and the acid concentrations before mixing the samples in the calibration trial. Operational measurements were also made in the laboratory to ensure that pressure and temperature sensors showed accurate values.

In order to prepare for an offer to the stainless steel producers, IVL decided to run the DAC system with water to check if it worked in the same way as with mixed acid. Unfortunately, the system was not as stable as last time due to varying pressure. These facts lead to a decision to trouble shoot the system; checking the constriction (no corrosion damages), the pressure gauge (ok) and finally the pump. It was found that some of the ceramic valves had almost dissolved. Since IVL were recommended these materials by the pump dealer, a complaint was made to them. After two months IVL got a reply from the firm that manufactured the pump through the pump dealer, recommending a change of the valves, springs and seals to corresponding parts made in Hastelloy 276 [9]. Since it is essential to have a functioning system to show to interested industries IVL decided to buy these components at a strongly reduced cost.

Taks 3.2 Performance of operational and laboratory measurements Measurements at TKS-RA

After the successful installation of their ultrasound based concentration measurement device at the crystallisation plant of TKS-RA data obtained from the system has been recorded continuously. A graphical representation of a part time recording of the physical parameters ultrasonic velocity and electric conductivity is given in Figure 45. Graphical representation of the concentration obtained for implied model implemented in the controller of the same period is given in Figure S5.

1635 700

1630 650

1625 600

1620 550

1615 500

Ultrasonic velocity m/s in velocity Ultrasonic 1610 450 Electric conductivity in mS/cm in conductivity Electric

1605 400

1600 350 01.12. 01.12. 02.12. 02.12. 03.12. 03.12. 04.12. 04.12. 05.12. 05.12. 06.12. 06.12. 07.12. 07.12. 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 00:00 12:00 Time Ultrasonic velocity [m/s] electric conductivity [mS/cm]

Figure 45 Typical time series of physical parameters from operational trials at TKS-RA

Since the operational attended testing has started BFI regularly took samples from the pickling liquor to compare the values of the online monitoring with analytical titration results. A comparison of values taken from the controller and corresponding analytical results is shown in Figure 46. It can be seen that the deviation of the online monitor results and the analytically obtained results is rather little. How ever a certain modification (calibration) of the implied model seemed adequate.

22,0 y = 1,0111x R2 = 0,6498 21,5

21,0

20,5 y = 0,6715x + 6,6308 R2 = 0,8738 20,0 Titration

19,5

19,0

18,5

18,0 18,0 18,5 19,0 19,5 20,0 20,5 21,0 21,5 22,0 Controller Reihe1 mögliche 2-Pkt-Kalibration alternative 2-Pkt-Kalibration durch 0

Figure 46 Comparison of concentrations from operational trials and mobile analysis

To show the feasibility of the model in general BFI has analysed both physical parameters as well as obtained concentrations from the model statistically to show that the whole range of values that should be covered by the model is represented during the individual measurement campaigns. Figure 47 therefore shows a diagram of the number of pairs of physical parameters (left) observed and recorded in the period represented in time series before. In the same figure similarly the frequency of pairs of concentration (right) obtained from the model is shown. It can be seen that a wide range of both physical parameters and concentration pairs are represented so that the general feasibility of the model is proved.

600 22

550

20 on i

500 -concentrat Electric conductivity Electric

H2SO4 18

450

400 16 1600 1610 1620 1630 1640 3456 Ultrasonic velocity Fe-concentration

Figure 47 Typical domains of physical parameters (left) and concentrations (right) from operational trials at TKS-RA. Diameter of the spots in the different domains represents the relative number of pairs from that domain.

Further more BFI has inspected the sensors immersed into the pickling medium regularly after periods of one month up to nine month of immersion. A picture of the sensors taken from the measurement device after one month of immersion can be seen in Figure 48.

Both sensors showed only very little black coloured, easily removable coverings that probably result from carbon depositions transported hydraulically with the pickling liquor. No corrosive attack, in particular at the metallic ultrasound sensor, has been observed, even after such long periods of immersion.

Besides, TKS-RA recorded some fluxes manually that are not recorded automatically, i.e. the overall

⋅ ⋅ circuit flow rate V Total , the fresh acid addition flow rate V Acid and the condensate addition flowrate

⋅ V cond that were correlated with production and concentration data, later, for control concepts.

Figure 48 Validation of sensor heads (left: ultrasound velocity, right: electric conductivity) immersed in pickling liquor at TKS-RA for one month

From the experiences made during operational testing the application of the ultrasound based concentration measurement device seems very promising for sulphuric acid pickling liquor.

Trials at IVL

IVL has made visual inspection and photographic documentation of damaged pump parts (see Figure 49 for the damages on the ceramic pump parts). Further IVL has verified that the pump vendor’s selection of materials for the new parts replacing the ceramic parts were accurate. The DAC-system is now functioning as good as before the break-down.

Figure 49 Upper left heavily dissolved ceramic valve-head, upper right slightly affected valve-head although miss-colouration and lower to the left a partly dissolved sealing ring

2.3.4 WP 4: Review of optimum pickling process parameters, programming of process control tool

Involved Partners: BFI, TKS-RA, CSM and UniOvi worked on all tasks.

Objectives of WP 4

In this work package, the main objective is the development of a virtual sensor for application to the pickling bath based on a previous model, whose training variables were established in WP1. This virtual sensor should predict some key parameters to improve the plant operation. In this way, programming of simulation modules and a graphical user interface are necessary.

Task 4.1 Preparation of process control tool development

UniOvi, mainly devoted to this WP took advice from intensive discussions with TKS-RA, CSM and BFI, which resulted, finally, in the definition of the actual aim of UniOvi, to provide with a model (or tool), capable of calculating set-points of strip speed in the pickling section. This shall be done regarding (among others) steel type, acid concentration and experience with former processing results, which have been reviewed for this task.

In total UniOvi acquired a number of 32391 inspected coils as data set. These coils were manufactured between January 2005 and October 2005 in line No 2 of the pickling line at Aceralia facilities in Avilés (Asturias-Spain). 61

The number of overpickling defects was quantified. Also the number of underpickling (or bad pickling) defects, which are considered as scale residues on the edge of the strip, are quantified. Both of them, overpickling and underpickling defects, are quantified using an automatic inspection system, which was described in task 1.2.

The considered number of defects has been computed using the length of the coil and the defect severity ratio by means of equation (2). In case of underpickling or overpickling as global defect, the values of the defect severity ratio are between 1 and 6. If these global defects don’t take place, this ratio is equal to 1.

The main improvement of the data set was the inclusion of coils which have not global defect, not seri- ous defect was found that is. Although most of the coils have not global defect, the training data set contains coils which have global defects of every kind.

The data are filtered taking into account that the final training data set must assert the following conditions:

• The line shutdown time must be less than 16 seconds. • The average line speed must be less than 120%. • The acid concentration in tank 3 must be greater than 60 g/l. • The strip thickness must be equal to 2.1 mm. The initial data set has also other thickness values but its number of samples is not enough to obtain a reliable model.

The objective taken to be developed in the task 4.2 is to estimate the optimum line speed that minimize as many overpickling defects as underpickling defects for each acid concentration, thickness and type of steel. An optimum line speed is computed in function of the process values. A Self-Organizing Map is proposed using a validation technique to obtain this optimum line speed. The training variables are the line speed, the acid concentration in tank 3, the number of overpickling defects, the number of underpickling defects and the line shutdown time. Also, the cooling temperature at the end of the hot rolling mill line is a key process variable but it has not been taken into account because is almost constant for the same type of steel. Thus, a data set was formed for each type of steel. Specifically, three data sets were obtained representing the three types of steel that have a high number of samples, shown in Figure 50 to Figure 52. This developed work is described in the next section.

120

100

60 LINE SPEED (%)

0 70 75 80 85 90 95 100 105 110 115 120 ACID CONCENTRATION IN TANK 3

Figure 50 Underpickling and overpickling defects for type of steel A

120

100

60 LINE SPEED (%)

0 70 75 80 85 90 95 100 105 110 115 120 ACID CONCENTRATION IN TANK 3 Figure 51 Underpickling and overpickling defects for type of steel B

120

100

60 LINE SPEED (%)

0 75 80 85 90 95 100 105 110 ACID CONCENTRATION IN TANK 3 Figure 52 Underpickling and overpickling defects for type of steel C

Preparation of VSA development

The flow-chart leading from the lab trials up to the Virtual Sensors is shown into Figure 53.

A/BHCl ------H2SO4 C /D

Selection of relevant parameters HCl and H2SO4

LAB trials DOE (WL - PT)

Predictive models Predictive models for WL for PT ANN

Single predictive Single predictive ANN for A/B model for WL model for PT and C/D

Index of Index of Pickling Aggressiveness Ability A - P

algorithm for a algorithm for a multi-tank system multi-tank system K - k

Global index of Global index of Aggressiveness Pickling Ability AG - PG

Virtual Sensor of Virtual Sensor of Aggressiveness Pickling Ability VSA - VSP

Operational data base

Figure 53 Flow-chart of the pathway to design the virtual sensors VSA and VSP

After the completion of the experimental trials, the next step consists of the development of predictive ANN models which correlate process conditions and aggressiveness/pickling-ability of the baths. The models predict pickling time and weight loss in each tank, starting from the operational conditions of the pickling bath.

Separate models had to be developed for hydrochloric and sulphuric baths, also in the presence of inhibitor. The values predicted by the ANNs are used to calculate two operational indexes: Index of Ag- gressiveness (A) and Index of Pickling ability (P), independent of steel class.

Two global indexes PG and AG will be derived from A and P to predict aggressiveness and pickling- ability in a multi-tank pickling line.

The indexes A. P, AG PG will be the main constituent to design the architecture of the Virtual Sensor of Aggressiveness VSA and the Virtual Sensor of Pickling ability (VSP), aimed to predict the total aggressiveness/pickling-ability of the pickling baths in a multi-tank line.

Task 4.2 Programming efforts At TKS-RA, BFI advised how to implement concentration data from the prototype measurement system to their statistical process control and visualisation. Necessary programming was performed by external staff, guided by both partners.

Introduction to SOM algorithm:

UniOvi used Self-Organizing Maps (SOM) to visualize the data structure. The SOM [12] is a neural network algorithm, which is based on unsupervised learning. It consists of an array of neurons arranged in a grid. SOM training associates with each neuron, i, a prototype vector mi=[mi1, mi2,…, mid] where d is the dimensionality of the input data. A correspondence is established between the coordinates of each neuron in the input space (prototype vectors) and their coordinates in the grid or output space. The objective is to find a suitable set of prototype vectors for each unit so that the network models the distribution of the input data and therefore in the output space.

