In-situ characterization of the reaction progress of the fluid catalytic cracking reactions by laser diagnostic techniques
Sandra Milena López Zamora
Universidad Nacional de Colombia Facultad de Minas, Departamento de Procesos y Energía Medellín, Colombia 2018
In-situ characterization of the reaction progress of the fluid catalytic cracking reactions by laser diagnostic techniques
Sandra Milena López Zamora
Thesis submitted as partial requirement to obtain the title of degree of Doctor of Philosophy in Engineering
Director: Ph.D., Alejandro Molina
Codirector: Ph.D., Hugo de Lasa
Research group: Grupo de Investigación en Bioprocesos y Flujos reactivos
Universidad Nacional de Colombia Facultad de Minas, Departamento de Procesos y Energía Medellín, Colombia 2018
A mis padres: José Fernando e Irene
A mi hermana Mariluz
Gracias por estar ahí cuando más los necesito, porque es posible alcanzar cada sueño al contar con ustedes
To my eternal friend Mike Mosley Thanks for being always there
La educación es el gran motor del desarrollo personal. Es a través de la educación como las hijas de un campesino pueden convertirse en una médica y una ingeniera, el hijo de un minero puede convertirse en el jefe de la mina, o el hijo de trabajadores agrícolas puede llegar a ser presidente de una gran nación
Nelson Mandela
Acknowledgements
I want to express my gratitude to my supervisor Alejandro Molina for his guidance and supervision. I also express my gratitude to my co-supervisor Prof. Hugo de Lasa who gave me all his help, support and for encourage me during the development of this thesis. I truly appreciate the opportunity that professors Hugo de Lasa and Amir Farooq gave me to work and learn with their research teams in the University of Western Ontario (London, Canada) and King Abdullah University of Science and Technology (Thuwal, Saudi Arabia).
The financial support from the Colombian Science Foundation (COLCIENCIAS), the Colombian state oil company (ECOPETROL S.A.) under contract No. 0423 2013 and the Emerging Leaders in the Americas Program (ELAP) Canadian Exchange Program is gratefully acknowledged. I would also like to thank Florencia de Lasa for her assistance with the editing of the published paper.
To my friends and colleagues: Carlos Rivera, Yulanderson Salguedo, Abdualkaber Alkhlel, Noel Andrés Gómez Mendoza, Juan Esteban Duque, Juan Felipe Hincapié, Daniel Díaz, Juan David Alzate, Jorge Galvis, Danielle Ochoa, Juan José Arias for all the discussion related or not with this research. To Graciela de Lasa for her wisdom and advices. To Berniece Dellow and Susan Mosley for being my friends and support during the hard times. To Dylan Sweetnam, for his support during the last part of this thesis. Finally, I would like to say thanks to López and Zamora families, who are the most important part of my life, and to whom I still say: “... La fijación de metas distingue a unos hombres de otros. Y aquí lo más importante no es alcanzar dichas metas, sino luchar por ellas." (Hector Abad Gómez).
Resumen
Técnicas analíticas sofisticadas, como espectrometría de masas y cromatografía líquida de alta eficacia (HPLC), pueden ser usadas para medir el contenido de aromáticos y saturados en el alimento y productos del craqueo catalítico fluidizado (FCC). Sin embargo, los métodos ópticos tienen la ventaja de ser rápidos y no intrusivos, operando en modo sin contacto. Las mediciones de absorción láser de la concentración de combustibles se hacen frecuentemente en longitudes de onda del infrarrojo medio (MIR) cerca de 3.4 µm, que se superpone con las fuertes transiciones vibratorias del enlace C-H presente en hidrocarburos y garantiza una detección sensible incluso para longitudes cortas de medición. El espectro MIR para hidrocarburos individuales puede encontrarse en diferentes bases de datos, sin embargo, la información se limita a especies con bajo número de carbonos. En este sentido, se propuso un método de contribución de grupos (GCM) para la predicción del espectro de los diferentes compuestos presentes en una reacción de craqueo catalítico en la región 3200-2800 cm-1.
Esta tesis de doctorado considera el desarrollo de una metodología láser para el monitoreo in-situ-libre de partículas del progreso de la reacción de craqueo catalítico fluidizado (FCC) usando componentes modelo. El objetivo es contribuir a la caracterización de la química y especies químicas involucradas en FCC mediante la evaluación discreta del espectro infrarrojo (IR). La metodología propuesta considera el análisis MIR in-situ del cambio en la concentración de los grupos funcionales presentes en el compuesto modelo 1,3,5- triisopropylbenceno (1,3,5-TIPB) como indicador del progreso de la reacción FCC. Esto se realiza en el ánulo del equipo conocido como “CREC Riser Simulator”. De igual manera, el planteamiento considerado postula la aplicación de esta metodología MIR in-situ para la caracterización de los “lumps” de gases livianos y gasolina en el contexto de una unidad FCC industrial usando una botella de muestreo con dos cámaras bajo vacío.
Esta investigación fue llevada a cabo en el Grupo de Investigación Bioprocesos y Flujos Reactivos en la Universidad Nacional de Colombia. Se realizó además una pasantía en el grupo de investigación del profesor Hugo de Lasa (Universidad de Western - Ontario, Canadá) la cual permitió el desarrollo de un método de contribución de grupos (GCM) para la caracterización del progreso de la reacción en una reacción FCC modelo.
Palabras clave: Craqueo Catalítico Fluidizado (FCC), Método de contribución de grupos (GCM), Mid-IR (MIR), Enlace C-H, Progreso de Reacción.
Abstract
Sophisticated analytical techniques, such as mass spectrometry and high-performance liquid chromatography (HPLC), can be used to measure aromatic and saturate contents of the Fluid Catalytic Cracking (FCC) feedstock and products. However, optical methods have the advantage of being rapid and non-intrusive, operating in contact-less mode. Laser- absorption measurements of fuel concentration are often made at mid-infrared (MIR) wavelengths near 3.4 µm, which overlap with the strong C-H stretch vibrational transitions of hydrocarbons and guarantee sensitive detection even for short measurement path lengths. The MIR spectra for individual hydrocarbons can be found in different databases, however the information is limited to being used with species having low carbon number. In this sense, a Group Contribution Method (GCM) is proposed for the spectra prediction of the different compounds present in the catalytic cracking reaction in the region 3200-2800 cm-1.
This PhD thesis considers the development of a laser diagnostic methodology for “in-situ- free of particles” monitoring of fluid catalytic cracking (FCC) reaction progress using model compounds. The aim is to contribute to the characterization of the chemistry and chemical species involved in FCC through the discrete evaluation of the infrared (IR) spectra. The methodology proposed considers the in-situ MIR analysis of the change in the concentration of functional groups present in the model compound 1,3,5-triisopropylbenzene (1,3,5-TIPB) as indicator of FCC reaction progress. This is performed in the annulus of a CREC Riser Simulator. As well, the considered approach postulates the application of this in-situ MIR methodology for the characterization of the light gases and gasoline lumps in the context of Industrial FCC unit using a sampling bottle with two cameras under vacuum.
This research was carried out at the Grupo de Investigación Bioprocesos y Flujos Reactivos at Universidad Nacional de Colombia. An internship in the group of Professor Hugo de Lasa (Western University - Ontario Canada) allowed the development of a Group Contribution Method (GCM) for the characterization of the reaction progress of a model FCC reaction.
Keywords: Fluid Catalytic Cracking (FCC), Group Contribution Method (GCM), Mid-IR (MIR), C-H Bond, Reaction Progress.
Table of Contents
Pág.
Acknowledgements ...... VII
Resumen ...... IX
Abstract ...... XI
Table of Contents ...... XIII
List of Figures ...... XVI
List of Tables ...... XIX
List of symbols and abbreviations ...... XXI
Introduction ...... 1
1. Literature Review ...... 3 1.1. Fluid Catalytic Cracking (FCC) process ...... 3 1.1.1. Reaction Chemistry...... 4 1.1.2. Feed characterization ...... 6 1.1.3. Products characterization ...... 6 1.1.4. Gasoline ...... 7 1.2. Characterization of hydrocarbons by laser diagnostics...... 7 1.3. State of the Art ...... 9 1.3.1. Paraffins and iso-paraffins (Alkanes) ...... 10 1.3.2. Olefins (Alkenes) ...... 10 1.3.3. Aromatics ...... 10 1.3.4. Fuels ...... 11 1.3.5. Reaction progress ...... 11 1.3.6. FCC monitoring ...... 12 1.4. Fundamentals of Mid-Infrared (MIR) absorption ...... 12 1.4.1. Beer-Lambert Law ...... 13 1.5. Fundamentals of the 2949.85 cm-1 (3.39 µm) HeNe laser ...... 14 1.6. Conclusion ...... 15
Table of Contents XIV
2. Group Contribution Method for the Prediction of Adsorption Cross Section Coefficients in the Mid-Infrared (MIR) Absorption Range 3200-2800 cm-1 ...... 16 2.1. Introduction ...... 16 2.2. Statistic Parameters ...... 18 2.3. Theoretical Considerations. The Group Contribution Method ...... 19 2.3.1. Establishing a Group Contribution Method (GCM) ...... 19 2.3.2. C-H Bond Number Influence on the Absorbance of Hydrocarbons ...... 20 2.4. Proposed GCM Methodology for MIR Predictions...... 25 2.5. Results and Discussion ...... 28 2.6. Conclusions ...... 32
3. Validation of the Group Contribution Method ...... 35 3.1. Introduction ...... 35 3.2. Materials and Methods ...... 37 3.2.1. Absorption Cross Section Measurements ...... 37 3.2.2. IR measurement ...... 38 3.3. Results and discussion ...... 38 3.3.1. Pure Component Predictions ...... 38 3.3.2. Predicting Gasoline MIR Spectra ...... 40 3.3.3. Experimental validation of GCM ...... 44 3.4. Conclusions ...... 46
4. Monitoring the Catalytic Cracking of Model Compounds in the Mid Infrared (MIR) in the 3200-2800 cm-1 Range ...... 48 4.1. Introduction ...... 48 4.2. Prediction of catalytic cracking conversion by MIR ...... 50 4.2.1. 1-Hexene catalytic cracking ...... 50 4.2.2. 1,3,5-TIPB catalytic cracking ...... 55 4.3. Application of MIR and Fiber Optics in the CREC Riser Simulator ...... 61 4.3.1. Selected conditions for catalytic cracking ...... 61 4.3.2. Application in the CREC Riser Simulator ...... 61 4.4. Conclusions ...... 66
5. Prediction of Light gases and Gasoline Lump Contents ...... 67 5.1. Introduction ...... 67 5.2. Application of the GCM for the Prediction of Lumps in Premium and Regular Gasolines ...... 68 5.3. Parametric Sensitivity Analysis for Gasoline Lumps ...... 70 5.4. Applicability of MIR-GCM for the Prediction of Lumps in FCC ...... 71 5.4.1. Calculation Methodology ...... 76 5.4.2. Experimental error estimation ...... 77 5.4.3. Results ...... 78 5.5. Conclusions ...... 80
Table of Contents XV
6. General conclusions and perspectives ...... 83 6.1. General conclusions ...... 83 6.2. Recommendations for Future work ...... 84
7. Contributions related to this thesis ...... 85 7.1. Papers...... 85 7.2. Conferences ...... 85
References ...... 87
Appendix A: List of Components Employed for the Group Contribution Method ...... 101
Appendix B: List of C-H Bond Classifications for Each Component According to the Group Contribution Method ...... 105
Appendix C: Results for Cross Section at a 3.39 μm HeNe Wavelength and Integrated Band Intensities Calculated with GCM and Linear Approximations ...... 111
Appendix D: Matlab script for the calculation of the Group Contribution Method parameters ...... 113
List of Figures
Pág. Figure 1.1. Schematics of a typical FCC converter with single feed. Taken from [12] ...... 4 Figure 1.2. Energy levels of helium and neon. Taken from [86] ...... 14 Figure 2.1. Changes of Absorbance of n-Paraffins in the MIR Range Showing the Effect of Increasing the Number of C−H Bonds. Spectra reported were obtained from the PNNL database [80] at 50C. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam...... 20 Figure 2.2. Changes of the Absorption Cross Section Coefficient (σλ,i) with the Number of C- H Bonds Using a 2949.85 cm-1 (3.39 µm) HeNe Laser for Paraffinic, Olefinic, Naphthenic and Aromatic Species at 50 C...... 21 −1 Figure 2.3. Integrated Absorption Band Intensities (Ψi) in the 3200-2800 cm Range for Paraffins, Olefins, Naphthenes and Aromatic Species...... 23 Figure 2.4. Characteristic Infrared Absorption Spectra in the C−H MIR Stretching Region for Paraffins, Olefins, Naphthenes and Aromatics. Note: The broken vertical line at 2949.85 cm- 1 represents the frequency of the HeNe laser beam...... 26 Figure 2.5. Parity Plot of Experimental and Calculated Absorption Cross Section Coefficient (σλ,i). The solid line indicates 1:1 correlation. Note: all hydrocarbon species considered are in the C1-C16 range...... 28 Figure 2.6. Parity Plot of Experimental and Calculated Integrated Absorption Band Intensities (Ψi). The solid line indicates 1:1 correlation. Note: all hydrocarbon species considered are in the C1-C16 range...... 29 Figure 2.7. Comparison between Experimental and Calculated MIR Spectra for individual Chemical Species: (a) n-Heptane, (b) Isooctane, (c) 1-Nonene, (d) Cis-2-pentene, (e) Cycloheptene and (f) sec-Butylbenzene. (--) PNNL (-) Predicted...... 31 Figure 2.8. Statistical Moments of Different Order. Symbols and Legend reported in Figure 2.5...... 32 Figure 3.1. Schematic description of the catalytic cracking of 1,3,5-TIPB. Adapted from [146] ...... 36 Figure 3.2. Experimental setup for cross section measurements at 2949.85 cm-1 (3.39 µm). Codes: Ch: Chopper, PD: PbSe Photodetector, DAQ: Data Acquisition...... 37 Figure 3.3. GCM Calculated Absorption Cross-Section Values and Reported Spectra for n- dodecane at 50 °C. The different lines represent: This work (-), Sharpe 2004 [80] results (--) and Klingbeil 2007 [22] (…) Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam wavenumber...... 40