The general method of training is the classical one. In a first stage, a selection of the most significant variables has to be carried out. These variables are the line speed, the acid concentration in tank 3, the number of overpickling defects, the number of underpickling defects and the line shutdown time, the strip thickness and the type of steel. In a second stage, data were normalized to a zero mean value and a unitary variance. This allows all the features to be treated by the SOM in a same way. After normalizing the process, an SOM network was trained with these variables using a sequential training algorithm. After the training process, the SOM is expected to capture the inherent geometry of the process data, allowing it to be displayed in 2D representations such as the component planes. These planes are built using gray or color levels to show the value of a given input feature for each SOM unit in the 2D lattice.

Validation measures and map size:

According to the properties of the SOM the trained neural network must achieve the topology preservation of the data. Therefore the neighborhood on the model and on the input space must be similar. If two prototype vectors close to each other in the input space are mapped wide apart on the grid, this is sig- naled by the situation where two closest best matching units of an input vector are not adjacent units. This kind of folds is considered as an indication of the topographic error in the mapping. The topographic error can be calculated [13] as the proportion of sample vectors for which two best matching units are not adjacent.

There are many others indexes for topology preservation measurement that are based on the same distribution of distances between the data samples in the input space and the neurons in the output space such as the topographic product [14], Bezdek error [15] and Zrehen measure [16]. However, a new index [17] is proposed taking into account the smooth trajectory of the process state projection over the map under non abrupt changes of the process variables. Then, the regions of the output space can be identified as different process zones from a operating point of view. In fact, this important property makes SOM algorithm a powerful tool for supervision of multivariable processes. The proposed index is calculated by means of a trajectory on the output space (6) obtained from the best matching neurons corresponding to a variation of a process variable overall its range keeping constant the rest of variables of the data set. The number of nodes along the trajectory, which is to say, the number of best matching neurons N is calculated. The higher the number of nodes the better is the resolution of the map. The number of directions (north, south, west, east, northwest, northeast, southwest and southeast) that is taken along the trajectory from node to node are considered. If opposite directions are taken, the index must be incremented to be penalized. Also the higher the number of directions taken along the trajectory, the higher is the index.

Ns Trajectory = + Nc + No + 5 ⋅ d (7) index N where No is the minimum of movements in opposite direction (north vs. south, east vs. west, southwest vs. northeast and southeast vs. northwest). Ns is the number of steps taken incrementing the process variables. Nc is the number of directions taken along the trajectory. It will be a value between 1 and 6. In both of them, No and Nc, the intention is to minimize them, whereas the number of best matching neurons N taken along the trajectory must be maximized to improve the map resolution. The average Euclidean distance from one node to other node along the trajectory is calculated as d. The higher this mean distance between nodes, the higher the index. This has been done to avoid sudden jumps and they must be penalized.

Moreover, the prototype vectors try to approximate to the data set. A consequence of this approach is the resolution error or the quantization error. To measure the resolution of the mapping the average quantization error (7) over the whole testing data set is usually used. N is the number of samples, xi is th the i data sample and mb is the prototype vector of the best matching unit for xi.

1 N eq = ∑ xi − mb (8) N i=1

The number of map units determines the accuracy and generalization capability of the SOM. The bigger the map size the lower the quantization error but the higher the topographic error. This is due to the neural network folds to reduce the quantization error. Moreover, the bigger the map size the higher the computational cost. Therefore, there is compromise between the increase of the topographic error and the reduction of the quantization error.

The next step consists in calculating the ratio between the number of rows n1 and the number of col- umns n2, Figure 54. This ratio between the map sidelengths [18] is the square root of the ratio between the two biggest eigenvalues of the Principal Components Analysis applied to the training data (8). The biggest eigenvalue is e1 and the second is e2.

n1 e = 1 (9) n2 e2

Figure 54 The calculus of the map side lengths is based on the number of samples and the eigenvalues of the data matrix SOM-Results

Forty SOM models were trained for each type of steel. The number of neurons for each map and its dimensions are shown in Table 12. The initialisation of the weights has been done randomly and the results seem to probe that the quantization error does not depend on the initial values of the weights. However, the topographic error depends on the initialisation of the weights. The best model has been chosen in function of the proposed index for topology preservation.

Table 12 Dimensions of SOM models for each type of steel

Type of steel Map size Number of neurons Number of data samples A 33 x 24 792 1670 B 26 x 20 520 1012 C 26 x 20 520 1009

The topology preservation property implies an important relationship to project the current values of the process variables. Similar values of these variables correspond to winning neurons that are neighbor each other on the lattice. Therefore, while the process state does not vary abruptly, its projection also will not change abruptly on the map. If this property were not preserved, the projection of the process state in the map would follow random and chaotic trajectories, disabling the process monitoring. Thus, the proposed index is used to check the topology preservation because of its importance to validate the developed model.

The objective is to estimate the optimum line speed that minimize as many overpickling defects as underpickling defects for each acid concentration, thickness and type of steel. The trajectory of the process state over the SOM lattice was obtained varying the acid concentration and is shown in Figure 55. Each node of the trajectory represents a best matching neuron under those conditions. This wining neuron is calculated taking into account that the prototype vector must assert the conditions determined by the values of acid concentration, line shutdown time approximately equal to zero (less than 1 second) and minimizing the sum of overpickling and underpickling defects.

Figure 55 Trajectory obtained under variation of de acid concentration, non shutdown line and mini- mization of the overpickling and underpickling defects

The values of the line speed component plane which belongs to that trajectory will give the values of optimum line speed to achieve the proposed estimation. In this way, the graphics of optimum line speed versus acid concentration has been obtained for each type of steel and thickness equal to 2.1 mm. It must be known that the standard range of acid concentration in tank 3 is from 80 g/l to 100 g/l. These graphics are shown from Figure 56 here and Figure 83 and Figure 84 in Annex I.

Figure 56 Optimum line speed obtained with the purposed technique for type of steel A 69

FLSOM Programming efforts

Later, a modification of the Fuzzy Labeled Self-Organizing algorithm (FLSOM) has been tested with the available dataset from the pickling plant. In this way, some improvements have been done in the neural network training. The main difference between SOM and FLSOM is that the first one is unsupervised and the latter is semi-supervised, it means the user knows the degree of membership to classes from each data vector. The advantage of fuzzy membership is that the degree of membership can be specified rather than just the binary and especially advantageous if patterns are not clearly members of one class or another. In a graphic display, it means that if SOM is used, each neuron has the same color intensity within the same class. However in FLSOM the color intensity of each neuron within the same class is different because of fuzzy class labeling (each neuron represents its own class typically).

Data preparation

The following constraints were taken into account during the data acquisition from the data set:

• One type of steel with a significant number of coils was selected and the strip thickness was equal to 2.1 mm. • Regarding the data set corresponding to coils without global defect, i. e. neither overpickling coils nor underpickling coils, the temperature in the fourth tank must be greater than 65 ºC and the line shutdown time must be less than 1 second. We have also considered only the coils with an available measurement of the cooling temperature at the end of the hot rolling line. • The data set of the overpickling coils was collected for coils with a line shutdown time less than 10 seconds. • The temperature must be greater than 65 ºC for the data set of underpickling coils.

All the underpickling and overpickling defects are divided by its coil length in order to normalize those variables. Besides this division, the underpickling defects in the underpickling coils and the overpickling defects in the overpickling coils must be penalized by the defect severity ratio according to what said in the previous sections.

The process variables that were acquired to train the neural network were the number of overpickling and underpickling defects, the line speed, the acid concentration in tank 4 and the temperature in tank 4.

FLSOM training

We have used a Fuzzy Labeled Self-Organizing algorithm [19] trying to improve the training algorithm. In this algorithm, the classification task influences the values of the prototype vectors and both of them take place at the same time during the training. In this way, FLSOM can be considered as a semisuper- vised algorithm. We have modified the original algorithm to update individually the kernel radii according to Van Hulle’s approach [20]. In this way, a significant reduction of the mean quantization error of

the numerical prototype vectors is achieved. This has been corroborated with some datasets from the UCI [21].

Equation (10) represents the numerical prototype wi updating and its second term represents the labeling or classification error of the labeling prototype vectors yi with regard to the probabilistic vectors x sup- plied by the training data set, whereas the first term represents the approximation of the numerical prototype vectors to the numerical data vectors v. Equation (11) is the labeling prototype vector yi updating, equation (12) determines the kernel radius σi updating, equation (13) is a monotonous decreasing function of the lattice distance from the winner neuron (in this case is a Gaussian), equation (14) is used as a neighborhood cooling term and its initial value is σΛ0, and equation (15) is a Gaussian kernel in the input data space so that the prototype vectors w close to the data vectors v determine the classification task.

v − wi 1 2 Δwi = α w (1− β )Λ(i,i*,σ Λ ) 2 +α wβ 2 (v − wi ) x − yi (10) σ i 4γ

Δyi = αl βgγ (v, wi )(x − yi ) (11)

2 1 ⎛ v − w ⎞ Δσ =η Λ(i,i*,σ ) ⎜ i −1⎟ (12) i σ Λ σ ⎜ dσ 2 ⎟ i ⎝ i ⎠

⎛ r − r 2 ⎞ Λ(i,i*,σ ) = exp⎜− i i* ⎟ (13) Λ ⎜ 2σ 2 ⎟ ⎝ Λ ⎠

⎛ t ⎞ ⎜ ⎟ (14) σ Λ (t) = σ Λ0 exp⎜− 2σ Λ0 ⎟ ⎝ tmax ⎠

⎛ v − w 2 ⎞ g (v, w ) = exp⎜− i ⎟ (15) γ i ⎜ 2 ⎟ ⎝ 2γ ⎠

where β is a weight to adjust the numerical approximation versus the labeling classification, αw and αl are the learning rate for numerical and labeling prototype vectors respectively, ησ is the learning rate for kernel radii, d is the number of numerical variables, t is the time step, tmax is the maximum number of time steps, ri and ri∗ are the lattice coordinates of the updated neuron i and the winning neuron i∗, respectively.

Each of these probabilistic data vectors, which is associated with a numerical data vector (in our case a coil), is assigned in a fuzzy way to the clusters of the data set according to a probabilistic membership. This stage is critical in the data preprocessing, but obviously the algorithm can be applied to crisp dis- tributions. 71

The data samples (or coils) can be classified into three groups or clusters:

1. Overpickling coils with overpickling as global defect.

2. Underpickling coils with underpickling as global defect.

3. Coils without global defect. Although these coils don’t have global any global defect, they might have any number of overpickling and underpickling defects.

This classification, not only the training variables, influences the values of the prototype vectors (estimated process variables) during the training. In this case, the classification is a crisp distribution. Al- though the option to employ a fuzzy distribution is very interesting, it requires a critical process understanding and it should be debated. The number of coils without global defect is very high (1418 coils) in comparison with the number of overpickling and underpickling. It is necessary to obtain a significant number of these coils to carry out a reliable analysis because the SOM map uses a number of neurons to map a certain working zone of the process that is proportional to the number of samples in the training data set. In this way, the number of coils without global defect must be reduced and 140 coils were randomly chosen with that purpose. The number of overpickling coils was 53 and the number of underpickling coils was 22. The total number of samples (or coils) of the training data set was equal to 215. In this way, the data set was considered as a crisp distribution and is composed of 215 instances, 5 numerical variables (overpickling defects, underpickling defects, line speed, acid concentration in tank 4 and temperature in tank 4) and 3 labelling variables (correct coils, underpickling coils and overpickling coils).

Random selection of coils that belong to the majority class (coils without any defects) has been done and the results have been reported. Although this procedure could cause a lack of information, a mini- mal modification of the algorithm can allow to be trained using the whole dataset. A recent research [31] has demonstrated that FLSOM with different rates is a suitable tool dealing with imbalanced datasets and successful results have been obtained compared with two previous works in the topic literature. Assigning two different values in the learning rate to the prototype vectors depending on the labeling data vector associated with the training data vector, can improve the accuracy to the majority class (specificity) and to the minority class (sensitivity). In this way, the main idea of the procedure proposed is still valid.

The initial values of the prototypes were randomly initialised. The map size was a 30x30 lattice. 40 maps were obtained and the most appropriate (or mean of the best ones) might be considered to estimate the optimum line speed. The number of epochs was equal to 30 and the values of the parameters were β = 0.5, αw = 0.01, αl = 0.5, γ = 0.5, ησ = 0.0001· αw, σΛ0 = 4, tmax = 29025.

As stated in previous reports, the objective taken to be developed in the task 4.2 is to estimate the optimum line speed that minimizes as many overpickling defects as underpickling defects for each acid concentration, thickness and type of steel. In this case, an optimum line speed is computed in function not only of the process values, but also taking into account the previous classification of the coils with respect to their global defect. This optimum line speed minimizes the prototype vectors of the component planes corresponding to the overpickling and underpickling defects, as well as the labelling prototype vectors corresponding to the estimated classes of overpickling and underpickling global defects.

The training variables were indicated above. The cooling temperature at the end of the hot rolling mill line is a key process variable but it has not been taken into account because is almost constant for the same type of steel.

The trained maps are shown in Figure 85 and Figure 86 in Annex I. One trajectory on the output space is formed by the sequence of the best matching units varying the acid concentration, minimizing the defects and corresponding to a certain temperature, see Figure 87 in Annex I and Figure S6, in the summary section.

The values of the line speed component plane which belongs to a selected model according to the topographic index give the values of optimum line speed to achieve the proposed estimation. In this way, the graphics of optimum line speed versus acid concentration has been obtained for this type of steel and strip thickness equal to 2.1 mm. It must be known that the standard range of acid concentration in tank 4 is from 90 g/l to 150 g/l. This graphic is shown in Figure 57.

Figure 57 Estimation of the optimum line speed with 74

ANN for Index of Aggressiveness

The WL and PT values predicted by the four ANNs constitute the input data to calculate two operational indexes: the Index of Aggressiveness (A) and the Index of Pickling ability (P), independent of steel class and peculiar of the chemical nature of each pickling bath.

The former expresses the pickling ability of the bath towards scale; the latter expresses the aggressiveness of the bath towards base metal.

The ANN models predict pickling time and weight loss in each tank, starting from the operational conditions of the pickling bath, and allow to calculate A and P in each tank.

Looking at the ranges of PT and WL values coming from each ANN (minimum-maximum), the two indexes A and P have been drawn by grouping the results in bands of aggressiveness or pickling ability. The size of each zone derives from a compromise among: the need to simplify the practical use of the tool, the scatter among predicted and experimental values and, finally, an adequate resolution in the zones representative of the process conditions in the industrial lines.

Within the investigated fields, the following values of the four empirical indexes had been stated:

- A for PT in HCl ranges 5 from to 72 - P for WL in HCl ranges from 1 to 26

- A for PT in H2SO4 ranges from 18 to 36

- P for WL in H2SO4 ranges from 1 to 45.

The minimum allowed value is always 1 (negative values are not possible for both WL and PT), while the maximum depend of the type of acid and the ranges of parameters investigated.

A global index is needed for a multi-tank pickling line (in the form of an algorithm for combination of partial indexes for each tank). It represents an important parameter to be derived for the definition of the architecture of the Virtual sensor of aggressiveness (VSA) and Virtual sensor of pickling ability (VSP). The calculation of the global Index of Aggressiveness and Index of Pickling-ability implies a combination of the single tank indexes as shown in the following:

PG = k1 P1 + k2 P2 + k3 P3 + kn Pn (global Index of Pickling-ability) (15)

AG= K1 A1 + K2 A2 + K3 A3 + Kn An (global Index of Aggressiveness) (16)

The k and K are corrective factors related to the peculiarity of the pickling line involved and may depend of a combination among:

- the length of each tank and line lay-out - temperature of incoming strip and difference in temperature between each couple of consecutive tank - percentage of covered and exposed surface of the strip in each tank - empirical factor due to peculiarity of the line.

The simple approach used to develop the basic format of the k and K factors includes:

- the between length of each tank/total length of the tanks

- the between ΔT incoming strip-tank/ ΔT total of the line.

Other corrective factors could be implemented in the future are: the percentage of surface area covered by scale in each tank (as regards PG) and the percentage of surface area of the base metal exposed to acid (as regards AG).

According to the flow-chart, VSP and VSA had been developed, suitable for predictive on line application and based on:

- acquisition of the process and line parameters for each tank (bath temperature, H+, Fe++, inhibitor concentration, incoming strip temperature, etc.) - the ANN predictive tools for WL and PT - dedicated algorithm for A and P calculation for each tank

- dedicated algorithm for AG and PG calculation in a multi-tank line - graphical user interface for data input and output of results.

The flow-chart representing the architecture of the virtual sensors, and how they work, is shown in Figure 58. The approach is common for VSA and VSP; the only difference lies in the use of DLL1 or DLL2, containing the ANN for WL or PT , respectively.

Start Application VSA_VSP.exe 1 Set-up data loading from file Parameters.ini

Simulation Mode OnLine Mode NO FileMode YES OnLine 3 3 YES Line NO 2 NO Line YES Running Running Mode Mode

4 6 6 5 Data One-Shot Mode One-Shot Mode Data loading from data loading by data loading by loading from File the operator the operator Line

7 7 Run Run

8 8 8 8 DLL1 DLL2 DLL1 DLL2 (WL) (PT) (WL) (PT)

9 9 The application receives The application receives from DLLs the output values from DLLs the output values WL , PT for tank n WL , PT for tank n

10 10 Calculation of the Calculation of the

indexes An , Pn and indexes An , Pn and of the factors kn, Kn of the factors kn, Kn for tank n for tank n

11 11 Number YES YES Number of Tanks of Tanks

NO NO 12 12 Calculation of the Calculation of the global indexes of the global indexes of the

line: AG , PG line: AG , PG 13 13

Plot data Plot data

14 15

NO YES YES NO End file Exit Stop

Figure 58 Flow-chart showing architecture and operational logic of the two virtual sensors

The flow-chart can be divided in two parts according to the operational mode to be used:

• on the left side is represented the Simulation mode which is a version developed for trials or demo application. It takes the set of process data from an external file prepared by the operator to simulate the acquisition from the line • on the right side is represented the OnLine mode which is the operational version, able to acquire the data directly from the process control system of the line, and the VSA and VSP use them to calculate and plot the indexes for each tank and for the whole line, exactly like in the OffLine mode.

In both the options the user can commute into the One-Shot mode, which allows him to insert manually the process data (bath temperature, acid, iron and inhibitor concentration) for each tank and try to fore- see the effect of variations of the process parameters on the tank or line conditions.

In the next a short description of the main steps showed in the flow chart is reported.

1 the program runs the set-up phase, loading from the file Parameters.ini the operational mode and all the line parameters (type of acid, line speed, strip temperature .....) and the tank parameters (number of tanks, tank length…), independent of the process conditions.

2 the FileMode parameter in the set-up file informs the program of which mode had been selected to continue:

• OnLine mode: the application acquires the set of data directly from the line • Simulation mode: the application takes the data from an external file prepared by the operator to simulate the acquisition from the line (trial or demo).

3 in the Line Running Mode the program automatically acquires the data from the line (or from the simulation file) unless the user commutes into the One-Shot Mode in which he manually inserts the data for each tank..

4 the program reads a set of data (bath temperature, acid, iron and inhibitor concentration for each tank) from a simulation file, then it stores them in a data-structure.

5 the program acquires a set of data from the line (bath temperature, acid, iron and inhibitor concentration for each tank), then it stores them in a data-structure.

6 in the One-Shot Mode the user manually inserts the data (bath temperature, acid, iron and inhibitor concentration for each tank), then the program stores them in a data-structure.

7 the procedure of calculation starts: the program recalls the data (from the data structures) relating to the tank n and supplies them, like input data, to the DLLs (DLL is the acronym for Dynamic Link Library).

8 the DLLs, containing the Neural Networks (ANN) developed to predict WL and PT, receive the input data from the program, calculate and generate the output parameters WL (from DLL1) and PT (from DLL2) for the tank n. The DLLs are different for hydrochloric and sulphuric pickling systems.

9 the program takes the values of WL and PT for Tank n, generated from the DLLs, and store them into a data-structure

10 the program calculates for the tank n:

• the indexes An and Pn, using the WL and PT values

• the corrective coefficients kn, Kn.

11 the program considers the sets of input data in groups of N, being N the total number of tanks of the line (from file Parameters.ini); it loops back to step 5 for N times before passing to step 12 to calculate the global indexes of the line.

12 the program calculates the global indexes PG and AG of the whole line, using a group of N values.

13 tank (Pn and An)and line (PG and AG) indexes are plotted and numerically displayed on the different masks on the screen.

14 if the data file is finished the program shuts off, otherwise it comes back to step 4 .

15 pushing the stop-key on the screen the program will stop, otherwise it goes back to step 5 .