List of Symbols and Abbreviations XVII
Figure 3.4. Comparison between the Predicted and the Experimental MIR Spectra of : (a) Regular gasoline comprised of 20.6% n-paraffins, 32.8% isoparaffins, 18.6% olefins, 27.9% aromatics, 0% ethanol (b) Premium gasoline comprised of 12.3% n-paraffins, 75.3% iso- paraffins, 5.4% olefins, 7.1% aromatics, 0.0% ethanol...... 42 Figure 3.5. R2 and RER Values for the 21 Gasoline Samples...... 43 Figure 3.6. Average Errors for the Spectra of the 21 Gasoline Samples: a) Average Absolute Error (휀), b) Average Signed Error or BIAS (휀푠𝑔푛), c) Average Relative Absolute Error (휉) and d) Average Relative Signed Error (휉푠𝑔푛) Legend: (o) Regular gasoline (without alcohol), (*) Regular gasoline (with alcohol), (+) Premium gasoline(without alcohol), (x) Premium gasoline(with alcohol)...... 44 Figure 3.7. Comparison of Absorption Cross Sections between experimental TGA-FTIR results and the GCM prediction for 1,3-DIPB. Note: the broken vertical line at 2949.85 cm-1 represent the HeNe laser beam wavenumber...... 45 Figure 3.8. Comparison of Absorption Cross Sections between the experimental TGA-FTIR results and the GCM prediction for 1,3,5-TIPB. Note: the broken vertical line at 2949.85 cm- 1 represent the HeNe laser beam wavenumber...... 45 Figure 4.1. Absorption cross sections for the reaction products of 1-hexene catalytic cracking. Note: the broken vertical line at 2949.85 cm-1 represent the frequency of the HeNe laser beam ...... 51 Figure 4.2. yi i Weighted Absorption Cross sections for the reaction products of 1-Hexene catalytic cracking over 100%-SAPO-34 catalyst at a 35% conversion. Note: the broken vertical line at 2949.85 cm-1 represent the frequency of the HeNe laser beam ...... 51 Figure 4.3. Changes of Integrated Absorption Band Intensities and 1-Hexene Molar Fractions during the Catalytic Cracking of 1-Hexene. Catalysts: Meso-SAPO-34, 30%-SAPO- 34 and 100%-SAPO-34 at 500 °C. Note: Doted lines represent the 10% error with respect to the linear trend...... 52 Figure 4.4. Changes of Absorption Cross Section Coefficient and 1-Hexene Molar Fraction during the Catalytic Cracking of 1-Hexene. Catalysts : Meso-SAPO-34, 30%-SAPO-34 and 100%-SAPO-34 at 500 °C. Note: Doted lines represent the 10% error with respect to the linear trend...... 53 Figure 4.5 Integrated Absorption Band Intensity as a Function of 1-Hexene Conversion during the Catalytic Cracking of 1-Hexene. Catalysts: Meso-SAPO-34, 30%-SAPO-34 and 100%-SAPO-34 at 500°C. Note: Doted lines represent the 10% error with respect to the linear trend...... 54 Figure 4.6. Absorption Cross Section Coefficient as a Function of 1-Hexene Conversion during the Catalytic Cracking of 1-Hexene. Catalyts: Meso-SAPO-34, 30%-SAPO-34 and 100%-SAPO-34 at 500°C. Note: Doted lines represent the 10% error with respect to the linear trend...... 55 Figure 4.7. Schematic Description of the Catalytic Cracking of 1,3,5-TIPB. Adapted from [146] ...... 56
List of Symbols and Abbreviations XVIII
Figure 4.8. Absorption Cross Section Coefficient of the Various Chemical Species Involved in the 1,3,5-TIPB Catalytic Cracking. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam...... 56 Figure 4.9. Weighted Absorption Cross Section Coefficient of the Reaction Products of 1,3,5- TIPB Catalytic Cracking having a 36% 1,3,5 TIPB Conversion. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam...... 57 Figure 4.10. Changes of the Integrated Absorption Band Intensity and 1,3,5-TIPB Molar Fractions and during 1,3,5-TIPB Catalytic Cracking. Note: Doted lines represent the 10% error with respect to the linear trend ...... 58 Figure 4.11. Changes of the Absorption Cross Section Coefficient and the 1,3,5-TIPB Molar Fractions during 1,3,5-TIPB Catalytic Cracking. Note: Doted lines represent the 10% error with respect to the linear trend ...... 58 Figure 4.12.Changes of Integrated Absorption Band Intensities with 1,3,5-TIPB Conversion. Note: Doted lines represent the 10% error with respect to the linear trend ...... 60 Figure 4.13. Changes of 1,3,5-TIPB Conversion with the Absorption Cross Section Coefficient. Note: Doted lines represent the 10% error with respect to the linear trend ...... 60 Figure 4.14. Schematic Diagram of the CREC Riser Simulator Experimental Set-up [167] ... 62 Figure 4.15. Molar Fractions of 1,3,5 TIPB and Propylene in the CREC Riser Simulator for: i) Vacuum (1.5 psi) and ii) 14.7 psi. Note: Reported values represent averages for at least 3 repeats. Blue lines represent measurements at 14.7 psi and black lines represents measurements at 1.5 psi...... 63 Figure 4.16. CREC Riser Simulator Coupled with MIR Measurements. The enlarged view of the CREC Riser Simulator annulus highlights the measuring volume and MIR beam...... 64 Figure 4.17. MIR Predicted Results for the 1,3,5-TIPB Catalytic Cracking Conversion and its Comparison with Experimental Data from the CREC Riser Simulator. The solid line indicates 1:1 correlation. Note: Calculated conversion uses Eq(10) emulating mix HeNe laser measurements...... 65 Figure 5.1. Parity Plot for the Prediction of the different Lumps in Gasoline. Blue color represents regular gasolines while black is used for premium gasolines. Note: (o) n-Paraffins, (*) Iso-Paraffins, (+) Olefins and (x) Aromatics ...... 69 Figure 5.2. Changes of dF/dxi for various premium gasoline molar fraction lumps: a) n- Paraffins, b) iso-paraffins, c) olefins, d) aromatics...... 71 Figure 5.3. CREC-MIR-GSS in a “free of particle” environment as considered to be implemented in an FCC industrial Unit. Codes: PD: Photodetector, DAQ: Data Acquisition ...... 72 Figure 5.4. Representation of Adsorption Cross Section Coefficient for Gasolines, Light Gases and the mixture Light Gases + Gasolines ...... 73 Figure 5.5. Hysis simulation of the CREC MIR Gasoline Sampling System (CREC-MIRGSS) conditions ...... 78 Figure 5.6. Parity plot results for Light Gases, a) paraffins and b) olefins ...... 79 Figure 5.7. Parity plot for gasoline, a) paraffins, b) olefins, c) naphthenes and d) aromatics ...... 80
List of Tables
Pág. Table 1.1. Main Reactions in FCC Catalysis. (Taken from [1]) ...... 5 Table 2.1. Statistics Employed for the Evaluation of the Group Contribution Method...... 18 Table 2.2. Fitting Parameters and Statistical Indicators by Assuming a Linear Relationship between Absorption Cross Section Coefficient (σλ,i) and the Number of C-H Bonds for Different Chemical Lumps at a 3.39µm Wavelength Using a HeNe Laser...... 22 Table 2.3. Parameters and Statistics Assuming a Linear Tendency of Different Functional Groups between 3200-2800 cm-1...... 24 Table 2.4. Type of C-H Bonds, as Related to the GCM Parameters...... 27 Table 2.5. Statistics for the Prediction of the Absorption Cross Section Coefficient (σλ,i) at a HeNe Wavelength and Integrated Absorption Band Intensities (Ψi)...... 30 Table 3.1. Comparison of Absorption Cross Section Coefficient (σλ,i) with Data from the Technical Literature at 3.39 µm. Deviations are reported as percentual differences (% Dif)...... 39 Table 3.2. Summary of Absorption Cross Sections at 3.39 µm ...... 46 Table 4.1. Comparison of 1-Hexene and 1,3,5 TIPB...... 62 Table 5.1. Statistical Indicators for the Prediction of Regular and Premium Gasolines Lumps...... 69 Table 5.2. Products distribution for Kraemer experiments [168] ...... 74 Table 5.3. Main products for Gasoline fraction ...... 75 Table 5.4. Statistic parameters for Light Gases and Gasoline lumps predictions ...... 79
List of symbols and abbreviations
Symbols with Latin letter I Light Intensity c Concentration [mol m-3] F Function h Plank’s constant [J/s] L Path Length [m] n Number of Bonds P Pressure [Pa] R2 Coefficient of Determination T Temperature [°C] V Volume [m3] X Conversion y Molar Fraction
Greek letters 훼 Absorbance 훾 Bond Type Coefficient ε Average Absolute Error ξ Average Relative Absolute Error 휆 Wavenumber [cm-1] 휎 Absorption Cross Section Coefficient [m2 mol-1] Ψ Integrated Absorption Band Intensity [m2 mol-1 cm-1] 푣 Frequency of the light
Subindex i Species mix Mixture o Initial obj Objective sgn Signed
Acronyms ASTM American Society for Testing Materials
List of Symbols and Abbreviations XXII
ATR Attenuated Total Reflectance BDP Barrels Per Day CREC Chemical Reactor Engineering Centre CREC-MIRGSS CREC MIR Gasoline Sampling System DFB Distributed Feedback laser DFG Difference-Frequency-Generation DG Dry Gas DIPB 1,3-diisopropylbenzene ECQCL External Cavity Quantum-Cascade Laser FAME Fatty Acid Methyl Esters FCC Fluid Catalytic Cracking FIR Far-Infrared Region GC Gas Chromatography GCM Group Contribution Method GC-MS Gas Chromatography – Mass Spectrometry GEISA Management and Study of Spectroscopic Information HCO Heavy Cicle Oil HeNe Helium Neon HITRAN High-resolution transmission molecular absorption database HPLC High-Performance Liquid Chromatography IR Infrared MIR Mid-Infrared MIR-GCM Coupled Mid-Infrared – Group Contribution Method Nd:YAG Neodymium-doped Yttrium Aluminum Garnet NIR Near-Infrared Region LCO Light Cycle Oil LIDAR Laser Imaging Detection and Ranging LG Light Gases LIF Laser-Induced Fluorescence LPG Liquefied Petroleum Gas PIONA Paraffins, Isoparaffins, Olefins, Naphthenes and Aromatics PLIF Planar Laser Induced Fluorescence PLS Partial Least Squares PNAs Polynuclear Aromatics PNNL Pacific Northwest National Laboratory PSD Particle Size Distribution RER Range Error Ratio RMSEP Root Mean Squared Error of Prediction RON Research Octane Number ROT Riser Outlet Temperature SAPO Silicoaluminophosphate STP Standard Temperature and Pressure
List of Symbols and Abbreviations XXIII
TDL Tunable Diode Laser TDLAS Tunable Diode Laser Absorption Spectroscopy TIPB 1,3,5-triisopropylbenzene UV Ultra-violet VGO Vacuum Gas Oil
Introduction
Petroleum refining is considered the world's largest business (about $2 trillion per year) [1]. Some observers consider petroleum refining as a technologically mature process; however, it is currently undergoing huge technological and economic changes in response to trends of decreasing feedstock quality, increasingly stringent fuel quality requirements, reduction of carbon dioxide (CO2) emissions and desire of decreasing the dependency of fossils sources [1–3]. A petroleum refinery is composed of several processing units which convert the raw crude oil into usable products such as gasoline, diesel, jet fuel, and heating oil [2]. Among the process technologies in the refinery, fluid catalytic cracking (FCC) is one of the most important and profitable technologies, its installed capacity worldwide is 14 million barrels/day [4] as reported in [5,6].
The regular FCC feedstock is the fraction of crude oil that boils in the 330-550 °C range [2]. FCC is used to convert large molecules (C30-40+) into smaller and more useful products [1]. In the next two decades, the FCC process remains an area of intense research activity to face the new challenges of producing cleaner fuels from heavier or unconventional feedstocks [7]. The challenge is to re-invent the FCC process and catalyst into a refinery solution, that will give a high conversion of heavy hydrocarbons to transportation fuels with low aromatic content, with the option of also making light olefins from heavier residual feedstocks [8].
Rapid, non-intrusive sensing is desirable to monitor the concentrations of multiple species in the process streams. Optical techniques employing the Infrared (IR) spectra are considered a good option for gas sensing. The IR spectrum can be regarded as a fingerprint of the molecule [9]. With the aim to monitor in situ the reaction progress in a FCC process, this thesis presents the development of laser diagnostic methodology for monitoring the FCC reactions progress and has as major scientific contribution the characterization of the chemistry involved in FCC by discrete evaluation of the IR spectra.
This thesis has six chapters. Chapter 1 is a literature review and gives a review on the application of optical techniques to the hydrocarbons characterization. Chapter 2 focuses on the development of a group contribution method for the prediction of hydrocarbon absorption spectra in the MIR region (3200-2800 cm-1). Chapter 3 deals with the validation of the Group Contribution Method by using literature and experimental data. MIR for pure compounds and gasoline contained hydrocarbons is reported. Chapter 4 deals with the
Introduction 2
application of MIR for the prediction of the reaction progress of catalytic cracking reactions such as: 1-hexene and 1,3,5-TIPB. Chapter 5 presents the applicability of the MIR-GCM coupled technique for the prediction of light gases and gasoline lumps. Chapter 6 reports the conclusions and the recommendations for future research. Chapter 7 describes the list of contributions related to this PhD thesis.
This is the first study that the author is aware of, that reports a Group Contribution Method (GCM) for MIR absorbance predictions and its application to complex hydrocarbon mixtures such as the ones found in light gases and gasolines is reported. It is shown that MIR is-situ measurements in a free of particles environment and the GCM allows to establish progress of the catalytic reaction and light gases and gasoline predictions in both a CREC Riser Simulator and in an FCC commercial unit.
One paper was published in the Chemical Engineering Science journal as result of this research [10] and a second one was submitted for review and publication to Industrial Engineering Chemistry Research [11].
1. Literature Review
1.1. Fluid Catalytic Cracking (FCC) process
In the FCC process, heavier feedstocks such as heavy gas oils (C20-40, 350-550 °C) or vacuum resid (>C40, >550 °C), are cracked to high-value transportation fuels in the gasoline-range (largely iso-paraffins and aromatics) [1,2]. An advantage of FCC is that it can produce high yields of valuable propylene and butylenes, which are used as feedstock in the chemical industry or as building blocks for a very clean gasoline [8].