Task 4.3. Setting up of graphical user interface

The aim is to develop a graphical user interface able to reach the objectives of the project allowing the user to be abstracted from the mathematical methods used for solving the problem. The objective taken to be developed in the task 4.2 (Programming efforts) is to estimate the optimum line speed that minimize as many overpickling defects as underpickling defects for each acid concentration, thickness and type of steel. The process variables and the number of pickling defects are distributed in two Access tables. The application gathers the data by means of SQL commands.

Borland C++ Builder was used and is a powerful environment to develop software for Windows and it provides the necessary tools to design, implement, execute and debug an application by means of a group of elements that operate in an integrated and complementary way. This programming environment allows the development of graphical interfaces as well as the implementation of C++ applications and the use of stand_alone application.

The proposed algorithms for training and validation of the model have been developed using Matlab functions. A SOM network with a validation technique is proposed to estimate the optimum line speed. By means of Matlab Compiler can be generated C++ source code for combining with other modules to form stand_alone application. Stand alone application do not require Matlab at run time; they can run event if Matlab is not installed on the system.

Assessment review of user requirement

Graphical user interface design is an important adjunct to application programming. Its goal is to enhance the usability of the underlying logical design of a stored program. Usability is mainly a characteristic of the user interface, but is also associated with the functionalities of the product. It describes how well a product can be used for its intended purpose by its target users with efficiency, effectiveness, and satisfaction, also taking into account the requirements from its context of use.

The development of graphical user interface has been carried out through the above and following item:

• Functionality requirements gathering: assembling a list of the functionality required of the system to accomplish the goals of the project and the potential needs of the users. • Analysis of the potential users: graphical interface design needs good understanding of potential user needs. • Feedback: Providing immediate feedback when a user performs an action. For example, when the user clicks a button, something on the screen should change so the user knows the system has registered the action.

• Accessibility: Users need to find information quickly and easily, creating a conceptual design, choosing the most appropriate interaction style, appropriate interaction devices, and making screen elements meaningful.

Programming of SOM graphical user interface

The graphical user interface (GUI) has been improved. Therefore some part of source code have been performed in order to achieve best result. Whatever be the approach to the software development, the program must finally satisfy some fundamental properties; bearing them in mind while programming reduces the costs in terms of time and/or money due to debugging, further development and user support. Although quality programming can be achieved in a number of ways, following five properties are among the most relevant: 79

• Efficiency: it is referred to the system resource consumption (computer processor, memory, slow devices, networks and to some extent even user interaction) which must be the lowest possible. • Reliability: the results of the program must be correct, which not only implies a correct code implementation but also reduction of error propagation (e.g. resulting from data conversion) and preven- tion of typical errors (overflow, underflow or zero division). • Robustness: a program must anticipate situations of data type conflict and all other incompatibilities which result in run time errors and stop of the program. The focus of this aspect is the interaction with the user and the handling of error messages. • Portability: it should work as it is in any software and hardware environment, or at least without relevant reprogramming. • Readability: the purpose of the main program and of each subroutine must be clearly defined with appropriate comments and self explanatory chose of symbolic names (constants, variables, function names, classes, methods, ...).

Therefore, in order to follow this quality requirements, source code was altered .

SOM Graphical user interface description

The main window of graphical application is showed in Figure 59. The following steps gives a quick overview of task must be carried out by user.

Figure 59 SOM Graphical user interface

Connection with the data base and table: The data of the process variables and the number of pickling defects are necessary and they are registered and distributed in two tables of a data base. Establish- ing the connection with the data base can be thus summarized: First, it should be established the connection with the data base by means of a connection string where is indicated the controller that be

used, the Server name where is the data base, the name of this one and the access option must be indicated. In this case Microsoft Jet Database Engine is used because it is dealing with an Access Database.

If an error occurred trying to start the connection, the developer application in C++ code allows the user to detect and correct the problem. Once the connection has been successfully, the selection of the table is done. A function in C++ code was implemented to test and modify the field of the acid concentration if they have invalid values.

Parameter selection: The training variables are the strip thickness, the average line speed, the acid concentration in tank 4 ,the number of overpickling defect, the number of underpickling defects and the line shutdown time. The selection of type of steel is done by means of a TComboBox component, see Figure 60.

Figure 60 Selection of type of steel

The data are filtered taking into account different selection criteria, so the interface may insert filter’s range of the variable through select menu, shown in Figure 61. The main advantage of using menu is than it allow to test with difference values, so the user has not to access to the code. Data must assert the following conditions:

Figure 61 Parameter selection menu

- The line shutdown time must be less than (in seconds): - The average line speed must be less than (in percentage): - The acid concentration in tank 4, must be greater than (in gr/l): - The strip thickness must be equal to (in mm):

A function in C++ code has been developed in order to detect and correct every possible error during storage, like not a number values or empty fields . This routine tests and then informs showing the message displayed in Figure 62.

Figure 62 Message displayed.

Choose coil: When parameter selection has been correctly made, the third step consist of selection of coil set. This selection is done by pressing the respective button, inserted in the groupbox, it was designed to carry out that task, as it is shown in Figure 63.

Figure 63 Selection coil set menu

- Coil defect less: coil set which have not global defect. - Coil with Overpickling defect: coil set which have overpickling global defect. - Coil with Underpickling defect: coil set which have underpickling global defect. - Coil defect less, with Overpickling and Underpickling: coil set which have not global defect or overpickling global defect or underpickling global defect, that is, the sum of all other previous mentioned coil set.

A function in C++ code has been developed in order to detect if there are no data with this selection criteria, and following inform showing a message. On the other hand, if everything is correct , is showed the message: “Data have been generated correctly”

When choosing the coil set, the data are filtered by means of a SQL sentence. After, the data is saved in a file. The proposed algorithms for training and validation of model have been developed using MAT-

LAB function. This model estimates the optimum line speed that minimizes the pickling defects for each coil set, parameter selection and type of steel.

That algorithms consist of three step: create maps, sort the best maps and represent maps. Beside the user must have an abstraction level avoiding the knowledge of the internal mathematical methods implemented in the application.

Create maps: This routine read the data from previous mentioned file and it is required two parameters: maximum number of maps and number of neuron, which are inserted by means of menu. A function in C++ code has been developed in order to detect and correct every possible error during storage, like not a number values or empty fields. The network training is carried out by pressing the button inserted in the groupbox, called Execute, see Figure 64.

Figure 64 Create maps function

Class Maps: A function in C++ code has been developed so as to only when the training is finished, the button which fulfills class the maps is enabled, see Figure 65. When

Figure 65 Class maps function the button called execute is pressed , it is undertaken class the maps in accordance with the minimizes of proposed error.

Represent Maps: This routine require one parameter, which is inserted by means of menu. A function in C++ code has been implemented in order to be enabled the button which carry out the maps representation , only when the previous mentioned step is finished, besides this function detect and correct every possible error during storage, like not a number values or empty field, see Figure 66. Once this is correctly finished, an optimum line speed is computed in function of the process values and then the application shows the resulting plots: optimum line speed versus acid concentration, Figure 67.

Figure 66 Represent maps function

Figure 67 Estimation of the optimum line speed with 78

GUI for Index of Aggressiveness

Two demo versions of the graphical user interface had been realised for the application of the two virtual sensors to a four tank line, one for hydrochloric pickling and other for sulphuric pickling.

Two modes of operation are available:

- one-shot - line-running.

Before running, the virtual sensors need to be preliminary tailored to the relevant pickling line. Geomet- rical parameters of the tanks (as length, number of tanks) and line parameters (as line speed, temperature of the incoming strip) must be collected into a file .ini, the system uses as a data base for calcula- tions. Such file .ini can be modified in progress by pushing the Line Parameters or Tank Parameters buttons.

The system elaborates data from a data base in which the operational process parameters coming from the tanks are collected by the operators or taken from PLC.

One-shot mode, shown in Figure 68, allows the manual input of the process data for each tank and calculates the tank indexes and the global index for the line. It is a support for the operator in term of veri-

fication of results or off-line prediction of the tank/s and line behaviour. Plots of the tanks’ indexes vs. time can be retrieved by the operators by pushing the A or P buttons.

Zero values of the indexes mean the tank is not working or, as in the sulphuric case, not existing.

Figure 68 images of the graphical user interface in the one-shot mode

Line-running mode, shown in Figure 69, is the operational mode to be used during production. It gives the operators just in time values of the two indexes for each tank and for the whole line and plots of the two indexes vs. time.

Figure 69 image of the graphical user interface in the line running mode

The virtual sensors constitute very ductile tools that can be adapted or implemented according to peculiarity of the involved line or special needs of the line operators. They can be applied in every pickling line using HCl or H2SO4 the above explained tailoring and some calibration according to the peculiarity of the relevant line (e. g. inhibitor use and effectiveness, scale preconditioning before entering the line etc).

Sketch of the program code used to realize the graphical user interface and the virtual sensors:

A crucial part of the program code (file VSA_VSPDlg.cpp) is shown and explained to give a sketch of the logic used to develop the virtual sensors and the graphical user interface. The file VSA_VSPDlg.cpp deals with the elaboration of data coming from the DLLs and the graphical and numerical visualization on the screen of the resulting indexes: An, Pn, AG e PG. DLL is the acronym of Dynamic Link Library, a library containing distinct functions not linked to the other during compilation of the software modules in the program. DLL is loaded just during the execution of the program. In our case the DLLs contain the ANNs and constitute the core of the two virtual sensors developed in the project.

Some functions into the program file VSA_VSPDlg.cpp serve for the graphics on the screen, others serve to exchange data and elaborate results coming form the DLLs.

The mask of the Graphical User Interface in the Line Running mode helps the comprehension of the links among the keys and the functions into the program file.

The functions in the file VSA_VSPDlg.cpp that are linked to graphic elements of the mask are the following:

• OnButtonNomodal: coupled to key Tank Parameters . Pressing the key, the function OnBut-

tonNomodal executes the opening of the mask Tank Parameters on the screen, allowing the user to check the tank parameters included into the file parametri.ini

• OnButtonNomodal2: coupled to key Line Parameters . Pressing the key, the function OnBut-

tonNomodal2 executes the opening of the mask Line Parameters on the screen, allowing the user to check the line parameters included into the file parametri.ini

• OnButtonNomodal3: coupled to key A . Pressing the key, the function OnButtonNomodal3

executes the opening of the mask A parameter for Tank, allowing the user to see simultaneously on the screen the outline of the A curves for all the tanks.

• OnButtonNomodal4: coupled to key P . Pressing the key, the function OnButtonNomodal4

executes the opening of the mask P parameter for Tank, allowing the user to see simultaneously on the screen the outline of the P curves for all the tanks.