The converter zone in FCC consists of two coupled reactors, called riser and regenerator; between them, there is a catalyst stripper (Figure 1.1). Common dimensions of the riser are: diameter from 0.8 - 1.3 m, length from 30 - 40 m and average residence time of 3 - 10 s; in contrast, the regenerator is about 10 - 15 m diameter. A microspherical catalyst (PSD ranges from 0.5 to 150 µm) is employed, (about 270 ton-300 ton of catalyst in a 40000 BPD unit) [2,12]. The ideal riser behavior simulates a plug flow reactor, where catalyst and vapor travel the length of the riser [2]. The product distribution on the riser reactor depends mainly on cracking temperature, pressure, catalyst to oil ratio, space velocity (or contact time), and catalyst activity [13]. The most important process variable to control conversion rates and product distribution is temperature. Increasing temperature from 500 °C to 600 °C results in a higher conversion but a decreasing gasoline yield due to secondary cracking to light products including methane. By increasing temperature, the alkene content and hence the octane rating increase [1]. However, at high temperatures, several end products including dry gas and coke are obtained, and thus the quality of light oil is decreased [14]. The conversion also increases as more coke is removed from the equilibrium catalyst during regeneration. An increase in the ratio of catalyst to oil in the feed also increases conversion. It is therefore essential to clearly understand what product slate is wanted so that process conditions can be appropriately chosen [1].
Chapter 1 4
Figure 1.1. Schematics of a typical FCC converter with single feed. Taken from [12]
1.1.1. Reaction Chemistry A complex series of reactions take place when a large gas oil molecule contacts the catalyst at 650-760 °C. Table 1.1 summarizes the main reactions for the FCC process. If a high-activity zeolite catalyst is employed, all of the cracking reactions take place in less than 10 seconds. In this way, efficient contact of the feed and regenerated catalyst is critical for achieving the desired cracking reactions [2].
The product distribution in catalytic cracking greatly depends on the chain propagation mechanism, particularly on the number of propagation events per initiation step. Controlling this parameter through reaction conditions as well as the design of the zeolite- catalyst is possible [1]. Although most cracking in the FCC is catalytic, thermal cracking reactions also occur. Thermal cracking is caused by factors such as non-ideal mixing in the riser and poor separation of cracked products in the reactor [2]. The product distribution from thermal and catalytic cracking is different because these two processes proceed via different mechanisms. Thermal and catalytic cracking can be described as follows:
Chapter 1 5
Table 1.1. Main Reactions in FCC Catalysis. (Taken from [1]) Reactant Reaction Products 퐶푟푎푐푘푖푛푔 Alkanes → Alkanes + Alkenes 퐶푟푎푐푘푖푛푔 → LPG Alkenes 퐶푦푐푙푖푧푎푡푖표푛 → Naphthenes 퐼푠표푚푒푟푖푧푎푡푖표푛 퐻 푡푟푎푛푠푓푒푟 → Branched alkenes → Branched alkanes Alkenes 퐻 푡푟푎푛푠푓푒푟 → Alkanes 퐶푦푐푙푖푧푎푡푖표푛 → 퐷푒ℎ푦푑푟표푔푒푛푎푡푖표푛 Coke → 퐶푦푐푙푖푧푎푡푖표푛 → Alkenes 퐷푒ℎ푦푑푟표푔푒푛푎푡푖표푛 퐷푒ℎ푦푑푟표푔푒푛푎푡푖표푛 Naphthenes → Cycloalkenes → Aromatics 퐼푠표푚푒푟푖푧푎푡푖표푛 → Naphthenes with different rings 푆푖푑푒−푐ℎ푎푖푛 푐푟푎푐푘푖푛푔 → Unsubsituted aromatics + Alkenes 푇푟푎푛푠푎푙푘푦푙푎푡푖표푛 Aromatics → Different alkylaromatics 퐷푒ℎ푦푑푟표푔푒푛푎푡푖표푛 퐴푙푘푦푙푎푡푖표푛 → Polyaromatics → Coke
Thermal Cracking Thermal Cracking depends on temperature and time, it occurs in absence of catalysts when hydrocarbons are exposed to high temperatures (425-650 °C). The initial step in thermal cracking is the formation of free radicals which are extremely reactive and short-lived. Products rich in C1 and C2, and a fair amount of alpha- olefins are formed. Free radicals undergo little branching (isomerization). One of the drawbacks of this reaction in FCC is that a high percentage of the olefins formed during intermediate reactions polymerize and condense directly to coke [2].
Catalytic Cracking The overall effect of catalytic cracking reactions is the primary cracking of the heavy hydrocarbons leading to the desired gasoline-range hydrocarbons, followed by excessive secondary cracking, which produces unwanted light gases [1]. In addition, as a consequence of the cracking reactions, a hydrogen-deficient material called “coke” is deposited on the catalyst (about 0.8-1 wt%), reducing the catalytic activity [2]. Coke is removed through regeneration, a relatively slow process compared to the riser reaction time [8]. The heat absorbed by the catalyst during regeneration provides the energy required to evaporate and heat the feed to its desired reaction temperature (about 560-580 °C) and to provide the heat for the overall endothermic reaction [12].
Cracking reactions involve C-C bond rupture via formation of positive-charged atoms called carbocations (carbenium and carbonium ions) catalyzed by Brønsted and Lewis acid sites of
Chapter 1 6
an acid solid such as SiO2-Al2O3 or zeolites [1]. Carbocations are formed when feed contacts the regenerated catalyst and vaporizes. The stability of carbocations depends on the nature of alkyl groups attached to the positive charge. Tertiary carbocations are highly stable and are responsible for the reaction propagation. As the reaction proceeds, the number of carbocations decreases due to an absence of substituents for stabilizing the formed carbocations [15]. This reduce the reactivity of the catalytic cracking reactions. . In addition to the primary cracking reactions there are a number of secondary acid-catalyzed reactions that occur during hydrocarbon cracking, such as skeletal and/or cis-trans isomerizations, aromatics alkylation, dealkylation, branching, cyclization, hydrogen transfer and polymerization [1].
1.1.2. Feed characterization Catalytic cracking is able to process different kinds of feedstock. The ability to crack each hydrocarbon type in the feedstock is quite different and depends on the feasibility to form carbonium and/or carbenium ions in presence of the solid acid catalyst [12]. The hydrocarbon types in the FCC feed are broadly classified as paraffins, iso-paraffins, olefins, naphthenes, and aromatics (PIONA) [2]. FCC feeds are predominately paraffinic, the paraffinic carbon content is typically between 50 and 65 wt% of the total feed.
Paraffins (CnH2n+2) are easy to crack and normally yield the greatest amount of total liquid products. Iso-paraffins (CnH2n+2) contains chains of single bond C-H chains. Olefins (CnH2n) typical content in the FCC feed is <5 wt%, unless unhydrotreated coker gas oils are being charged. Olefins are unstable and can react with themselves or with other compounds. They are not the preferred feedstocks to an FCC unit because they indicate thermally produced oil and often polymerize to form undesirable products such as slurry and coke.
Naphthenes (CnH2n) are cyclic hydrocarbons and are considered desirable FCC feedstocks because they produce high-octane gasoline. Finally, aromatics (CnHn) are very stable and are not a preferred feedstock because few of the molecules will crack. The cracking of aromatics mainly involves breaking off the side chains resulting in excess fuel gas yield. Some of the aromatic compounds contain several rings (polynuclear aromatics, PNAs) than can interact to end up on the catalyst as carbon residue (coke) [2].
1.1.3. Products characterization The FCC production objectives change depending on market demands and environmental policies. During the 2nd World War and from 90s to the date, the policy was the maximization of the yield to gasoline, which requires the increase of the riser outlet temperature (ROT). During 1980s and part of the 1990s, along the cold season, many refiners switched to middle distillates mode of operation of the FCC converter, in order to increase yield to light cycle
Chapter 1 7
oil (LCO); this LCO was burned to heat crude oil from reservoirs located in cold places [12]. With the continually growing demand of propylene, operators of FCC units look increasingly to the petrochemicals market to boost their revenues by taking advantage of economic opportunities that arise in the propylene market [16]. The worldwide demand for propylene has been increasing at an annual average rate of 5.7% since 1991; in year 2000, propylene production was about 52 million tonnes, and it is projected that the demand will grow to 130 million tonnes by year 2023 [17,18].
The FCC products can be classified in: light cycle oil (LCO, hydrotreated downstream to produce diesel and fuel oil; boiling point > 221 °C), gasoline (C5s - C12s; 38.5 °C < boiling point < 221 °C) and liquefied petroleum gas (LPG, C3s - C4s) and some by-products such as dry gas (DG, used to generate thermal energy in the refinery, H2 - C2), sour gas (H2S, sent to Klaus Sulphur recovering processes), heavy cycle oil (HCO, considered as unconverted feedstock) and solid coke [12]. The most important product in FCC is gasoline, the second one is LPG, particularly if the FCC unit supplies it to downstream processes; usually LPG yield is around 12 wt.% - 16 wt.%. Typical yields of other sub-products are: DG (5 wt.%), LCO (15 wt.%) and HCO (8 wt.%). About 4 wt.% - 6 wt.% of the original feedstock converts to coke [12].
1.1.4. Gasoline Gasoline is one of the most important products of the FCC process. It is considered the most valuable product of a catalytic cracking unit, reaching up to 35 vol% of the total US gasoline pool [2]. There are two important characteristics for the gasoline: i) yield and ii) quality.
Gasoline yield can be improved by: increasing the catalyst to oil ratio and decreasing the pre-heat temperature, increasing the catalyst activity, increasing the fresh catalyst addition, increasing the reactor temperature or lowering carbon on the regenerated catalyst [2]. In terms of gasoline quality, the measurements can be made by: i) octane, ii) benzene or iii) sulfur.
1.2. Characterization of hydrocarbons by laser diagnostics
Sophisticated analytical techniques, such as mass spectrometry and high-performance liquid chromatography (HPLC), can be used to measure aromatic and saturate contents of the FCC feedstock. American Society for Testing Materials (ASTM) methods D2549, D2786, and D3239 can be used to measure total paraffin, naphthene, and aromatic ring distributions [2]. However, rapid, non-intrusive sensors are desirable to monitor the concentrations of multiple species in its process streams, so that the yield of the desired end products can be optimized [19].
Chapter 1 8
Laser based methods present the inherent advantage of operating in situ and in contact- less mode yielding timely results without interfering with the system under investigation. In contrast to techniques that rely on sampling, the measurements can be done in a timely fashion and are not so prone to errors [20]. Laser analysis techniques are at the threshold of routine applications in environmental monitoring and industrial process gas analysis [21]. Optical-absorption diagnostics have been used to probe various species in these environments and to infer quantities such as concentration, temperature, pressure and velocity. However, there have been only a limited number of demonstrations of optical diagnostics for hydrocarbon fuels in a context different from combustion [22].
Optical methods are able to take advantage of the infrared (IR) irradiation which may be used in quantitative or qualitative analysis. IR spectrum can be used as a fingerprint for molecules identification by the comparison of the spectrum from an unknown with previously recorded reference spectra [23]. The bands that appear in an infrared spectrum can usually be assigned to particular parts of a molecule, producing what are known as group frequencies [9]. The vibrational spectrum of a molecule is considered to be a unique physical property and is characteristic of the molecule [23]. The IR spectrum can be divided in: i) Near-Infrared Region (NIR, 13000 - 4000 cm-1, 0.77-2.5 m, ii) Mid-Infrared Region (MIR, 4000 - 400 cm-1, 2.5-25 m) and iii) Far-Infrared Region (FIR, 400 - 100 cm-1, 25-100 m).
Near-Infrared Region (NIR): The absorptions observed in the near-infrared region are overtones or combinations of the fundamental stretching bands which occur in the 3000 - 1700 cm-1 (3.3-5.9 m) region [24]. The resulting bands in the near infrared are usually weak in intensity, which generally decreases by a factor of 10 from one overtone to the next. The bands in the near infrared are often overlapped, making them less useful than the mid- infrared region for qualitative analysis. However, there are important differences between the near-infrared positions of different functional groups and these differences can often be exploited for quantitative analysis [9].
Mid-Infrared Region (MIR): The mid-infrared spectrum can be approximately divided into four regions and the nature of a group frequency may generally be determined by the region in which it is located. The regions are generalized as follows: the X-H stretching region, 4000 - 2500 cm-1 (2.5-4.0 m); the triple-bond region, 2500 - 2000 cm-1 (4.0-5.0 m); the double- bond region 2000 - 1500 cm-1 (5.0-6.7 m) and the fingerprint region 1500 - 600 cm-1 (6.7- 16.7 m) [9]. C-H stretching bands from aliphatic compounds occur in the range 3000 - 2850 cm-1 (3.3-3.5 m). If the C-H bond is adjacent to a double bond or aromatic ring, the C-H stretching wavenumber increases and absorbs between 3100 and 3000 cm-1 (3.2-3.3 m) [9]. C=C stretching occurs at around 1650 cm-1 (6 m), but this band is often absent for symmetry or dipole moment reasons [9].
Chapter 1 9
Far-Infrared Region (FIR): This region is more limited than the mid infrared for spectra– structure correlations but does provide information regarding the vibrations of molecules containing heavy atoms, molecular skeleton vibrations, molecular torsions and crystal lattice vibrations [9].
It has been reported that mid-IR is more sensitive and has a higher absorption strength than near-IR. MIR analysis is fast, cheap, simple to apply, requires limited or no sample preparation (if attenuated total reflectance, ATR, is employed), and can be portable [25].
Laser-absorption measurements of fuel concentration are often made at mid-infrared wavelengths near 2941 cm-1 (3.4 m), which overlap with the strong C-H stretch vibrational transitions of hydrocarbons, ensuring sensitive detection even for short measurement path lengths [26].There are many examples of optical absorption diagnostics for fuel concentration because fuel concentration is an important quantity. Wavelength-tunable mid-IR lasers are becoming commercially available, providing more freedom in wavelength selection for increased sensitivity to key hydrocarbon species [22]. Near-IR diagnostics have become popular because of the advent of high-quality telecommunications diode lasers. Near- IR fuel diagnostics generally exploit the first overtone of the C-H stretch near 1.6-1.8 m and often take advantage of the tunability of diode lasers to enhance sensitivity [22].
1.3. State of the Art
Optical characterization of species and reactions progress has been studied previously by authors employing different techniques such as quantum-cascade laser [27], linear Raman scatering [28], spectral and time-resolved fluorescence and incandescence [29], Differential-absorption LIDAR [30], Laser-induced fluorescence (LIF) [31,32], planar laser- induced fluorescence (PLIF) [33] and tunable diode laser (TDL) [34], among others.
The use of laser techniques has been extended to the qualitative or quantitative determination of different species such as: oxygen [35–41], hydrochloric acid [42,43], methane [44–49], water [50–56], amonia [57–59], among others. Additionally, low chain hydrocarbons have been previously studied: methane [44–49], ethylene (C2H4) [60], propylene (C3H6) [60,61], acetylene (C2H2) [62], ethane (C2H6), 1,3-butadiene (C4H6) [61], toluene [26], n-dodecane [26], n-decane [26], among others. This thesis focuses in the prediction of the reaction progress for FCC by using MIR. The principal groups present in the FCC process are: parafins, iso-paraffins, olefins, naphthenes and aromatics. The following sections describe the state of the art of the evaluation of the concentration of these species and the progress of different reactions by laser techniques.
Chapter 1 10
1.3.1. Paraffins and iso-paraffins (Alkanes) For monitoring of alkanes, the C–H stretch vibrational absorption can be exploited. The n- heptane mole fraction has been monitored with IR He–Ne laser absorption. In the case of n-heptane oxidation, at early reaction times the absorption at 2949.85 cm-1 (3.39 m) is dominated by the n-heptane [63]. N-dodecane and n-decane were measured by tunable mid-infrared laser sources near 2941 cm-1 (3.4 m), using difference-frequency-generation (DFG) [26]. In the case of iso-octane, an infrared absorption method with a 2948 cm-1 (3.392 m) He–Ne laser was used to determine the hydrocarbon fuel concentration near the spark plug in a spark-ignition engine [64,65].
1.3.2. Olefins (Alkenes) Different alkenes have been determined employing laser techniques. The spectroscopic detection of ethylene and propylene by widely-tunable rapid-scanning Mid-IR Laser Spectrometer has been reported. The system was based on DFG and has a tuning range of 3283-2812 cm-1 (3.0-3.6 m), corresponding to 471 cm-1 (0.5 m) of mode-hop-free tuning and delivers 58823 cm-1 (0.17 m) of mid-IR power with 6 mW of near-IR power launched into the waveguide [60]. External cavity quantum-cascade laser (ECQCL) system (Daylight Solutions 11100-UT, 917 cm-1 (10.9 m), 100 kHz) were used for propene concentration for combustion gases from the pyrolysis of n-butane in a shock tube [27]. Ethylene mole fraction was monitored by taking advantage of the fortuitous overlap of the P(14) line of the CO2 gas laser at 950 cm-1 (10.532 m) with the strong ethylene absorption band near 943 cm-1 (10.6 m). Finally, tunable diode laser (TDL) spectroscopy at 891 cm-1 (11.2 m) was used to collect the molecular spectra for 1,3-butadiene and propylene [61].
1.3.3. Aromatics The evaluation of the concentration of aromatic compounds traditionally takes advantage of its absorption in the UV region. Measurements of toluene and other aromatic hydrocarbons by differential-absorption LIDAR in the near-ultraviolet were developed by the third harmonic of the Nd:YAG laser [30]. Laser Induced Emission measurements were employed for the identification of aromatic compounds and particles in an atmospheric- pressure opposed-flow flame of ethylene. The fourth harmonic radiation (266 nm) of a Nd:YAG laser were used as excitation source. The energy of the laser pulse was kept constant at 0.8 mJ with a pulse duration of 8 ns [29].
Laser-induced fluorescence has also been employed to quantify the concentration of polycyclic aromatic hydrocarbons (PAH). The use of a Nd:YAG Laser in the fourth harmonic (266 nm) [32] and a nitrogen laser at 337 nm [31] has been reported.
Chapter 1 11
1.3.4. Fuels Laser techniques have been previously studied for the determination of fuels in different applications. Nd:YAG laser radiation at 266 nm and 355 nm was applied for the investigation of fuel-rich sooting hydrocarbon flames by Raman scattering. A laminar diffusion ethylene stagnation flow burner was employed [28]. In situ measurement of hydrocarbon fuel concentration near a spark plug in an engine cylinder using a 2948 cm-1 (3.392 m) infrared laser absorption method was reported. A HeNe laser was used to determine the hydrocarbon fuel concentration near the spark plug in a spark-ignition engine using iso- octane as fuel [64,65].
In the case of spark-ignition internal-combustion (IC) engines, fuel vapor and decomposition species were measured. Mid-IR laser light near 2941 cm-1 (3.4 m) was generated using DFG of a near-IR signal. Validation experiments were conducted for a single- component hydrocarbon fuel (2,2,4-Trimethyl-pentane, commonly known as iso-octane) and a gasoline blend in a heated static cell (300 1.3.5. Reaction progress Several reactions have been studied by laser diagnostics [68–73]. According to this state of the art the most common technique is TDLAS. Sensing representative molecules or functional groups gives information about the overall progress of the reaction. In the case of the reaction of chlorine (Cl) atoms with methyl iodide (CH3I) (298 K), the evaluation of the hydrogen chloride (HCl) yield and chemical reaction kinetics was performed by infrared TDLAS. A pulsed ArF excimer laser was operated at the UV wavelength 51813 cm-1 (0.193 m), with pulse duration 10 ns, repetition rate 1 Hz and energy 8–10 mJ/p [68]. The selective catalytic reduction was studied by monitoring the -1 ammonia (NH3) mole fraction using a fiber-coupled 4545 cm (2.2 m) distributed feedback (DFB) diode laser. A temporal resolution of 13 Hz and temperature-dependent precisions of NH3 mole fraction ranging from 50 to 70 ppm were achieved [69]. The reaction rate between atomic hydrogen and molecular oxygen was studied by tunable -1 diode laser absorption of H2O near 4000 cm (2.5 m). A DFB diode laser from Nanoplus GmbH was used [70]. Infrared diode laser spectroscopy was used to probe the kinetics of -1 the NCCO + O2 reaction at 1887 cm (5.3 m) [71]. Mid-IR laser absorption spectroscopy Chapter 1 12 by a nanosecond repetitively pulsed plasma was employed for the study of C2H4/Ar dissociation and C2H4/O2/Ar oxidation in a low temperature flow reactor [72]. Near-infrared diode laser absorption spectroscopy was applied to reactive systems and combustion control. A fast-response (100 kHz) TDL absorption sensor for H2O absorption at 7205 cm-1 (1.3 m) was developed [73]. The reaction progress of fluid catalytic cracking by laser techniques has not been studied before. 1.3.6. FCC monitoring A few studies document laser measurement of hydrocarbons (HC) and fuels [19,22,26,66] have been performed. Additionally, there are reviews about the application of tunable diode laser absorption spectroscopy (TDLAS) in the petrochemical industry [20,74–76]. On the other hand, GEISA [77], HITRAN [78], The Environmental Protection Agency [79] and Pacific Northwest National Laboratory (PNNL) [80] databases can be used for the determination of the IR region for hydrocarbons with low carbon number. Nevertheless, when the number of carbons is increased, the availability of an IR database for laser application is reduced. Klingbeil 2007 [22], developed a spectroscopic library for hydrocarbons sensing in harsh environments such as shock tubes, pulse detonation engines, and internal combustion engines. According to the author, this spectroscopic library provides the first high- temperature spectral information for many of the species studied ( In the case of high number of carbons, the determination of functional groups is an interesting option that gives information about the reaction progress that can be useful even for process optimization or for data validation in simulation. No effort has been made for the determination of the reaction progress by laser techniques in FCC. The use of the 2949.85 cm-1 (3.39 m) HeNe laser in the C-H bond region results interesting. As it can be observed while valuable the MIR has been limited and there is no demonstration as the author is aware of applications in the context both experimental laboratory scale reactors or large scale FCC units. 1.4. Fundamentals of Mid-Infrared (MIR) absorption When a molecule interacts with radiation, it can absorb, scatter or transmit the light. In the Mid-Infrared (MIR) the scattering is negligible [81]. The absorption spectra of a molecule in Chapter 1 13 the infrared region is a result of rovibrational transitions due to the change in vibrational and rotational quantum number when the photon is absorbed by the molecule [22,81]. Hydrocarbons presents C-H stretch vibrational transitions in the MIR region near 3.4 µm [26,82]. The absorption strength at a particular wavelength depends mainly on the population in the lower-state energy level [83]. According to Plank’s law, a molecule absorbs the laser when the energy of the light has the same difference as initial (E1) and excited (E2) energy levels [81]. 퐸2 − 퐸1 = ∆퐸 = ℎ푣 Equation 1.1 Where h is the Plank’s constant (h=6.63*10-34 J/s), 푣 is the frequency of the collimated light and ∆퐸 is the energy difference of the photon corresponding to the absorption process. 1.4.1. Beer-Lambert Law When a monochromatic light source is employed, the Beer-Lambert Law can be applied. Thus, the fractional radiation transmission, I/I0, of a monochromatic light source, with a wavenumber λ and an intensity I0, flowing through a gas cell of a length L, containing a uniformly distributed ci species, is described by Klingbeil [22], as follows: 퐼 훼휆,푖 = −푙푛 ( ) = 휎휆,푖(푇, 푃)푐푖퐿 Equation 1.2 퐼0 2 where σλ,i(T, P) represents the “i” Absorption Cross Section Coefficient in m per mole and α λ,i denotes the "i” species absorbance at a given wavenumber [84]. The Absorption Cross Section Coefficient is the probability of the molecule to absorb the light at a given frequency (푣) [81] and can be interpreted as the “effective area perpendicular to the direction of an incoming beam that is seen by that beam and causes an absorption process” [85]. The value for the Absorption Cross Section Coefficient can be obtained from Equation 2.1 as: 푐푖퐿 휎휆,푖(푇, 푃) = Equation 1.3 훼휆,푖 One can integrate the frequency-dependent Absorption Cross Section Coefficient (σλ,i) for any specific absorption range [22]. The integration of the σλ,i absorbance over a given region of the spectra is designated as the Integrated Absorption Band Intensity (Ψi). 휆 휓푖 = ∫ 휎휆,푖(푇, 푃)푑휆 Equation 1.4 휆0 Chapter 1 14 2 -1 -1 where Ψi is given in m mol cm . 1.5. Fundamentals of the 2949.85 cm-1 (3.39 µm) HeNe laser The HeNe laser has been used for adjusting and positioning but also, because of its excellent optical properties, in interferometers, sensors or spectrometers. The important wavelengths for the HeNe lasers are: 632.8 nm, 1.15 m and 3.39 m [85]. The red HeNe laser (632.8 nm) is known for is common use in laser pointers. The laser used in this study has a wavelength of 3.39 m (2949.85 cm-1) as is located in the infrared region, it results invisible to human eye. The HeNe laser consists of a long and narrow discharge tube (diameter ~ 2–8 mm and length 10–100 cm) which is filled with helium and neon with typical pressures of 1 torr and 0.1 torr [86]. It is an electrically pumped continuously emitting gas laser. Its basic principle is a gas discharge in a glass tube filled with a mixture of helium and neon under low pressure. The gas discharge is set up by a cathode and an anode placed at opposite sides of the glass tube. The laser mirrors are usually fixed to the end of the tubes [85]. Figure 1.2 presents the energy levels of Helium (He+) and Neon (Ne+). When an electrical discharge is passed through the gas, the electrons which are accelerated down the tube collide with helium and neon atoms and excite them to higher energy levels [86]. Electron collisions excites the neutral helium which transfers its energy by nearly resonant inelastic atomic collisions to the neon [85]. The excited helium atoms colliding with neon atoms in the ground state excite neon atoms to E4 and E6 levels which have similar energy than the F2 and F3 levels. The transition between E6 and E3 produces the 633 nm line. The lines 1.15 m and 3.39 m are produced by the transitions from E4 to E3 and E6 to E5 [86]. Figure 1.2. Energy levels of helium and neon. Taken from [86] Chapter 1 15 1.6. Conclusion The reported technical literature shows a limited application of the MIR for catalytic cracking studies, with no report describing in-situ MIR in an environment free of catalyst particles. It is in this area where the present PhD dissertation concentrates the research studies establishing with the help of a new Group Contribution Method both the progress of the catalytic cracking reactions as well key FCC lump product fraction compositions, such as light gases and gasoline. 2. Group Contribution Method for the Prediction of Adsorption Cross Section Coefficients in the Mid- Infrared (MIR) Absorption Range 3200-2800 cm-1 Abstract A new Group Contribution Method (GCM) based on molecular functionality is proposed for the prediction of hydrocarbon absorption spectra in the MIR region (3200−2800 cm−1). Dependence of the Absorption Cross Section Coefficient (σλ,i) and Integrated Absorption Band Intensities (Ψi) with C-H bonds number and type is discussed. Negligible dependence on temperature over the range of interest is mentioned. 15 parameters, representing the different types of C-H bonds, are proposed for the prediction of Absorption Cross Section −1 Coefficient (σλ,i) in the 3200−2800 cm range. The MIR spectra at 50 °C between 3200−2800 cm−1 for 21 paraffins, 20 olefins, 8 naphthenes and 16 aromatics from the spectral database of the Pacific Northwest National Laboratory (PNNL) were considered for the definition of the 15 parameters. 