• OnButtonOneshot: coupled to key ONE-SHOT . Pressing the key, the function OnButtonOne-

shot executes the opening of the mask ONE-SHOT, allowing the user to manually insert the process data to calculate the A and P indexes.

• OnButtonCalc: coupled to key CALC . Pressing the key, the function OnButtonCalc load the

operational process data from the line (or simulating file) and runs the continuous calculation of the indexes. If the user works in the ONE-SHOT mode, the function takes the input data manually inserted in the mask and calculates the indexes, than it commutes in stand by waiting for further user’s command.

• Aggiorna_Scale: coupled to square Autoscale . If the square is checked off, the function Ag-

giorna_Scale automatically adapts the Y range in the graphs to calculated values, otherwise the Y range in the graphs will be fixed. The same function automatically shows an interval of the last n minutes on the X axis (time scale), being n value contained into the file parametri.ini.

The functions in the file VSA_VSPDlg.cpp that serve to exchange data and elaborate results coming form the DLLs, are the following:

• EseguiDll: the function EseguiDll executes the dynamic link with DLL1 e DLL2, feeding them the input data stored into EngProcData[Tank]. The ANNs into the DLLs calculate the primary values of WL and PT and put them into Out_Prop, Out_Prop1.Dll

• VisualizzaRisultati: the function VisualizzaRisultati visualizes, into the 10 windows of the mask, the numerical values of the indexes A and P calculated by the program

• CalcolaMedia: the function CalcolaMedia calculates the indexes An e Pn for each tank from the primary values of WL and PT stored into Out_Prop, Out_Prop1.Dll . The procedure is N times

repeated, being N the total number of tanks, and finally the two line indexes AG e PG are calculated too.

The program listing of the file VSA_VSPDlg.cpp, showing all the functions previously explained, is shown in Annex V.

2.3.5 WP 5: Operational tentative implementation, testing and verification of sensor based PCT

Involved Partners: BFI, TKS-RA, CSM, UniOvi worked on all tasks of this work package.

Objectives of WP 5 In this work package the devices developed shall be implemented at the same selected processes as in WP 1, e.g. Aceralia and TKS-RA to verify the results achieved earlier. Furthermore, functionality of the developed process control (WP4) and sensors (WP2) shall be tested and validated economically, regarding

- minimisation of metals precipitation, - enhancement of pickling velocity and - reduction of surface pickling defects.

Finally an assessment of the transferability of the achieved results to different processes shall be given.

Tasks 5.1 and 5.2 Preparation and Performance of verification trials

BFI and TKS-RA installed the ultrasound based sensor at the plant already in WP 3. Here, it has also been implemented in visualisation and prepared for control tasks. All relevant communication between the new sensor (WP 2) and process control has been established.

In continuation of WP3 TKS-RA continuously recorded production programme and operational data, accompanied by sampling and analyses by BFI in operation at the named production site. Different possibilities of control have been considered, Figure 70.

Addition

H2SO4 Control of H2SO4 or Control of H2SO4 or Fe Fe Sampling Point

Strip X Strip 1234

XXSampling Point Sampling Point

Online measurement

Regeneration (Crystallisation)

FeSO4*7H2O

Figure 70 Possibilities of sensor-based control of pickling lines (e.g. at TKS-RA)

Concentration measurement at the locations indicated is recommended. Installations at tanks 1 and 4, only, would already be quite convenient. However, control loops shall be closed in that way that Fe content in tank 1 and acid content in tank 4 are controlled. Closing the loops in the other way, this would lead to extent of concentrations where they are not controlled. Even worse, in the case of the hot sulphuric acid pickling process, the wrong control strategy would lead to irreversible formation and precipitation of monohydrate in the tanks, [22].

Including concentration, control in general aims at:

- high strip quality; - high plant productivity; - no scrap; - low costs and - safety by means of

- strip speed; - temperature control and, as mentioned above, - concentration control.

In the last period, furthermore, new data from the Aceralia pickling line were used to check the algorithm proposed by UniOvi. The same constraints, calculation of the pickling defects and training variables that were considered above in the FLSOM training were taking into account to carry out this as- say.

The training data was obtained from coils manufactured between May and November 2006. In this data set there are neither overpickling nor underpickling coils. The total number of samples (or coils) of the training data set was equal to 372.The typical sequential algorithm of the SOM has been used instead of Fuzzy Labelled Self-Organizing (FLSOM) algorithm since there is just one cluster in the data set (coils without global defect).

The results show the estimation of the optimum line speed that minimizes as many overpickling defects as underpickling defects for each acid concentration corresponding to a temperature between 77-80ºC with the thickness and type of steel that were considered. We can see that estimated line speed is lower than the estimated in the previous report with FLSOM algorithm because a change of the operating conditions seems to be taken place.

Task 5.3 Assessment of achieved results

Developments by BFI and UniOvi have been implemented at TKS-RA and Aceralia pickling lines. At TKS-RA data coming from the concentration measurement have been implemented in the pickling plant PLC, Figure 71, and statistical process control.

Data is used to calculate the distance from concentration ranges at which precipitation would take place. At the newly introduced mixing tank at TKS-RA, prior to tank 4, concentration data is used to generate set-values for fresh acid and condensate addition, Figure S7. TKS-RA is convinced that about 3 to 5 % of their annual productivity increase origins in the introduction of the installed measurement system.

Figure 71 Visualisation of concentration in TKS-RA pickling plant PLC for tank 1

2.3.6 WP 6: Implementation of sensor bases online control in European pickling lines

Involved Partners: All partners have worked on this work package.

Objectives of WP 6 In this work package, a general approach for the implementation of sensor-based online control in European pickling lines shall be developed regarding

- Multistep carbon steel pickling line (Sulphuric and hydrochloric acid pickling)

- Multistep stainless steel pickling line (mixed acid pickling)

- Multistep stainless steel pickling line (electrolytic pickling)

Task 6.1 and 6.2 Assessment of available analytical techniques, control devices and simulation tools Usually, in the industrial pickling baths the parameters regularly measured are

- acid and Fe++ concentration, - temperature and - inhibitor (if used) delivery, often not in all the tanks.

Bath aggressiveness and pickling-ability cannot be measured in continuous due to lack of sensors or techniques for online measurements and to aggressiveness of the tank environment. Long and destructive dipping tests of standards or line steel samples to measure WL and PT, or H2 evolving form the process solution are available, only. Inhibitor concentration is complex and long to analyse and its delivery is the only parameter easily available.

So, all the above mentioned parameters are used as empirical, indirect indicators of the stability of properties of the bath, to be maintained more or less constant during the process. Effects of their variations cannot be easily predicted or counterbalanced.

With the BFI development of the online concentration measurement basing on ultrasound technology, which has recently been patented [23], real-time information of process bath composition can be made ++ available. The development was performed with the ternary mixture (H2SO4, Fe , H2O) of a single acid pickling process, i.e. sulfuric acid pickling together with TKS-RA. The information is now implemented in their visualisation and used for insuring secure operation of the plant, Figure 71 and Figure S7.

The transferability seems to be given immediately for other single pickling solutions or ternary mixtures, e.g. for hydrochloric acid pickling [24], [25]. For the application to mixed acid pickling, major efforts are needed to determine a feasible third parameter to be measured continuously, to calculate a model for such quarternary mixtures and – besides – to consider the normally higher potential of corrosion of such acid systems.

Passive acoustic measurements (DAC) have undergone a rapid development within the last 10 years. Development has been made related to mechanical design of the equipment, signal processing and data analysis/calibration methodology. These developments are necessarily at least partly application specific due to the different requirements in different applications. A number of applications based princi- pally on the methodology described above have been published in the scientific literature or patents, for an overview see [26]. IVL has no knowledge that any other has built passive acoustic measurement systems for pickling acid. Experience of the equipment so far is that component selection seems correct with the exception of using ceramic valves in the pump (that have been replaced). The results from the pilot/laboratory test with industrial grade mixed pickling acids are promising and realistic. However, to fully evaluate the measurement system a test at mill is need for 3-9 months. This to enable a calibration based on 30-100 samples and using the obtained model to predict HF, HNO3 and metal ion content during 3-4 months. IVL is currently seeking industrial partners to perform such trial after the project has finished.

The transferability to other pickling applications is good. It is our opinion that building measurement equipments for sulphuric and hydrochloric pickling acids is viable. However, careful material selections have to been done to make the equipment corrosion resistant enough. Note also that sulphuric and hydro chloric pickling acids have less species in solution, therefore it is reasonable to assume that the measurements would be more straightforward.

In principle, indicators of pickling and over-pickling ability, such as VSA and VSP, able to combine the already measured data , could also be useful for better managing multi-tank pickling lines (both sulphuric and hydrochloric), with difference in acid, Fe concentration and T among the tanks (e.g. the Aceralia HCl or TKS-RA H2SO4 pickling line) and during the production. No important modification would be needed in terms of physical sensors or measurements because a combination of existing/measured parameters are used for the VSA and VSP.

Sets of process data coming from different pickling lines can be managed in the same way, apart of some additional information on the line arrangement as tank lengths or incoming strip temperature.

The key factor of the transferability of the SOM application developed by UniOvi is to provide some parameters quantifying the quality of the steel surface in the pickling line. In this way, the proposed application estimates the optimum line speed minimizing the overpickling and underpickling defects which are measured by an automatic inspection system. The data acquisition of the application does not need to be on-line because of the system requirements.

Task 6.3 Coupling and tailoring of PCT for the selected lines

2.3.7 WP 7: Co-ordination, reporting, documentation All partners have met regularly to intensively assess and discuss their results and to refine their further common work within this project. Reports and publications have been presented to the technical group members and to public [11]. Documentation of the different GUIs are presented within this report.

2.4 Conclusions

Within the completed research project the partners have developed several different approaches towards sensor based online control of pickling lines. BFI together with their subcontractor SensoTech and together with the industrial partner TKS-RA have developed installed and taken into operation a prototype of online concentration measurements for sulphuric acid and iron content in pickling liquor. The new sensor system has proved its reliability in operational trials, so that communication to plant-wide control has been established and a closed loop dosage of fresh acid has been realised. TKS-RA is con-

vinced that about 3 to 5 % of their annual productivity increase origins in the introduction of the installed measurement system.

A different approach of measuring aggressive compounds exemplarily in mixed acids systems has been followed by IVL. In laboratory trials the system has been tested and calibrated while operational verification shall be performed after the completion of the project.