9 of the parameters were related to specific chemical bonds (single- bond, double-bond and aromatic rings), as well as 6 other coefficients were related to the CH3- and -CH2- groups at side branching positions. The GCM is applicable for paraffins, iso- paraffins, olefins, naphthenes and aromatics. The average relative absolute errors are 18.15% and 1.4% for the prediction of Absorption Cross Section Coefficient (σλ,i) (at HeNe wavenumber) and Integrated Absorption Band Intensities (Ψi), respectively. Keywords Group Contribution Method, mid-IR, C-H bond, Absorption Cross Section Coefficient (σλ,i), Integrated Absorption Band Intensity (Ψi) 2.1. Introduction Traditionally, analytical techniques, such as Gas Chromatography (GC), Gas Chromatography−Mass Spectrometry (GC-MS) and High-Performance Liquid Chromatography (HPLC), have been used to establish FCC process conditions and product quality. These techniques have relatively long response times (e.g. larger than 30 min) and cannot be used for in-situ measurements and process control. Rapid, non-intrusive sensing is desirable to monitor the concentrations of multiple species in the process streams. In the last decades, light hydrocarbon concentrations (C1−C7) have been determined using: a) Chapter 2 17 Linear Raman scattering [28], b) Spectral and time-resolved fluorescence and incandescence [29], c) Differential-absorption LIDAR [30], d) Laser-induced fluorescence (LIF) [31–33] and e) Tunable diode laser (TDL) [34]. Laser-based techniques, have been previously applied to reaction monitoring [68–70], given its high time resolution which is useful for process control. Different light sources can be used to obtain infrared light (IR) hydrocarbon spectra information from individual chemical species. Databases such as GEISA [77] and HITRAN [78] are however, limited to being used with species which have a low carbon number. The Environmental Protection Agency [79] and the Pacific Northwest National Laboratory (PNNL) [80] databases are frequently preferred to obtain quantitative MIR information [22,81,84,87,88]. However, MIR databases are rather limited, with many MIR spectra missing for relevant FCC related chemical species. Furthermore, infrared light (IR) can be applied to obtain gas phase species composition measurements, given that the IR spectrum can provide the chemical species fingerprints [9]. In particular, the IR spectra can be employed for the quick measurement of the product lumps and of the functional groups that constitute the gasoline [25]. In this respect, the technical literature reports attempts for the prediction of PIONA lumps (paraffins, isoparaffins, olefins, naphthenic and aromatics) [89,90,99,100,91–98] using signal deconvolution, partial least squares (PLS) and chemometrics to obtain the Near IR and the IR spectra. One should note however, that none of these previous studies attempted a fundamentally based approach using a Group Contribution Method (GCM), as the one of the present research. The GCM of this study is based on the cumulative influence of various molecular functions on the IR absorbance spectrum. The proposed GCM approach uses structural information from individual chemical species (e.g. functional groups). Thus, the calculation of Absorption Cross Section Coefficient (σλ,i) at a given wavenumber, involves adding the product of constants related to the structural groups and their frequency of occurrence. This methodology does not require experimentation or prior knowledge of the physical chemical properties of chemical species. To evaluate the proposed approach, a number of statistically based coefficients and equations (see [101–103]) are employed in the present study such as: a) the Coefficient of Determination (R2), b) the Root Mean Square Error of Prediction (RMSEP), c) the Average Absolute Error (휀), d) the Average Signed Error (휀푠푔푛), e) the Average Relative Absolute Error (휉), f) the Average Relative Signed Error (휉푠푔푛) and g) the Range Error Ratio (RER). As far as the author knowledge, there is not a similar contribution addressing this important issue in the technical literature. Chapter 2 18 The proposed GCM can be used as theoretical basis for an MIR “in-situ-free of particle” methodology proposed in the present doctoral thesis. This methodology as will be shown in the upcoming Chapters 4 and 5 can be employed for the assessment of reaction progress in FCC using model compounds (e.g. TIPB) as well as for the analysis of light gases and gasoline lump fractions in the context of an industrial FCC unit. 2.2. Statistic Parameters Table 2.1 reports the several statistical indicators (see [101–103]) using in the GCM. In this respect, the RER provides an assessment of the adequacy of the proposed Group Contribution Method as follows: a) a 푅퐸푅 ≥ 4, means acceptable for sample screening; b) a 푅퐸푅 ≥ 10, means acceptable for quality control; and c) a 푅퐸푅 ≥ 15 means good for quantification [103]. Additionally, three statistical moments (first, second and third moments) [104] of the resulting absorbance distributions were also calculated. Table 2.1. Statistics Employed for the Evaluation of the Group Contribution Method. Statistic Equation 1 휀 = ∑|휎 − 휎 | Average Absolute Error (휀) 푁 푃푟푒푑 퐸푥푝 푖 1 휀 휀푠푔푛 = ∑(휓푃푟푒푑 − 휓퐸푥푝) Average Signed Error or BIAS ( 푠푔푛) 푁 푖 1 휓푃푟푒푑 − 휓퐸푥푝 Average Relative Absolute Error (휉) 휉 = ∑ | | 푁 휓퐸푥푝 푖 1 휓푃푟푒푑 − 휓퐸푥푝 Average Relative Signed Error (휉푠푔푛) 휉푠푔푛 = ∑ ( ) 푁 휓퐸푥푝 푖 1 2 Root Mean Squared Error of Prediction (RMSEP) 푅푀푆퐸푃 = ∑(휓 − 휓 ) √푁 푃푟푒푑 퐸푥푝 푖 1 2 Standard Error of Prediction (SEP) 푆퐸푃 = ∑(휓 − 휓 − 퐵퐼퐴푆) √푁 푃푟푒푑 퐸푥푝 푖 푅푎푛𝑔푒 Range Error Ratio (RER) 푅퐸푅 = 푆퐸푃 Chapter 2 19 2.3. Theoretical Considerations. The Group Contribution Method Group Contribution Methods (GCM) were previously reported for the computation of different properties of pure hydrocarbons such as: a) physical and thermodynamic properties (e.g. formation entropy and enthalpy, heat capacity) [105–109], b) phase change points and critical properties [110–113], c) flash point [114–117], d) heat of combustion [118], e) aniline point temperature [119], f) autoignition temperature [120], g) cetane number [121] and h) octane number [122–124]. However, there is no report in the technical literature, of a GCM for MIR Absorption Cross Section Coefficient (σλ,i) predictions and its application to paraffins, iso-paraffins, olefins, naphthenes, aromatics and complex hydrocarbon mixtures. The 3200-2800 cm−1 MIR region is of great interest due to appearance of C−H stretching bands characteristic of hydrocarbon species. 2.3.1. Establishing a Group Contribution Method (GCM) Figure 2.1 reports the absorption spectra for n-paraffins at 50 °C, as a function of both wavenumber and the number of C−H bonds. This figure was developed using the spectral database from the Pacific Northwest National Laboratory (PNNL) [80]. One should note that Figure 2.1 reports the progressive increase of the MIR absorption band intensities with the number of C-H bonds in the 3200-2800 cm-1 region. In the case of catalytic cracking, the reactant hydrocarbons include a higher number of C-H bonds than the hydrocarbon cracking products. Thus, MIR absorbance is influenced by the extent of the catalytic cracking, and this is given its dependence on both the number of C-H bonds and the species molecular weight. Chapter 2 20 Figure 2.1. Changes of Absorbance of n-Paraffins in the MIR Range Showing the Effect of Increasing the Number of C−H Bonds. Spectra reported were obtained from the PNNL database [80] at 50C. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam. 2.3.2. C-H Bond Number Influence on the Absorbance of Hydrocarbons According to the Beer-Lambert Law (Section1.4.1), the absorbance of a compound is related with the path length of measurement (L), the concentration (ci) and the Absorption Cross Section Coefficient (σλ,i) of the compound. The Absorption Cross Section Coefficients (σλ,i, Equation 1.3) are affected by the number of chemical species C-H bonds in the 3200-2800 cm−1 MIR range. In this respect, Tomita et al., 2003 determined the Absorption Cross Section Coefficient for 10 normal paraffins and isooctanes, 5 olefins, 5 aromatics and 4 naphthenes using a 3.39 µm HeNe wavelength incident beam. Measurements were performed in the 300−600 K range at 100−2000 kPa. These authors reported that the Absorption Cross Section Coefficient was directly proportional to the number of C-H bonds. Differences in the Absorption Cross Section Coefficients were found to be dependent on the type of the C-H bonds [64]. Furthermore, Campbell et al. [84] studied 11 fatty acid methyl Chapter 2 21 esters (FAME) and proposed a linear relationship between the hydrocarbon species Absorption Cross Section Coefficient (σλ,i) at 3.39 µm and the logarithm of the number of C- H bonds. A contribution coefficient method was proposed by Mével et al. [87] for the prediction of the Absorption Cross Section Coefficient at 3.39 µm for alkanes, aromatics and substituted hydrocarbons. A correlated weight for every type of C-H bond in various hydrocarbon molecules was proposed. However, the scope of this method was limited, with no molecular structure being accounted for. Additionally, Klingbeil et al. [125] varied the conditions from 298 K and 673 K and from 500 to 2000 Torr, demonstrating that the Absorption Cross Section Coefficients of larger hydrocarbons at 3.39 µm had a negligible dependence on pressure and only a weak dependence on temperature. -1 Figure 2.2 reports the Absorption Cross Section Coefficient (σλ,i) for the 2949.85 cm (3.39 µm) HeNe beam of the present study. This Absorption Cross Section Coefficient (σλ,i) was obtained by considering the MIR spectra at 50C, for 21 paraffins, 20 olefins, 8 naphthenes and 16 aromatics using the PNNL database [80]. Additional information is provided in the Appendix of this thesis. Figure 2.2. Changes of the Absorption Cross Section Coefficient (σλ,i) with the Number of C-H Bonds Using a 2949.85 cm-1 (3.39 µm) HeNe Laser for Paraffinic, Olefinic, Naphthenic and Aromatic Species at 50 C. Chapter 2 22 It can be noticed in Figure 2.2, that there is a linear relationship between the Absorption Cross Section Coefficient (σλ,i) and the number of C−H bonds in the case of n-paraffins and n-olefins, as proposed by other authors [64,84]. This linear relationship shows R2=0.945 and R2=0.858 determination coefficient for olefins and paraffins respectively, with this linear dependence being less reliable for aromatics and naphthenes (R2=0.768 and R2=0.288, respectively). Furthermore, if one considers iso-paraffins, iso-olefins, aromatics or naphthenes this relationship is non-linear. Table 2.2 provides additional information assuming a linear dependence of Absorption Cross Section Coefficient (σλ,i = Ax + B), with x representing the number of C-H bonds. Various fitting parameters and related statistical indicators (see Table 2.1) are reported as well. In general, error parameters for paraffins and olefins are considered acceptable (e.g. RER>10), while for naphthenes and aromatics the assumption of a linear tendency is less apparent (e.g. RER<10). The approximation for the Absorption Cross Section Coefficient (σλ,i ) could be improved by using a parameter that indicates the presence of double bond, aromatic ring or branches. Table 2.2. Fitting Parameters and Statistical Indicators by Assuming a Linear Relationship between Absorption Cross Section Coefficient (σλ,i) and the Number of C-H Bonds for Different Chemical Lumps at a 3.39µm Wavelength Using a HeNe Laser. Type Paraffins Olefins Naphthenes Aromatics # Compounds 21 20 8 16 A 1.91 ± 0.0003 2.69 ± 0.0015 2.20 ± 0.0011 2.07 ± 0.0030 B 8.16 ± 0.0049 -11.55 ± 0.0173 12.60 ± 0.0151 -11.09 ± 0.0351 R2 0.858 0.945 0.288 0.768 Average Absolute Error 4.53 1.92 8.63 2.07 (휀) Average Signed Error or BIAS 0.17*10-15 -6.66*10-15 -10.66*10-15 -8.33*10-15 (sgn) Average Relative Absolute Error 0.1624 0.2577 0.2338 4.0114 (휉) Average Relative Signed Error 0.0595 -0.1264 0.0964 3.8826 (휉푠푔푛) RMSEP 5.66 2.27 12.46 2.50 RER 12.06 15.41 3.79 8.38 Thus, and instead of using the Absorption Cross Section Coefficient (σλ,i), Klingbeil [22], postulated that a linear dependence of the Integrated Absorption Band Intensity (Ψi) (as Chapter 2 23 defined in Equation 1.4) was more effective. This was demonstrated using 10 paraffins, 8 olefins and 8 aromatics. This linear trend was also observed by Campbell et al., (2014). As a result, the proposed Integrated Absorption Band Intensity (Ψi) approach appears to be a sound one. This is the case given that the Integrated Absorption Band Intensity (Ψi) helps in smoothing the effect of the positive and the negative Absorption Cross Section Coefficient deviations from the linear trend. Klingbeil et al. [126] studied 12 hydrocarbons and found that the MIR spectra between 25 °C and 500 °C was broadened at high temperatures, but that the Integrated Absorption Band Intensity was insensitive to temperature. According to Klingbeil [22] the Integrated Absorption Band Intensity is expected to be independent of temperature, for temperatures lower than 900 K. This provides a major advantage for a combined MIR and GCM in the CREC Riser Simulator annulus, without the needed calibration-recalibration under the 500 °C - 575 °C conditions of operation, as will be discussed later in Chapter 4. Figure 2.3 shows the applicability of a linear relationship between C-H bond number and 2 the Integrated Absorption Band Intensities (Ψi) for the paraffins (R =0.959), aromatics (R2=0.988), olefins (R2=0.977) and naphthenic (R2=0.954) lumps. Table 2.3 reports the parameters of the linear equation for each lump: (Ψi = ax + b), with x representing the number of C-H bonds. In general, error parameters are considered acceptable (e.g. RER>10). Chapter 2 24 −1 Figure 2.3. Integrated Absorption Band Intensities (Ψi) in the 3200-2800 cm Range for Paraffins, Olefins, Naphthenes and Aromatic Species. Table 2.3. Parameters and Statistics Assuming a Linear Tendency of Different Functional Groups between 3200-2800 cm-1. Type Paraffins Olefins Naphthenes Aromatics # Compounds 21 20 8 16 A 379.22±2.62*10-6 310.94±2.05*10-5 377.03±4.76*10-5 293.15±1.07*10-4 -988.01±4.84*10- B -817.84±2.38*10-4 -401.34±6.31*10-4 -1003.93±0.0012 5 R2 0.959 0.977 0.954 0.988 Average Absolute 383.90 115.21 252.60 60.77 Error (ε) Average Signed 1.59*10-12 -5.68*10-14 -1.65*10-12 -9.52*10-13 Error or BIAS (εsgn) Average Relative 0.0745 0.0504 0.0647 0.0262 Absolute Error (ξ) Average Relative -0.0111 0.0083 0.0094 0.0014 Signed Error (ξsgn) MSEP 571.59 165.05 297.86 70.88 RER 20.86 25.61 14.43 33.04 Chapter 2 25 Thus, the assumption of a linear dependence between the Integrated Absorption Band Intensities (Ψi, Equation 1.4) and the number of C-H bonds in the MIR range appears to be suitable and to agree with previously reported data [22,64,81,84,87]. It is important to stress that the influence of the C-H bond number and bond type on the IR absorbance has been studied using paraffins. Francis [127] postulated that individual hydrocarbon molecules could be considered as non-interacting blends of CH3, CH2 and CH structural groups. Integrated Absorption Band Intensities could then be represented as linear combinations of these structural groups. On the other hand, Jones [128] and Wexler [129] considered a linear relationship between the number of methylene groups and the IR intensity. These authors proposed the concept that the IR intensities for n-paraffins (from hexane to hexatriacontane) can be approximated by adding the IR properties from molecular structural units. Furthermore, Rericha et al. [130] and Pinkley et al. [131] studied liquid n-paraffins ( While these findings are valuable, they are limited to paraffins, iso-paraffins or olefin species. There is in fact, no study in the technical literature that we are aware of, that considers a Group Contribution Method (GCM) based on Absorption Cross Section Coefficients and functional groups and that can be applied to paraffins, iso-paraffins, olefins, naphthenes and aromatics. This valuable approach was developed in this doctoral thesis. As well and as it will be shown in Chapter 5, the GCM can be applied to a diversity of hydrocarbon fractions (paraffins, iso-paraffins, olefins, naphthenes and aromatics) in the 3200- 2800 cm−1 MIR range. 