CSM has performed several laboratory trials to assess optimum pickling time and weight loss behaviour for different steels in both hydrochloric and sulphuric acids. Basing on those results a theoretical approach towards pickling control was followed. Namely an index of aggressiveness has been developed and a model to be applied to pickling plants was developed using neural network technology.

Uniovi has finally made use of artificial intelligent technology as well, namely Fuzzy Labeled Self Or- ganizing Maps (FLSOM). Their approach was to analyse huge amounts of data provided by the pickling line of Aceralia Steelworks in Aviles, Spain. In the end a model was developed that could predict optimum pickling line speed in order to achieve high productivity and avoid pickling defects.

2.5 Exploitation and impact of the research results

The results of the completed research project are very promising towards later exploitation. To establish that in detail the partners of the completed research project BFI, TKS-RA and Uni Oviedo have been granted funding for a pilot and demonstration project to apply both technologies and their combination for optimum pickling line control. Within the pilot project four new sensor systems shall be installed to enable control of four including the prototype five measurement locations.

BFI has patented their measurement system [23] and presented the intermediate project results at CETAS in Luxembourg, 2006 [11]. IVL has presented their technology mainly to Swedish SMEs, Steel manufactures and other industries. This has been done through direct talks and conferencing/lectures. These activities have continued till the end of the project and will continue after the project.

Rasselstein furthermore enforces their engagement in pickling line control research meaning that from other german funded projects and from this completed project different results shall be applied and lead to further optimisation of their plant, i.e. the index of aggressiveness developed by CSM is considered to keep high potential for such task.

References

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[19] Villmann, T., Sei ert, U., Schleif, F.-M., Brss, C., Geweniger, T., & Hammer, B. Fuzzy Labeled Self- Organizing Map with Label-Adjusted Prototypes. Artificial neural Networks in Pattern Recognition, 4087 (2006) 46–56.

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List of Tables and Figures

Table 1 Concentration values during CSM industrial campaigns

Table 2 Concentration values from five industrial lines

Table 3 Matrix for laboratory measurements with artificial H2SO4 pickling solutions

Table 4 Ranges of selected parameters for HCl baths

Table 5 Ranges of selected parameters for H2SO4 baths

Table 6 BFI-analyses of samples taken at TKS-RA

Table 7 Correlation coefficient interpretation

Table 8 Correlation values between overpickling defects and line speed

Table 9 Correlation values between thickness strip and line speed

Table 10 Key process variables

Table 11 New sets of trials with inhibitor

Table 12 Dimensions of SOM models for each type of steel

Figure S1 Schematic of pickling section at TKS-RA, Andernach

Figure S2 Grid of lines of constant concentrations (Fe and H2SO4) at a given temperature in the plane set-up by physical parameters

Figure S3 Presents the model predicted value (x-axis) of metal ions against the observed value (y- axis)

Figure S4 Installation of operational measurement device at TKS-RA in the feed pipe of the crystalli- zation section

Figure S5 Typical time series of concentrations from operational trials at TKS-RA

Figure S6 Trajectory to estimate the optimum line speed using FLSOM

Figure S7 Calculation of fresh acid and water addition at TKS-RA mixing tank

Figure 1 Schematic of Aceralia pickling line No. 2

Figure 2 BFI laboratory set-up

Figure 3 Surface Inspection system at Aceralia pickling line No. 2

Figure 4 Lightning and cameras of the surface inspection system at Aceralia pickling line No. 2

Figure 5 Example of typical curve obtained from BFI laboratory trials

Figure 6 Schematic of process section of the TKS-RA pickling line, Andernach

Figure 7 Number of overpickling defects versus line speed

Figure 8 Defect severity ratio and line shutdown time for each overpickled coil vs. line speed

Figure 9 Number of bad pickling defects versus line speed

Figure 10 Acid concentrations in the tanks

Figure 11 Thickness versus line speed with overpickling coils

Figure 12 Thickness versus line speed with bad pickling coils

Figure 13 Line speed versus acid concentration with overpickling coils

Figure 14 Speed versus acid concentration with bad pickling coils

Figure 15 Overpickling and bad pickling ranges

Figure 16 Domain of investigation in the plane [HCl]/[Fe++]

Figure 17 Matrix of conditions for HCl DOE trials

Figure 18 Matrix of conditions for DOE trials with H2SO4

Figure 19 Existing Rotating Cylinder Electrode and new sample holder for hot-rolled samples

Figure 20 Schematic of aspired measurement device at TKS-RA

Figure 21 Surface of hydrochloric pickled samples

Figure 22 Normalized time to pickling time for steel A and B vs. temperature

Figure 23 Weight loss vs. temperature for steel A and B

Figure 24 Time to pickling time vs. temperature for steels C and D

Figure 25 Time to pickling for steel A and B in HCl

Figure 26 Weight loss vs. temperature for steel A and B in HCl

Figure 27 Time to pickling for steels C and D in H2SO4

++ Figure 28 Weight loss for steels C and D in H2SO4, with 20 g/l of Fe

Figure 29 Effect of single variables on weight loss for steel A in HCl 98

Figure 30 Analysis of DOE results: pickling time for steel C and D in H2SO4

Figure 31 Analysis of DOE results: weight loss for steel C in H2SO4

Figure 32 Weight loss vs. inhibitor concentration for HCl and H2SO4 baths

Figure 33 Principal component analysis of water test data.

Figure 34 Overview of experimental domain in calibration trial

Figure 35 Presents the model predicted value (x-axis) of HF acid against the observed value (y-axis)

Figure 36 Presents the model predicted value (x-axis) of HNO3 acid against the observed value (y- axis)

Figure 37 General type graphical representation of a MIMO model with three input and two output parameters

Figure 38 Comparison between ANN and deterministic approach

Figure 39 Correlation of the pickling time between steel A and B

Figure 40 Correlation between measured and calculated PT values in HCl baths (steel A and B)

Figure 41 Comparison among measured and calculated PT values in HCl baths (steel A and B)

Figure 42 Schematic drawing Pickling Acid DAC-system

Figure 43 DAC Equipment at IVLs laboratory

Figure 44 Installation of operational measurement device at TKS-RA in the feed pipe of the crystalli- zation section

Figure 45 Typical time series of physical parameters from operational trials at TKS-RA

Figure 46 Comparison of concentrations from operational trials and mobile analysis

Figure 47 Typical domains of physical parameters (left) and concentrations (right) from operational trials at TKS-RA

Figure 48 Validation of sensor heads (left: ultrasound velocity, right: electric conductivity) immersed in pickling liquor at TKS-RA for one month

Figure 49 Upper left heavily dissolved ceramic valve-head, upper right slightly affected valve-head although miss-colouration and lower to the left a partly dissolved sealing ring

Figure 50 Underpickling and overpickling defects for type of steel A

Figure 51 Underpickling and overpickling defects for type of steel B 99

Figure 52 Underpickling and overpickling defects for type of steel C

Figure 53 Flow-chart of the pathway to design the virtual sensors VSA and VSP

Figure 54 The calculus of the map side lengths is based on the number of samples and the eigenvalues of the data matrix

Figure 55 Trajectory obtained under variation of de acid concentration, non shutdown line and mini- mization of the overpickling and underpickling defects

Figure 56 Optimum line speed obtained with the purposed technique for type of steel A

Figure 57 Estimation of the optimum line speed with 74

Figure 58 Flow-chart showing architecture and operational logic of the two virtual sensors

Figure 59 SOM Graphical user interface

Figure 60 Selection of type of steel

Figure 61 Parameter selection menu

Figure 62 Message displayed

Figure 63 Selection coil set menu

Figure 64 Create maps function

Figure 65 Class maps function

Figure 66 Represent maps function

Figure 67 Estimation of the optimum line speed with 78

Figure 68 Images of the graphical user interface in the one-shot mode

Figure 69 Image of the graphical user interface in the line running mode

Figure 70 Possibilities of sensor-based control of pickling lines (e.g. at TKS-RA)

Figure 71 Visualisation of concentration in TKS-RA pickling plant PLC for tank 1

Figure 72 Coil map

Figure 73 Classification of coil defects from coil maps (e.g. from previous figure)

Figure 74 Correlation of the pickling time between steel C and D

Figure 75 Correlation of the weight loss between steel A and B

100

Figure 76 Correlation of the weight loss between steel C and D

Figure 77 Correlation between measured and calculated PT values in H2SO4 baths (steel C and D)

Figure 78 Comparison among measured and calculated PT values in H2SO4 baths (steel C and D)

Figure 79 Correlation between measured and calculated WL values in HCl baths (steel A and B)

Figure 80 Comparison among measured and calculated WL values in HCl baths (steel A and B)

Figure 81 Correlation between measured and calculated WL values in H2SO4 baths (steel C and D)

Figure 82 Comparison among measured and calculated WL values in H2SO4 baths (steel C and D)

Figure 83 Optimum line speed obtained with the purposed technique for type of steel B

Figure 84 Optimum line speed obtained with the purposed technique for type of steel C

Figure 85 Estimated process variables estimated by FLSOM training

Figure 86 Estimated global defects of coils by FLSOM training

Figure 87 Trajectory to estimate the optimum line speed with 74

Figure 88 Measurement chain when using DAC

Figure 89 Screen-dump of the selection-dialog for selecting a setting file in the DAC-software

Figure 90 Screen-dump of the GUI-tab for file paths and filenames in the DAC-software

Figure 91 Screen-dump of the GUI-tab of the status window in the DAC-software

Figure 92 Screen-dump of the Spectra-tab (the main tab of user interface) in the DAC-software

Figure 93 Geometric interpretation of PLS

Figure 94 Permutation validation plot for Metal ion content

Figure 95 Permutation validation plot for HF concentration

Figure 96 Permutation validation plot for HNO3 concentration

101

Annex I

Additional Figures

103

Figure 72 Coil map

104

Figure 73 Classification of coil defects from coil maps (e.g. from previous figure)

105

Figure 74 Correlation of the pickling time between steel C and D

Figure 75 Correlation of the weight loss between steel A and B

106

Figure 76 Correlation of the weight loss between steel C and D

Figure 77 Correlation between measured and calculated PT values in H2SO4 baths (steel C and D)

107

Figure 78 Comparison among measured and calculated PT values in H2SO4 baths (steel C and D)

Figure 79 Correlation between measured and calculated WL values in HCl baths (steel A and B)

108

Figure 80 Comparison among measured and calculated WL values in HCl baths (steel A and B)

Figure 81 Correlation between measured and calculated WL values in H2SO4 baths (steel C and D)

109

Figure 82 Comparison among measured and calculated WL values in H2SO4 baths (steel C and D)

Figure 83 Optimum line speed obtained with the purposed technique for type of steel B

110

Figure 84 Optimum line speed obtained with the purposed technique for type of steel C

Figure 85 Estimated process variables estimated by FLSOM training

111

Figure 86 Estimated global defects of coils by FLSOM training

Figure 87 Trajectory to estimate the optimum line speed with 74

112

Annex II

The Direct Acoustic Chemometrics, DAC software and modeling of data from the system In this appendix the DAC measurement chain, the DAC software and multivariate modeling is described.