2.4. Proposed GCM Methodology for MIR Predictions. The Group Contribution Method (GCM) is the basis of a procedure used to calculate the -1 Absorption Cross Section Coefficient (σλ,i) at different wavenumbers in the 3200-2800 cm spectral range. This methodology accounts for the influence of various characteristic infrared absorption C−H stretching bonds at set wavenumbers. Thus, the Integrated Absorption Band Intensities (Ψi, Equation 1.4), can be determined if one has an “a priori” knowledge of the Absorption Cross Section Coefficient (σλ,i, Equation 1.3) at various wavenumbers [22]. Chapter 2 26 Figure 2.4 reports the IR absorption spectra in the MIR range for different bond types, present in different chemical hydrocarbons and shows the typical absorbance of the olefins, paraffins, aromatics and naphthenics in the MIR region. Individual compounds absorbance were obtained from the PNNL database [80] and from the characteristic wavenumbers as reported in a spectroscopy handbook [138]. Figure 2.4. Characteristic Infrared Absorption Spectra in the C−H MIR Stretching Region for Paraffins, Olefins, Naphthenes and Aromatics. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam. Following the GCM, the Absorption Cross Section Coefficient (σλ,i) at each wavenumber can be calculated for every “i” chemical species, as the sum of the contribution of each bond coefficient (j) multiplied by the nj number of bonds as follows: 휎휆,푖 = ∑ 훾푗푛푗 Equation 2.1 푗 with γj representing the coefficient related to the C-H bond type and nj denoting the total number of functional groups involved in a particular chemical species. Chapter 2 27 This approach can be applied to every one of the species contained in the FCC reaction mixture (paraffins, isoparaffins, olefins, naphthenes, aromatics) in the range of 3200 - 2800 cm−1 using the following equations: 휎휆,푚푖푥 = ∑ 푦푖휎휆,푖 Equation 2.2 푖 Equation 2.3 휓푚푖푥 = ∑ 푦푖휓푖 푖 where yi is the molar fraction of the species, σλ,i corresponds to the Absorption Cross Section Coefficient and Ψi stands for the Integrated Absorption Band Intensity of the hydrocarbon species lump. The GCM considers 15 different kind of C-H bonds. Table 2.4 reports the 9 indexes related to specific chemical bonds (single-bond, double-bond and aromatic rings), as well as 6 other coefficients related to the CH3- and -CH2- groups at side branching positions. Furthermore, a represents a double bond in an aromatic group. While an extension of this analysis for the determination of α-CH, α-C groups and β carbons, is desirable, the required database is not available at the present time. As a result, the Group Contribution Method as described in Equation 2.1, can be applied using the data of Table 2.4. Table 2.4. Type of C-H Bonds, as Related to the GCM Parameters. Single Bond Related Double Bond Related Aromatic Ring Related j Type j Type j Type 1 –CH3 7 = CH2 12 a = CH – 2 –CH2 – 8 = CH– 13 a = C < 3 >CH– 9 = C< 14 (훼) –CH3 4 >C< 10 (훼) –CH3 15 (훼) –CH2 – 5 (훼) –CH3 11 (훼) –CH2 – 6 (훼) –CH2 – In order to validate the GCM contribution coefficients, the MIR spectra (3200-2800 cm-1) for 65 hydrocarbons (21 paraffins, 20 olefins, 8 naphthenes and 16 aromatics) from the PNNL database were considered [80]. This led to the prediction of the Absorption Cross Section -1 Coefficients in the 3200-2800 cm wavenumber range with σλ,i involving 15 coefficients. To proceed with calculations, a linear least square constrained problem was considered using MatLab®, with the Absorption Cross Section Coefficients (σλ,i) always being positive. As well, a MIR spectrum for each type of C-H bond was accounted for, with the number of each C-H bond type involved in every molecule, included in the analysis. Chapter 2 28 2.5. Results and Discussion To compare the results of the Group Contribution Method (GCM) with the linear assumption, the Absorption Cross Section Coefficient (σλ,i) at the HeNe wavenumber (2949.85 cm-1 wavenumber or 3.39 µm wavelength) and the Integrated Absorption Band Intensities (Ψi) for the 65-chemical species were calculated. Figures 2.5 and 2.6 show the parity plots obtaining between the experimental data from PNNL [80] and the data calculated with GCM. Figure 2.5. Parity Plot of Experimental and Calculated Absorption Cross Section Coefficient (σλ,i). The solid line indicates 1:1 correlation. Note: all hydrocarbon species considered are in the C1-C16 range. Chapter 2 29 Figure 2.6. Parity Plot of Experimental and Calculated Integrated Absorption Band Intensities (Ψi). The solid line indicates 1:1 correlation. Note: all hydrocarbon species considered are in the C1-C16 range. A summary of the various statistical correlation indicators is provided in Table 2.5. When the GCM is used, the overall quality of the predictions for Absorption Cross Section 2 Coefficient (σλ,i) is good, with R >0.8, RER>10, 휉<20%, 휉푠푔푛<±5%, among others. In the case 2 of the Integrated Absorption Band Intensities (Ψi), R >0.95, RER>15, 휉<5%, 휉푠푔푛<±5% On this basis, one can conclude that when compared with the linear assumption, the GCM displays an improved determination coefficient (R2) for paraffins (R2=0.887), olefins (R2=0.95) and naphthenes (R2=0.334). Furthermore, for the prediction of the Integrated Absorption Band Intensity (Ψi), the GCM gives a similarly good R2 coefficient. This is the case for the entire 3200-2800 cm-1 IR range. As well, the value of Range Error Ratio (RER) for the Integrated Absorption Band Intensity (Ψi)prediction was larger than 15, which is an indicator of good quantification [103]. Better results are expected with the use of a larger database, specifically in the case of naphthenes and aromatics. However, that database is not available at the present time. Chapter 2 30 Table 2.5. Statistics for the Prediction of the Absorption Cross Section Coefficient (σλ,i) at a HeNe Wavelength and Integrated Absorption Band Intensities (Ψi). Absorption Cross Section Integrated Absorption Band Type Coefficient Intensities (σλ,i) (Ψi) R2 0.849 0.978 Average Absolute Error (휀) 4.13 182.02 Average Signed Error or BIAS (휀푠푔푛) -0.7835 38.29 Average Relative Absolute Error (휉) 0.1815 0.0490 Average Relative Signed Error (휉푠푔푛) -0.0237 0.0140 RMSEP 6.81 339.76 SEP 6.82 340.23 RER 11.52 39.50 Figure 2.7 provides a comparison between the experimental (from PNNL database) and predicted MIR spectra for a paraffin (e.g. n-heptane), an isoparaffin (e.g. iso-octane), a 1- olefin (e.g. 1-nonene), a branched olefin (e.g. cis-2-pentene), a naphthene (e.g. cycloheptene) and an aromatic (e.g. sec-butylbenzene). One can observe that there is a good correlation in all cases, between the predicted and the experimental MIR spectra, with only mild disparities being observed. Chapter 2 31 Figure 2.7. Comparison between Experimental and Calculated MIR Spectra for individual Chemical Species: (a) n-Heptane, (b) Isooctane, (c) 1-Nonene, (d) Cis-2-pentene, (e) Cycloheptene and (f) sec-Butylbenzene. (--) PNNL (-) Predicted. Thus, and to establish the agreement between the experimental and predicted spectra, statistical moments of the different order (statistical moments of order one, two and three) were considered. The first moment represents the mean, the second moment is the variance, the third standardized moment is the skewness. Figure 2.8 reports such a comparison. One can notice that the resulting R2 were 0.97, 0.90, 0.78 respectively. Which represents how close are the mean, variance and skewness of the experimental and predicted data for the 65 components. Furthermore, MIR spectra statistical moments of different order closely match these figures as well. Chapter 2 32 Figure 2.8. Statistical Moments of Different Order. Symbols and Legend reported in Figure 2.5. 2.6. Conclusions a) Mid-infrared (MIR) spectroscopy can be applied for the characterization of FCC reaction at wavelengths near 3.4 µm, which overlap with the strong C-H stretch vibrational transitions of hydrocarbons. b) There is a relationship between the absorption of hydrocarbons and the number of the different C-H bond types. c) Negligible dependence on temperature over the range of interest was previously reported. d) Available databases are limited to being used with species having low carbon number. The development of a tool able to extrapolate the Absorption Cross Sections for higher C-H bond number is imperative. Chapter 2 33 e) The GCM provides a through approach to calculate both Absorption Cross Section Coefficient (σλi) and the Integrated Absorption Band Intensity (Ψi) for individual species spectra in the MIR range between 3200-2800 cm-1. f) The GCM can be used for the evaluation of Absorption Cross Section Coefficient (σλi) and the Integrated Absorption Band Intensity (Ψi) for paraffins, iso-paraffins, aromatics and olefins in the MIR range between 3200-2800 cm-1. 3. Validation of the Group Contribution Method Abstract As proposed in the previous chapter, in the MIR, the Absorption Cross Section Coefficients (σλ,i) and Integrated Absorption Band Intensity (Ψi) can be related to the number of C-H bonds in the different chemical species. The proposed method is based on Group Contribution Method (GCM), which account for an additive contribution of molecular functionalities related with the C-H bonds present in hydrocarbons. This allows absorption spectra prediction in 3200−2800 cm−1 spectra region. This chapter validates the proposed GCM by means of: i) the data available in the literature for pure components and mixtures (gasolines) and ii) the experimental results from two different compounds: 1,3,5- triisopropylbenzene (1,3,5-TIPB) and 1,3-diisopropylbenzene (1,3-DIPB), which are of interest for the Fluid Catalytic Cracking reactions of model compounds. Keywords Group Contribution Method, Fluid Catalytic Cracking, Mid-IR, 1,3,5-Triisopropylbenzene 3.1. Introduction The FCC feedstock is composed basically of large hydrocarbons (C30-40+) that are converted into shorter molecules, giving a huge quantity of compounds over the reaction. Different compounds have been used as model feedstock for FCC: 1-hexene [139], hexadecane [140], cumene [141], 1,2,4-trimethylbenzene (1,2,4-TMB) [142], 1,3-diisopropylbenzene (1,3- DIPB) [141], 1,3,5-triisopropylbenzene (1,3,5-TIPB) [141–148], among others. For this research, the catalytic cracking of a model compound such as 1,3,5-TIPB was studied. This model compound has been used particularly in kinetic studies. The selection of 1,3,5-TIPB was mainly based in the fact that experimentation with this model compound illustrates and elucidates the overall reaction regime change (reaction- to diffusion-control) that is found in the catalytic cracking of gasoil under relevant operating conditions of FCC units [145]. According to Al-Khattaf [145], using 1,3,5-TIPB as model compound has as advantages: a) it can be considered a typical gasoil molecule , b) it has a 9.5 Å critical molecular diameter significantly larger than the 7.4 Å Y-zeolite opening, presenting the Chapter 3 36 diffusion constrains that can be observed in the FCC commercial plant, c ) the reaction path is relatively easy to follow since alkyl- benzenes crack following a quite known mechanism. Regarding the various steps involved in the catalytic cracking of 1,3,5-TIPB, a network of three prevailing in-series reactions can be considered (Figure 3.1): (a) 1,3,5-TIPB de- alkylation yields 1,3-DIPB and propylene, (b) 1,3-DIPB dealkylation gives cumene and propene, (c) cumene de-alkylation forms benzene and propene [149]. Due to selectivity of the FCC catalyst, neither cracking nor condensation of benzene is expected at this reaction conditions, and polymerization of propene is neglected [150]. Figure 3.1. Schematic description of the catalytic cracking of 1,3,5-TIPB. Adapted from [146] To understand the behaviour of the reaction in the MIR region, data related to the Absorption Cross Section Coefficient of the different compounds is required. Information about the MIR spectra and cross section coefficients between 3200-2800 cm-1 for propylene, benzene and cumene can be found in the literature: e.g. PNNL database [80]. However, in the case of 1,3-diisopropylbenzene (1,3-DIPB) and 1,3,5-triisopropylbenzene (1,3,5-TIPB), there is no information related with the cross-section coefficients. The Group Contribution Method can be used in this case. However, its validation is needed. To