The DAC measurement chain

Vibrations

Physical Phenomenon Sensor Amplification Lowpass Accelerometer Filter

F{X(t)} = X(f)

A/D FFT Pre-treatment Multivariate Conversion Analysis

Figure 88 Measurement chain when using DAC

In the final step multivariate calibration yield a model that can be used to predict eg. HF concentration based on a new measurement spectra. This is done by weighting the values of different frequencies, summing these contributions and adding a constant.

The DAC software and modeling path The main DAC software includes functions for

• Starting, stopping and controlling a pump. • Measurements of temperature and pressure. • Acquiring vibration signals and computing spectra of the vibration signals. • Saving data into MATLAB-file format. • Selection and reading setting for acquiring acoustic data from a text-file.

An additional software has been developed to simplify rinsing with water of the DAC-system after each runs with different acid concentrations.

The DAC software is run under LabVIEW 8.x. The Graphical User Interface, GUI for selecting a settings file is shown in Figure 89. Thereafter follows screen dumps of the GUI-tabs for file folder and naming, the status-tab and finally the spectral tab (the main interface for the user). The file format MATLAB was chosen because variables can easily be read separately and it is fairly stable even if a file is not closed after reading.

113

To collect spectral and concentration data from files for each trial-point/run a MATLAB 6.5 script was used. This script also save data into a format suitable for the multivariate calibration run in commercial software SIMCA-P+. MATLAB graphs was used for inspecting data of separate runs and the calibration information which software for multivariate analysis/calibration uses.

Figure 89 Screen-dump of the selection-dialog for selecting a setting file in the DAC-software. The setting files include pressures and measurement times.

114

Figure 90: Screen-dump of the GUI-tab for file paths and filenames in the DAC-software. Here we can see and change the paths and base filenames.

Figure 91: Screen-dump of the GUI-tab of the status window in the DAC-software.

Here we can see the progress when collecting spectra (“Averaging loop”), how many averages the latest spectra are based on and when the latest spectra was written to file.

115

Figure 92: Screen-dump of the Spectra-tab (the main tab of user interface) in the DAC-software.

In this image we can see the setting-file has 28 program-steps and it is on step 2. To the right we can see that temperature was 22,01 °C and the pressure was 4,98 bar for the shown spectra. To the upper-left we can see the current pump frequency was 62Hz, pressure set-point was 5 bar and the current pressure was 4,99 bar. The spectral graphs shown in white, red and green is the logarithm of the power spectra for each sensor. The blue line shows the logarithm of cross spectra between accelerometer 0 and 1.

116

Model development using multivariate tools Multi Variate Data Analysis is a tool that can be used for many types of data, in this project on acoustic spectra from the DAC equipment.

Typical examples of MVDA methods are principal component analysis (PCA) [28-29] and partial least squares (PLS) [29-30]. Both techniques reduce the multidimensional data set to lower dimensions by calculating so-called principal components (PCs) that describe the data. A PCA model is based on the X-block (i.e. the frequencies) and calculated in such a way that it describes as much variance as possible in the data, whilst a PLS model also takes the correlation to the response(s) of interest (eg. HF and HNO3 concentration) into account. Results from PLS and PCA are often interpreted in score plots and loading plots. Score plots show how the samples are distributed and loading plots display the relationships between the variables (eg. frequencies). Figure 93 below shows a geometric interpretation of PLS.

Score plot

Loading plot

a. b. c.

Figure 93 a. Each sample has a value for each frequency, giving it a coordinate in the n-dimensional space (n = number of variables (frequencies), here n = 3). Each sample also has a corresponding HF concentration value. b. A number of principal components (PCs) are placed in the n-dimensional space in such a way that they describe the data as good as possible. c. The score plot shows the projection of the samples on the PC plane and the loading plot shows the influence of each variable on the PCs.

The PLS-models can be used to produce coefficients (calculated based on the loading weights) similar to multi linear regression, MLR. These coefficients that can be used for predictions eg. of the HF concentration, using the following equation: ) y = a + bX NEW

) Where y is the predicted value, a a constant, b a vector of constants and X NEW the data the prediction is based on, in this study a acoustic spectra. For more information about how a and b is computed from a PLS-model see [28] 117

Annex III

Model validation Metal ion content

Figure 94 Permutation validation plot for Metal ion content

Figure 94 shows a permutation plot for the metal content model. The permutation plot is based on cal- culations where the order of Y is randomly permuted 20 times and then separate models are fitted to all the permuted Y's extracting as many components as was done with the original matrix Y.

The plot displays the correlation coefficient between the original Y and the permuted Y versus the cu- mulative R2 and Q2, and draws the regression line. The intercept (R2 and Q2 when correlation coefficient is zero) is a measure of the over fit.

The metal content model can from this test be found good.

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HF- acid content

Figure 95 Permutation validation plot for HF concentration

Figure 95 shows a permutation plot for the HF content model. The permutation validation shows that the model for HF is fair.

119

HNO3- acid content

Figure 96 Permutation validation plot for HNO3 concentration

Figure 96 shows a permutation plot for the HNO3 content model. The permutation validation shows that the model for HNO3 is fair/poor.

Reciprocal the validity of models are ranging from good (metal-ion), fair (HF), fair/poor (HNO3).

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Annex IV

1. WL_AB and WL_CD

A data base of 288 experimental points had been used for WL_AB, divided in two sub-sets: 144 points for training and remaining 144 for testing.

A data base of 400 experimental points had been used for WL_CD, divided in two sub-sets: 200 points for training and remaining 200 for testing.

Weight Loss models WL_AB and WL_CD are similar apart of a different number of nodes in the hidden layer: the former contains 9 hidden nodes, the latter contains 7 hidden nodes.

The basic structures of the ANNs WL_AB and WL_CD are the following:

Code input = Back propagation

Code output = Sigmoidal

[INPUT VECTOR] [TRAINING]

Index n.0 = T Epoch = 50

Index n.1 = H SB freq = 1

Index n.2 = Fe Access = Random

Index n.3 = I SbCriteria = RMS error

Initialize = Random

[OUTPUT VECTOR]

Index n.0 = WL

Schedule Epoch H1Lr µ OLr µ

Cycle 0 10 0.40 0.40 0.40 0.40

Cycle 1 30 0.20 0.20 0.20 0.20

Cycle 2 50 0.10 0.10 0.10 0.10

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f_offset = 0.01

ErrorFactor = 2.00

WL_AB WL_CD

[STRUCTURE] [STRUCTURE]

Input n. = 4 Input n. = 4

Hidden 1 n. = 9 Hidden 1 n. = 7

Hidden 2 n. = 0 Hidden 2 n. = 0

Output n. = 1 Output n. = 1

2. PT_AB and PT_CD

A data base of 300 experimental points had been used for PT_AB, divided in two sub-sets: 150 points for training and remaining 150 for testing.

A data base of 384 experimental points had been used for PT_CD, divided in two sub-sets: 192 points for training and remaining 192 for testing.

Pickling Time models PT_AB and PT_CD have the same structure and both contain 7 nodes in the hidden layer.

The basic structures of the ANNs PT_AB and PT_CD are the following:

Code input = Back propagation

Code output = Sigmoidal

[INPUT VECTOR] [OUTPUT VECTOR]

Index n.0 = T Index n.0 = PT

Index n.1 = H

Index n.2 = Fe

Index n.3 = I

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[TRAINING]

Epoch = 50

SB freq = 1

Access = Random

SbCriteria = RMS error

Initialize = Random

Schedule Epoch H1Lr µ OLr µ

Cycle 0 20 0.40 0.40 0.40 0.40

Cycle 1 40 0.20 0.20 0.20 0.20

Cycle 2 50 0.10 0.10 0.10 0.10

f_offset = 0.01

ErrorFactor = 2.00

PT_AB PT_CD

[STRUCTURE] [STRUCTURE]

Input n. = 4 Input n. = 4

Hidden 1 n. = 7 Hidden 1 n. = 7

Hidden 2 n. = 0 Hidden 2 n. = 0

Output n. = 1 Output n. = 1

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Annex V

Program listing of the file VSA_VSPDlg.cpp :

//****************************************************** void CVSA_VSPDlg::EseguiDll (int Tank) //richiama la dll avendo precedentemente selezionarto il file di set relativi {hDLL = LoadLibrary(SceltaDll1); if((hDLL != NULL) )

//se la dll viene correttamente richiamata e linkata si procede alla elaborazione delle variabili di in- gresso {PropCalcStart_calc = (PropCal- cStart_FUNC)GetProcAddress(hDLL,"PropCalcStart"); if((PropCalcStart_calc)) {ret_code = (*PropCalcStart_calc)(WorkPath_ANN1,ret_mes,sCoil, Fla- gPrint_Prop,&NumSelectVar,EngProcData[Tank],OutputMap_Prop,&Out_Prop); }} FreeLibrary(hDLL);

//richiama la dll avendo precedentemente selezionato il file di set relativi hDLL = LoadLibrary(SceltaDll2); if((hDLL != NULL) )

//se la dll viene correttamente richiamata e linkata si procede alla elaborazione delle variabili di in- gresso {PropCalcStart_calc = (PropCal- cStart_FUNC)GetProcAddress(hDLL,"PropCalcStart"); if((PropCalcStart_calc)) {ret_code = (*PropCalcStart_calc)(WorkPath_ANN2,ret_mes,sCoil, Fla- gPrint_Prop,&NumSelectVar,EngProcData[Tank],OutputMap_Prop,&Out_Prop1); } } FreeLibrary(hDLL);}

//****************************************************** void CVSA_VSPDlg::VisualizzaRisultati ()

//stringa di appoggio dove andranno costruite le stringhe da rappresentare nei plot edit {char AnalogAcquisSting[120];

//scrive il parametro di output Iag sprintf(AnalogAcquisSting,"%.0f",Iag); BoxIag.SetTextEdit(AnalogAcquisSting, RED);

//scrive il parametro di output Ia per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[0].Ia); BoxIa1.SetTextEdit(AnalogAcquisSting, RED);

//scrive il parametro di output Ia per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[1].Ia); BoxIa2.SetTextEdit(AnalogAcquisSting, RED);