validate the proposed Group Contribution Method, two approaches were implemented : i) using literature data and ii) employing experimental results. With this end, literature data for pure compounds and hydrocarbons mixtures were considered. In the case of hydrocarbon mixtures, 21 gasolines previously measured by Klingbeil [22] were employed. Additionally, the Absorption Cross Section Coefficient (σλ,i) for 1,3-DIPB and 1,3,5-TIPB at 3.39 µm employing a HeNe laser was determined. The FTIR spectra between 3200-2800 cm- 1 for 1,3-DIPB and 1,3,5-TIPB was also measured. Chapter 3 37 3.2. Materials and Methods 3.2.1. Absorption Cross Section Measurements Figure 3.2 describes the MIR Gas Cell and the auxiliary equipment developed in the present project for the determination of the Absorption Cross Section Coefficient at 2949.85 cm-1 (3.39 µm) using a dual line HeNe laser. The MIR Gas Cell measurement cell was equipped with two sapphire windows (1.27 cm). A path length of 14.7 cm was employed. Nitrogen was used as carrier gas with a flow of 37 cm3/min (STP). For every experiment an adequate temperature was chosen, and this to prevent model compound condensation , avoiding interference during measurements. One should emphasize that the concentration-path length (ci x L) of the MIR Gas Cell was selected to be equivalent to the one in the annulus of the CREC Riser Simulator Unit. Figure 3.2. Experimental setup for cross section measurements at 2949.85 cm-1 (3.39 µm). Codes: Ch: Chopper, PD: PbSe Photodetector, DAQ: Data Acquisition. Regarding the MIR Gas Cell, it was equipped with an Impinger Gas Saturator. Nitrogen was used as a carrier gas. The Impinger Gas Saturator was placed inside a thermostatic bath, in order that various temperatures may be achieved in the MIR Gas Cell, in the range of the Chapter 3 38 molar fractions of interest. As well, the experimental setup was provided with a 2949.85 cm-1 (3.39 µm) HeNe laser, an optical chopper, a PbSe photodetector (1.5-4.8 µm, 10 kHz) and a signal recording system. The 1,3,5-TIPB is a valuable model compound for FCC studies given its balanced aromatic and iso-paraffinic functionalities. The 1,3,5-TIPB is a chemical species with a C15 carbon number, a 9.5 Å critical molecular diameter significantly larger than the 7.4 Å Y-zeolite opening. Thus, 1,3,5-TIPB is most valuable to elucidate kinetics and diffusion effects [145]. Information about the MIR spectra and cross section coefficients between 3200-2800 cm-1 for propylene, benzene and cumene can be found in the literature: e.g. PNNL database [80]. However, there is a lack of availability of MIR reported data for the following: a) 1,3- diisopropylbenzene, b) 1,3,5-triisopropylbenzene. Thus, the experimental plan of this thesis was designed to address this issue. 3.2.2. IR measurement The FTIR spectra in the region 3200-2800 cm-1 was determined for both 1,3-DIPB and 1,3,5- TIPB using a thermogravimetry instrument coupled to a Fourier transform infrared (TGA- FTIR) spectrometer. Fifty (50) mg ca. of liquid sample were employed for the measurement. Nitrogen was used as carrier gas with a flow of 100 ml/min. The temperature was incremented from room temperature to 100 °C with a ramp of 50 °C/min. This was the case until 100 °C was reached. This thermal level was kept for at least 20 min. 3.3. Results and discussion 3.3.1. Pure Component Predictions To provide a thorough validation of the GCM, a comparison was developed between the Absorption Cross Section Coefficients of pure components for IR HeNe with a 2949.85 cm-1 wavenumber (3.39 µm wavelength). Both GCM results and the data reported in the technical literature [80,87,125,151,152] were used. Table 3.1 provides this information, using as a basis the percentual relative difference (%Dif) between experimental and GCM predicted values. Table 3.1 reports the Absorption Cross Section Coefficient at 2949.85 cm-1 wavenumber (3.39 µm wavelength), available in the technical literature for different compounds using techniques such as HeNe or FTIR. Reported values were measure at temperatures between 298 K and 595 K. It is important to mention that Klingbeil et al. [125] demonstrated that the Absorption Cross Section Coefficients (σλ,i) of larger hydrocarbons at 3.39 µm display only a Chapter 3 39 very weak dependence on temperature. Thus and on this basis, it is possible to compare data from GCM at 50 °C (323 K) with the ones reported at technical literature. Table 3.1. Comparison of Absorption Cross Section Coefficient (σλ,i) with Data from the Technical Literature at 3.39 µm. Deviations are reported as percentual differences (% Dif). Absorption Temperature Cross- This Compound Gas Technique Ref. %Dif [K] Section Work (m2/mol) HeNe 3.39 n-Dodecane 323 Nitrogen 53.5 [71] 17.2% µm n-Dodecane 323 Nitrogen 57.5 9.0% FTIR [15] n-Dodecane 298 Nitrogen 53.4 62.7 17.4% n-Dodecane 446 Argon 66.8 6.1% n-Dodecane 523 Argon 68.09 FTIR [72] 7.9% n-Dodecane 595 Argon 66.8 6.1% n-Propyl 333 N/A 52.78 19.5% Cyclohexane n-Propyl 353 N/A 52.45 19.0% Cyclohexane n-Propyl HeNe 3.39 373 N/A 52.69 [19] 42.5 19.4% Cyclohexane µm n-Propyl 393 N/A 51.9 18.2% Cyclohexane n-Propyl 413 N/A 51.65 17.8% Cyclohexane Methyl- 356 Argon 49.5 28.9% Cyclohexane FTIR [72] Methyl- 360 Argon 49.8 35.2 29.4% Cyclohexane Methyl- HeNe 3.39 297 N/A 50.1 [73] 29.8% Cyclohexane µm Isocetane 354 Argon 71.9 FTIR [72] 94.7 31.7% n-Butyl- HeNe 3.39 297 N/A 56.6 [73] 47.8 15.5% Cyclohexane µm One can notice in Table 3.1 that these percentual differences are in the 6-31% range. Figure 3.3 reports the experimental MIR spectra of n-dodecane [22,80] and the absorption cross section predicted from the GCM. A vertical line highlights the comparison at the 2949.85 cm-1 wavenumber (3.39 µm wavelength) using IR HeNe where experimental measurements were developed as part of this study. Chapter 3 40 Figure 3.3. GCM Calculated Absorption Cross-Section Values and Reported Spectra for n-dodecane at 50 °C. The different lines represent: This work (-), Sharpe 2004 [80] results (--) and Klingbeil 2007 [22] (…) Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam wavenumber. One can notice that when GCM calculated spectra values are reported for n-dodecane using the Integrated Absorption Band Intensity (Ψi), deviations are significantly reduced. Thus, the Integrated Absorption Band Intensity (Ψi) helps in smoothing the positive and the negative Absorption Cross Section Coefficient deviations. For instance, Sharpe [80] reported a 8570 m2mol-1cm-1 value, Klingbeil [22] reported a 8340 m2mol-1cm-1 while the Group Contribution Method yields a 8783.3 m2mol-1cm-1. These experimental and GCM theoretical values differ only by 2.5% and 5.3%, respectively, with these values below 900 K being temperature independant [22]. 3.3.2. Predicting Gasoline MIR Spectra The present study attempts to demonstrate the applicability of the proposed Group Contribution Method for the prediction of MIR absorbance spectra in the range 3200-2800 cm-1. This is achieved by using Klingbeil (2007) [22] experimental absorption spectra data obtained at 50oC for 11 regular and 10 premium gasolines. In this respect, one should mention that Klingbeil (2007) [22] reported 21 fully characterized gasolines (13 ethanol free and 8 containing ethanol) in terms of paraffinic, isoparaffinic, Chapter 3 41 olefinic and aromatic lumps. Additionally, Klingbeil (2007) [22] proposed typical individual chemical species for each lump for both regular and premium gasolines. On this basis, the MIR spectra of each one of the gasolines under study is predicted here using Equation 3.1, and is compared to the experimental MIR spectrum data. The individual chemical species fractions for each lump (ethanol free) are taken from Klingbeil (2007) [22]. 휎휆,푔푎푠표푙푖푛푒 = ∑ 푦푙푢푚푝휎휆,푙푢푚푝 Equation 3.1 푖 Figure 3.4 reports the comparison between the predicted MIR spectra using the Group Contribution Method and the experimental MIR spectra for both regular and premium gasolines. This example illustrates the excellent ability of the GCM to establish the MIR spectra for hydrocarbon blends in the gasoline boiling point range. Chapter 3 42 (a) (b) Figure 3.4. Comparison between the Predicted and the Experimental MIR Spectra of : (a) Regular gasoline comprised of 20.6% n-paraffins, 32.8% isoparaffins, 18.6% olefins, 27.9% aromatics, 0% ethanol (b) Premium gasoline comprised of 12.3% n-paraffins, 75.3% iso-paraffins, 5.4% olefins, 7.1% aromatics, 0.0% ethanol. Chapter 3 43 Furthermore, and to demonstrate the significant value of the GCM, both R2 and Range Error Ratio (RER) coefficients for the MIR absorption spectra of the 21 gasolines were calculated, as reported in Figure 3.5. Figure 3.5 shows a very good correlation in all cases illustrated, with R2 and RER being larger than 0.9 and 15, respectively. Figure 3.5. R2 and RER Values for the 21 Gasoline Samples. Average errors are also reported in Figure 3.6. One can notice that with the only exception of the first gasoline of the series of 21, the Average Absolute Error (휀) is lower than 1.3 and the Average Relative Absolute Error (휉) of the gasoline MIR spectra remains below 25%. In the case of the signed errors, BIAS (휀푠푔푛) is between -1.5 and 0.5, while the Average Relative Signed Error (휉푠푔푛) is between -20% and 10%. Thus, on this basis and given all these positive indicators, one can conclude that the Group Contribution Method provides a good estimation of the MIR spectra between 3200-2800 cm-1 for gasolines of different types. Chapter 3 44 Figure 3.6. Average Errors for the Spectra of the 21 Gasoline Samples: a) Average Absolute Error (휀), b) Average Signed Error or BIAS (휀푠푔푛), c) Average Relative Absolute Error (휉) and d) Average Relative Signed Error (휉푠푔푛) Legend: (o) Regular gasoline (without alcohol), (*) Regular gasoline (with alcohol), (+) Premium gasoline(without alcohol), (x) Premium gasoline(with alcohol). 3.3.3. Experimental validation of GCM To establish the reaction progress using the MIR, it is necessary to obtain the Absorption Cross Section Coefficients for the pure components involved in the reaction. In the case of the 1,3,5-TIPB reaction compounds, Absorption Cross Section Coefficients for 1,3-DIPB and 1,3,5-TIPB, are not available in the literature. Figures 3.7 and 3.8 compare the predicted Absorption Cross Section Coefficients for 1,3-DIPB and 1,3,5-TIPB using the GCM and the TGA-FTIR in the 3200-2800 cm-1 region. One can confirm with these data, the closeness of the two MIR spectra. Chapter 3 45 Figure 3.7. Comparison of Absorption Cross Sections between experimental TGA-FTIR results and the GCM prediction for 1,3-DIPB. Note: the broken vertical line at 2949.85 cm-1 represent the HeNe laser beam wavenumber. Figure 3.8. Comparison of Absorption Cross Sections between the experimental TGA-FTIR results and the GCM prediction for 1,3,5-TIPB. Note: the broken vertical line at 2949.85 cm-1 represent the HeNe laser beam wavenumber. Chapter 3 46 Furthermore, Table 3.2 reports the Absorption Cross Section Coefficients at the specific 3.39 µm (2949.85 cm-1) HeNe wavelength using the MIR Gas Cell. One can observe the closeness of the GCM predicted values with the ones observed using the MIR Gas Cell. As a result, it is possible to confirm that the GCM can accurately predict the Absorption Cross Section Coefficient at a 2949.85 cm-1 HeNe wavenumber for cumene, DIPB and TIPB. As shown in Table 3.2, in the case of cumene, data reported in the technical literature [80] was also employed to test the Absorption Cross Section Coefficient measurement accuracy in the MIR Gas Cell experimental setup. Table 3.2. Summary of Absorption Cross Sections at 3.39 µm Experimental Theoretical Temperature In the MIR Gas Ref. Compound #Exp. using GCM (°C) Cell. (50 °C) (m2/mol) (m2/mol) 1 30 10.50 10.70 PNNL [80] Cumene 2 30 10.57 9.54 GCM 1 40 19.34 DIPB 2 50 19.22 19.08 GCM 3 50 18.74 1 70 28.50 TIPB 2 70 28.15 28.63 GCM 3 70 28.65 As a result, it is possible to confirm that the GCM is able to accurately predict the Absorption Cross Sections for cumene, DIPB and TIPB. 3.4. Conclusions a) The proposed GCM method has the ability of predicting the entire MIR spectra of n- paraffins, isoparaffins, olefins, naphthenes and aromatics in the 3200-2800 cm-1 region. The method was validated for the prediction of Absorption Cross Section Coefficients and Integrated Absorption Band Intensities of pure components. b) The GCM is considered for predicting the MIR spectra between 3200-2800 cm-1 of 21 gasolines. The obtained RER values above 15, show the possibility of using the Group Contribution Method for the prediction of MIR spectra of gasolines. Chapter 3 47 c) The experimental results for 1,3-DIPB and 1,3,5-TIPB were compared with the GCM prediction, showing its applicability for the prediction of absorption cross-sections at the 3.39 µm of HeNe laser. d) The proposed GCM is of significant value given it provides the theoretical framework for an in-situ-free of particles approach for measuring light gases and gasoline lumps in FCC process units, as it is reported in the upcoming Chapter 5 of this doctoral thesis 4. Monitoring the Catalytic Cracking of Model Compounds in the Mid Infrared (MIR) in the 3200- 2800 cm-1 Range Abstract Hydrocarbon species concentrations in the gas phase are of critical importance to elucidate catalytic cracking kinetics and riser/downer fluid dynamics. In this respect, Mid-Infrared (MIR) spectroscopy provides a singular approach to monitor chemical species conversions at various reaction times. The Group Contribution Method, proposed in Chapter 2 and validated in Chapter 3, can be applied in conjunction with a helium-neon (HeNe) laser, with a 2949.85 cm-1 wavenumber (3.39 µm wavelength). The technique is validated using both 1-hexene and 1,3,5-TIPB catalytic cracking data. The importance of the proposed method is shown in the context of using a CREC Riser Simulator, a mini-fluidized laboratory scale unit invented at CREC-UWO [153]. Hydrocarbon species are MIR monitored in the outer CREC Riser Simulator annulus. The data obtained can be used to extrapolate