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//scrive il parametro di output Ia per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[2].Ia); BoxIa3.SetTextEdit(AnalogAcquisSting, RED);

//scrive il parametro di output Ia per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[3].Ia); BoxIa4.SetTextEdit(AnalogAcquisSting, RED);

//scrive il parametro di output Ipg sprintf(AnalogAcquisSting,"%.0f",Ipg); BoxIpg.SetTextEdit(AnalogAcquisSting, GREEN);

//scrive il parametro di output Ip per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[0].Ip); BoxIp1.SetTextEdit(AnalogAcquisSting, GREEN);

//scrive il parametro di output Ip per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[1].Ip); BoxIp2.SetTextEdit(AnalogAcquisSting, GREEN);

//scrive il parametro di output Ip per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[2].Ip); BoxIp3.SetTextEdit(AnalogAcquisSting, GREEN);

//scrive il parametro di output Ip per vasca sprintf(AnalogAcquisSting,"%.0f",DatiVasca[3].Ip); BoxIp4.SetTextEdit(AnalogAcquisSting, GREEN); PlotOutputIag.ChartY(Iag, RED, 1, STEP); PlotOutputIpg.ChartY(Ipg, GREEN, 1, STEP);}

//****************************************************** void CVSA_VSPDlg::CalcolaMedia (int j) //Calcola il peso K relativo alla vasca count-esima {CalcolaPesoK(j); /* //Rivedere : scrivere all'interno del buffer //il valore del parametro per la n-esima vasca DatiVasca[j].Ia = ((DatiVasca[j].Ia)/Coeff_Ia); DatiVasca[j].Ip = ((DatiVasca[j].Ip)/Coeff_Ip); */ //Rivedere : scrivere all'interno del buffer //il valore del parametro per la n-esima vasca DatiVasca[j].Ia = ((Out_Prop1.YS)/Coeff_Ia); DatiVasca[j].Ip = ((Out_Prop.YS)/Coeff_Ip); if((DatiVasca[j].Ia - ((int)DatiVasca[j].Ia))>0) DatiVasca[j].Ia = ((int)DatiVasca[j].Ia) + 1; else DatiVasca[j].Ia = ((int)DatiVasca[j].Ia); if((DatiVasca[j].Ip - ((int)DatiVasca[j].Ip))>0) DatiVasca[j].Ip = ((int)DatiVasca[j].Ip) + 1; else DatiVasca[j].Ip = ((int)DatiVasca[j].Ip); Iag += K[j]*DatiVasca[j].Ia; Ipg += K[j]*DatiVasca[j].Ip;}

//******************************************************

125

void CVSA_VSPDlg::InitVar()

{CBitmap appo;

//compone la directory in cui è presente la mappa di variabili di input e tutti i file di setup strcpy(WorkPath_ANN1, PercorsoFile); strcat(WorkPath_ANN1,"Dll1\\");

//compone la directory in cui è presente la mappa di variabili di input e tutti i file di setup strcpy(WorkPath_ANN2, PercorsoFile); strcat(WorkPath_ANN2,"Dll2\\");

//compone il nome della dll strcpy(SceltaDll1,WorkPath_ANN1); strcat(SceltaDll1,"PropDLL1.dll");

//compone il nome della dll strcpy(SceltaDll2,WorkPath_ANN2); strcat(SceltaDll2,"PropDLL2.dll");

//Imposta il range dei plot PlotOutputIag.SetRange(0,10,0,30); PlotOutputIpg.SetRange(0,10,2,55);

//Ogni volta che si esegue un set range, affinché il plot non si sporchi e necessario effettuare un reset PlotOutputIag.ResetPlot(); PlotOutputIpg.ResetPlot(); WinDiv = (WinAcq*60)/AcqInt; AcqInt = 1000*AcqInt;}

//****************************************************** void CVSA_VSPDlg::OnButtonOneshot ()

{static NoModal5INIT* g_Appo5 = NULL; CBitmap appoBit;

//crea la dialog relativa if(Multi_Tank)

//Cambia il titolo PICCOLO e la scritta sul pulsante di selezione modalità di funzionamento {SetDlgItemText(IDC_BUTTON_ONESHOT, "LINE-RUNNING" ) ; appoBit.LoadBitmap(IDB_BITMAP5); m_Grande.SetBitmap(appoBit);

//disabilita o abilita tasti di comando GetDlgItem(IDC_BUTTON_NOMODAL3)->EnableWindow(false); GetDlgItem(IDC_BUTTON_NOMODAL4)->EnableWindow(false); GetDlgItem(IDC_BUTTON_NOMODAL)->EnableWindow(false); GetDlgItem(IDC_BUTTON_NOMODAL2)->EnableWindow(false); SetDlgItemText(IDC_BUTTON_RUN, "CALC" ) ; g_Appo5 = new NoModal5INIT(this);} else

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//Cambia il titolo PICCOLO e la scritta sul pulsante di selezione modalità di funzionamento {SetDlgItemText(IDC_BUTTON_ONESHOT, "ONE-SHOT" ) ; appoBit.LoadBitmap(IDB_BITMAP3); m_Grande.SetBitmap(appoBit);

//disabilita o abilita tasti di comando GetDlgItem(IDC_BUTTON_NOMODAL3)->EnableWindow(true); GetDlgItem(IDC_BUTTON_NOMODAL4)->EnableWindow(true); GetDlgItem(IDC_BUTTON_NOMODAL2)->EnableWindow(true); GetDlgItem(IDC_BUTTON_NOMODAL)->EnableWindow(true); delete g_Appo5;} Multi_Tank = !Multi_Tank;}

//****************************************************** void CVSA_VSPDlg::OnButtonCalc () //verifica se il sistema è gia run oppure deve essere posto in run {if(RunStop) {RunStop = false; SetDlgItemText(IDC_BUTTON_RUN, "CALC" ) ; KillTimer(0);//interrompe OnTimer} else {RunStop = true; SetDlgItemText(IDC_BUTTON_RUN, "STOP" ) ;}

//verifica la modalità di funzionamento scelta : se One-Shot o Line running //Funzionamento ONE-SHOT if(!Multi_Tank) {Iag = 0; Ipg = 0; RunStop = false; SetDlgItemText(IDC_BUTTON_RUN, "CALC" ) ;

//esegue le dll per il calcolo dei parametri for(int j=0;j

//comanda alla finestra non modale n°5 di rappresentare i risultati del calcolo all'interno del plot ShowResult = true;}

//Funzionamento MULTI-TANK if(Multi_Tank && RunStop)

//apre il file dove sono presenti i valori delle variabili di input {InputF = fopen ("DataIn.txt", "r");

//inserisce il numero del coil in esame strcpy(sCoil, "Coil_100");

//legge prima riga del file dati fgets (text_var, 300, InputF);

//Inizializzazione timer acquisizioni elaborazione SetTimer(0,(unsigned int)(AcqInt),NULL);}}

127

//****************************************************** void CVSA_VSPDlg::OnButtonNomodal ()

{static NoModalINIT* g_Appo1 = NULL;

//distrugge la dialog precedentemente create delete g_Appo1;

//crea la dialog relativa g_Appo1 = new NoModalINIT(this);}

//****************************************************** void CVSA_VSPDlg::OnButtonNomodal2 ()

{static NoModal2INIT* g_Appo2 = NULL;

//distrugge la dialog precedentemente create delete g_Appo2;

//crea la dialog relativa g_Appo2 = new NoModal2INIT(this);}

//****************************************************** void CVSA_VSPDlg::OnButtonNomodal3 ()

{static NoModal3INIT* g_Appo3 = NULL;

//distrugge la dialog precedentemente create delete g_Appo3;

//crea la dialog relativa g_Appo3 = new NoModal3INIT(this);}

//****************************************************** void CVSA_VSPDlg::OnButtonNomodal4 ()

{static NoModal4INIT* g_Appo4 = NULL;

//distrugge la dialog precedentemente create delete g_Appo4;

//crea la dialog relativa g_Appo4 = new NoModal4INIT(this);}

//****************************************************** void CVSA_VSPDlg::Aggiorna_Scale ()

{static int count = 0; static bool appo1 = false; static bool appo2 = true; if(count != WinDiv) {BufferIag[count] = Iag; BufferIpg[count] = Ipg; count++;} else {count = 0;

128

appo1 = true;} if(((CButton*)GetDlgItem(IDC_CHECK_AUTOSCALEIAG))->GetCheck()) {int MaxVal_Iag=0; for(int i=0;iMaxVal_Iag) MaxVal_Iag = BufferIag[i]; PlotOutputIag.SetRange(0,WinDiv,0,MaxVal_Iag);} else PlotOutputIag.SetRange(0,WinDiv,0,30); if(((CButton*)GetDlgItem(IDC_CHECK_AUTOSCALEIGP))->GetCheck()) {int MaxVal_Ipg=0; for(int i=0;iMaxVal_Ipg) MaxVal_Ipg = BufferIpg[i]; PlotOutputIpg.SetRange(0,WinDiv,0,MaxVal_Ipg);} else PlotOutputIpg.SetRange(0,WinDiv,0,50);

//Barra mobile grafici char AppoString[40]; char tmpbuf[128]; if(appo1) {strtime( tmpbuf ); SetDlgItemText(IDC_STATIC_UNIT5, tmpbuf ) ; SetDlgItemText(IDC_STATIC_UNIT11, tmpbuf ) ; int AppoMin; int AppoOra; int AppoSec; AppoOra = atoi(strtok(tmpbuf,":")); AppoMin = atoi(strtok(NULL,":")); AppoSec = atoi(strtok(NULL,":")); for(int i = 0;i

129

else {AppoMin = 1; if(AppoOra!=24) {AppoOra++;} else {AppoOra = 0;}}}; sprintf(AppoString,"%02d:%02d:%02d",AppoOra,AppoMin,AppoSec); SetDlgItemText(IDC_STATIC_UNIT5, AppoString ) ; SetDlgItemText(IDC_STATIC_UNIT11, AppoString ) ; appo2 = false;}}

130 European Commission

EUR 23872 — Sensor-based online control of pickling lines

H. Schmermbeck, R. Wolters, B. Schmidt, W. Henning, A. Giannetti, M. Gabriele, G. Zangari, H. Lopez, I. Machon, A. Björk, S. Nilsson, M. Andersson, R. Bergström

Luxembourg: Office for Official Publications of the European Communities

2009 — 130 pp. — 21 × 29.7 cm

Research Fund for Coal and Steel series

ISBN 978-92-79-11589-9

ISSN 1018-5593 doi 10.2777/48068

Price (excluding VAT) in Luxembourg: EUR 20