gas phase hydrocarbon conversions and fluid molar densities in risers and downers. This information can be also used to accurately predict fluid dynamics in FCC catalytic cracking units. Keywords Mid-infrared, catalytic cracking, reaction conversion, HeNe laser 4.1. Introduction A petroleum refinery is composed of several processing units which convert the raw crude oil into usable products such as gasoline, diesel, jet fuel, and heating oil [2]. Among the process technologies in the refinery, fluid catalytic cracking (FCC) is one of the most important and profitable technologies. Its installed capacity worldwide is 14 million barrels/day [4] as reported in [5,6]. The FCC feedstock is composed basically of large hydrocarbons (C30-40+) that are converted into shorter molecules, producing a huge quantity of compounds during the reaction. Different compounds have been used as model feedstocks for FCC. They are as follows: 1-hexene [139], hexadecane [140], cumene [141], 1,2,4-trimethylbenzene (1,2,4-TMB) [142], 1,3-diisopropylbenzene (1,3-DIPB) [141], 1,3,5- Chapter 4 49 triisopropylbenzene (1,3,5-TIPB) [141–148], among others. In this work and our previous published paper [10], the value of MIR Group Contribution Method in the context of FCC is established. Two relevant cases were considered: cracking of 1-hexene and cracking of 1,3,5-TIPB. FCC with different model compounds and vacuum gas oils (VGOs) has been studied previously using the CREC Riser Simulator invented by de Lasa [153]. The CREC Riser Simulator has shown that there are significant differences between hydrocarbon conversions defined based on the hydrocarbons contained in the entire reactor and hydrocarbon concentrations in the gas phase[154]. The reason for this difference was assigned to the internal mass transport limitations anticipated for hydrocarbon molecules in Y zeolite catalysts. The CREC Riser Simulator is a reactor that has been successfully employed for catalyst evaluation and kinetic studies in a number of catalytic reactions such as: a) oxidative dehydrogenations [155–159], b) biomass gasification [160,161], c) methylation [162], d) isomerization [163], among others. The CREC Riser Simulator operates in conjunction with a vacuum box heated to ∼250 °C. The contents of the vacuum box are typically evacuated at a pressure of ∼0.5 psi [164]. This essentially allows all hydrocarbons (reactant and products) to be desorbed from the catalyst. Reaction products are then, directed to a gas chromatograph for hydrocarbon conversion and product composition calculations. Furthermore, recent studies with the CREC Riser Simulator [154] have shown that one could fine-tune the gas sample recovered, by giving more importance to the gas phase components. This research has unveiled, the significant dominance of unconverted hydrocarbons in the gas phase. This is given that they are influenced by intra-crystallite diffusional limitations, making the detailed measurement of the gas phase hydrocarbons imperative. However, the approach proposed by Aponte et al. [154], is not free of pressure and concentration disturbances. Furthermore, this approach is intrinsically limited to a few evaluations while gas sampling is being effected. As a result, a new MIR methodology (3200- 2800 cm−1) is proposed to be implemented in the “free-of-particles” external annulus of the CREC Riser Simulator. MIR measurements in conjunction with the GCM provide continuous model compound conversions monitoring, in the external CREC Riser Simulator annulus. This new “in-situ free of particle” methodology permits one to unambiguously elucidate the influence of hydrocarbon species transport processes on FCC kinetics and set the stage for application of this method in the context of an FCC catalytic cracking plant. Chapter 4 50 4.2. Prediction of catalytic cracking conversion by MIR 4.2.1. 1-Hexene catalytic cracking Regarding the 1-hexene catalytic cracking, product yields, 1-hexene conversions and MIR spectra are reported in the open literature [80,165]. In this respect, 1-hexene was used by others as a model compound for analyzing catalytic cracking reactions [139,165]. In this work, the data from Nawaz et al.[165] at 500 °C for the catalytic cracking of 1-hexene were considered. The reaction took place using three different catalysts: meso-SAPO-34, 30%- SAPO-34 and 100%-SAPO-34. Researchers prepared a 100% SAPO-34 catalyst by the mixing of Al2O3:P2O5:SiO2:TEA:H2O = 1:1:0.5:2:100 (molar ratio) and calcinated at 600 °C after their synthesis steps. The 30% SAPO-34 catalyst was prepared with a mixture of pure SAPO-34 zeolite, kaolin, and silicon solution with a weight ratio of 30%, 40%, and 30%, respectively. The meso-SAPO-34 was prepared using kaolin (a combined source of aluminum and silicon) and phosphorus. The spectra available at PNNL database [80] together with the data previously reported [165] were employed in GCM calculations for 1-hexene catalytic cracking. The reaction products taken in consideration were, as suggested by Nawaz et al. (2009) [165]: dry gas, propane, butane, pentane, ethylene, propylene and butene. The dry-gas was represented as ethane. For instance, with a 100% SAPO-34 catalyst, for a typical 35% 1-hexene conversion, the following is obtained: 2.8% dry gas (ethane), 0.46% propane, 0.03% butane, 0.72% pentane, 2.91% ethylene, 43.29% propylene, 3.02% butene, 46.78% 1-hexene. Furthermore, considering the respective Absorption Cross Section Coefficients for various reaction products as reported in Figure 4.1, the GCM the yi i Weighted Absorption Cross Section Coefficients can be calculated as reported in Figure 4.2. Chapter 4 51 Figure 4.1. Absorption cross sections for the reaction products of 1-hexene catalytic cracking. Note: the broken vertical line at 2949.85 cm-1 represent the frequency of the HeNe laser beam Figure 4.2. yi i Weighted Absorption Cross sections for the reaction products of 1-Hexene catalytic cracking over 100%-SAPO-34 catalyst at a 35% conversion. Note: the broken vertical line at 2949.85 cm-1 represent the frequency of the HeNe laser beam Chapter 4 52 Thus, one can observe that while considering the yi i Weighted Absorption Cross Section Coefficients, both Equation 2.4 and 2.5, they can be simplified given the overriding influence of the unconverted 1-hexene and produced propylene as follows: 휓푚푖푥 ≈ 푦퐶6퐻12(휓퐶6퐻12 − 휓퐶3퐻6) + 휓퐶3퐻6 Equation 4.1 휎휆,푚푖푥 ≈ 푦퐶6퐻12(휎퐶6퐻12 − 휎퐶3퐻6) + 휎퐶3퐻6 Equation 4.2 Figures 4.3 and 4.4 report the anticipated Integrated Absorption Band Intensity and Absorption Cross Section Coefficient for 1-hexene mole fractions between 0 and 1. One can see that linearity for 1-hexene molar fractions larger than 0.6 is demonstrated. This can be expressed by Equations 4.1 and 4.2. Figure 4.3. Changes of Integrated Absorption Band Intensities and 1-Hexene Molar Fractions during the Catalytic Cracking of 1-Hexene. Catalysts: Meso-SAPO-34, 30%-SAPO-34 and 100%- SAPO-34 at 500 °C. Note: Doted lines represent the 10% error with respect to the linear trend. Chapter 4 53 Figure 4.4. Changes of Absorption Cross Section Coefficient and 1-Hexene Molar Fraction during the Catalytic Cracking of 1-Hexene. Catalysts : Meso-SAPO-34, 30%-SAPO-34 and 100%-SAPO-34 at 500 °C. Note: Doted lines represent the 10% error with respect to the linear trend Furthermore, one can also observe in Figure 4.5 and 4.6 that the Integrated Absorption Band Intensity and the Absorption Cross Section Coefficient can also be related to the 1-hexene conversion (푋퐶6퐻12) as shown in Equations 4.3 and 4.4. This is the case given that 1-hexene molar fractions can be expressed in terms of 1-hexene conversion (푋퐶6퐻12), and in terms of the molecular weights of 1-hexene (푀푊퐶6퐻12) and propylene (푀푊퐶3퐻6). (1 − 푋 ) 퐶6퐻12 푀푊퐶6퐻12 휓 ≈ (휓 − 휓 ) + 휓 휆,푚푖푥 (1 − 푋 ) 푋 퐶6퐻12 퐶3퐻6 퐶3퐻6 Equation 4.3 퐶6퐻12 + 퐶6퐻12 [ 푀푊퐶6퐻12 훼푀푊퐶3퐻6 ] (1 − 푋 ) 퐶6퐻12 푀푊퐶6퐻12 휎 ≈ (휎 − 휎 ) + 휎 Equation 4.4 휆,푚푖푥 (1 − 푋 ) 푋 퐶6퐻12 퐶3퐻6 퐶3퐻6 퐶6퐻12 + 퐶6퐻12 [ 푀푊퐶6퐻12 훼푀푊퐶3퐻6 ] Chapter 4 54 Equations 4.3 and 4.4 suggest that the proposed combined MIR and GCM can be used in an ample range of 1-hexene conversions up to 40%. For higher 1-hexene conversions however, this methodology is limited. This is the case, given the large potential errors in 1-hexene conversions values while being assessed by either the Absorption Cross Section Coefficient or the Integrated Absorption Band Intensity. Figure 4.5 Integrated Absorption Band Intensity as a Function of 1-Hexene Conversion during the Catalytic Cracking of 1-Hexene. Catalysts: Meso-SAPO-34, 30%-SAPO-34 and 100%-SAPO-34 at 500°C. Note: Doted lines represent the 10% error with respect to the linear trend Chapter 4 55 Figure 4.6. Absorption Cross Section Coefficient as a Function of 1-Hexene Conversion during the Catalytic Cracking of 1-Hexene. Catalyts: Meso-SAPO-34, 30%-SAPO-34 and 100%-SAPO-34 at 500°C. Note: Doted lines represent the 10% error with respect to the linear trend 4.2.2. 1,3,5-TIPB catalytic cracking As stated, a 1,3,5-triisopropylbenzene model compound has been used in kinetic studies in catalytic cracking. Catalytic cracking of 1,3,5-TIPB involves a network of three prevailing in- series reactions (Figure 4.7): (a) Dealkylation of 1,3,5-TIPB yielding 1,3-DIPB and propylene, (b) Dealkylation of 1,3-DIPB giving cumene and propene, (c) Dealkylation of cumene forming benzene and propene[149]. Due to selectivity of the FCC catalyst, neither the cracking nor the condensation of benzene is expected at these reaction conditions, and therefore, in this case, polymerization of propene can be neglected [150]. Chapter 4 56 Figure 4.7. Schematic Description of the Catalytic Cracking of 1,3,5-TIPB. Adapted from [146] Figure 4.8 reports the Absorption Cross Section Coefficients in the 3200-2800 cm-1 range for various 1,3,5-TIPB catalytic cracking products. It should be noted that, in the case of 1,3- DIPB and 1,3,5-TIPB, the Absorption Cross Section Coefficients were calculated by means of the GCM given they are not available in the open literature. Figure 4.8. Absorption Cross Section Coefficient of the Various Chemical Species Involved in the 1,3,5-TIPB Catalytic Cracking. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam. Chapter 4 57 The MIR and GCM application involve however, the Weighted Absorption Cross Section Coefficient (yi i ) as shown in Equation 2.5 and Figure 4.9 considers the use of a commercial FCC catalyst displaying a typical 36% 1,3,5-TIPB conversion. Under these conditions the following products are obtained: 28.88% 1,3,5-TIPB, 0.25% 1,3-DIPB, 2.02% cumene, 3.90% benzene, 64.96% propylene. One can notice that these weighted Absorption Cross Section Coefficients are mainly determined by both the 1,3,5-TIPB and the propylene. Figure 4.9. Weighted Absorption Cross Section Coefficient of the Reaction Products of 1,3,5-TIPB Catalytic Cracking having a 36% 1,3,5 TIPB Conversion. Note: The broken vertical line at 2949.85 cm-1 represents the frequency of the HeNe laser beam. As a result, the Integrated Absorption Band Intensities (Figure 4.10) and the Absorption Cross Section Coefficients, as reported in Figure 4.11, can be equated yielding Equations 4.5 and 4.6 as follows: 휓휆,푚푖푥 ≈ 푦퐶15퐻24(휓퐶15퐻24 − 휓퐶3퐻6) + 휓퐶3퐻6 Equation 4.5 휎휆,푚푖푥 ≈ 푦퐶15퐻24(휎퐶15퐻24 − 휎퐶3퐻6) + 휎퐶3퐻6 Equation 4.6 Chapter 4 58 Figure 4.10. Changes of the Integrated Absorption Band Intensity and 1,3,5-TIPB Molar Fractions and during 1,3,5-TIPB Catalytic Cracking. Note: Doted lines represent the 10% error with respect to the linear trend Figure 4.11. Changes of the Absorption Cross Section Coefficient and the 1,3,5-TIPB Molar Fractions during 1,3,5-TIPB Catalytic Cracking. Note: Doted lines represent the 10% error with respect to the linear trend Chapter 4 59 Equations 4.5 and 4.6 can be modified to relate both Integrated Absorption Band Intensity and the Absorption Cross Section to the conversion as indicated in Equations. 4.7 and 4.8. One can anticipate that Equations 4.7 and 4.8 will allow one to use a HeNe laser with a 2949.85 cm-1 wavenumber (3.39 µm wavelength) for the measurement of the reaction progress in the annulus of the CREC Riser Simulator. It is expected that these non-linear equations will provide reliable 1,3,5-TIPB conversion values up to 60%, which corresponds in fact to the maximum conversion from available experimental data. (1 − 푋 ) 퐶15퐻24 푀푀퐶15퐻24 휓 ≈ (휓 − 휓 ) + 휓 Equation 4.7 휆,푚푖푥 (1 − 푋 ) 푋 퐶15퐻24 퐶3퐻6 퐶3퐻6 퐶15퐻24 + 퐶15퐻24 [ 푀푀퐶15퐻24 훼푀푀퐶3퐻6 ] (1 − 푋 ) 퐶15퐻24 푀푀퐶15퐻24 휎 ≈ (휎 − 휎 ) + 휎 Equation 4.8 휆,푚푖푥 (1 − 푋 ) 푋 퐶15퐻24 퐶3퐻6 퐶3퐻6 퐶15퐻24 + 퐶15퐻24 [ 푀푀퐶15퐻24 훼푀푀퐶3퐻6 ] Chapter 4 60 Figure 4.12.Changes of Integrated Absorption Band Intensities with 1,3,5-TIPB Conversion. Note: Doted lines represent the 10% error with respect to the linear trend Figure 4.13. Changes of 1,3,5-TIPB Conversion with the Absorption Cross Section Coefficient. Note: Doted lines represent the 10% error with respect to the linear trend Chapter 4 61 4.3. Application of MIR and Fiber Optics in the CREC Riser Simulator 4.3.1. Selected conditions for catalytic cracking To establish the differences between gas phase based conversions and total reactant conversions, catalytic cracking runs using a commercial catalyst were performed [166]. All the catalytic cracking reactions were developed in the CREC mini-fluidized Riser Simulator [153]. The model compound (1,3,5-TIPB) amount was 0.2 g and the catalyst loading was 0.5 g. Therefore, the catalyst/oil ratio was determined to be 2.5 gcat/goil. Reaction temperature and the reaction time were set at 550 °C and 7 s respectively. Experiments followed two protocols: (a) Protocol 1: Setting the vacuum box at 1.5 psia, (b) Protocol 2: Setting the vacuum box at 14.7 psia. Reactant conversion and yields of gas products were estimated by GC–MS analysis (Agilent 6890 N GC, column HP-1 Methyl Siloxane; by Agilent Technologies). Protocol 1 allows evacuating and transferring to the vacuum box the almost complete hydrocarbon species content of the CREC Riser Simulator. These data can be used to establish accurately the overall TIPB conversion, using both GC and σmix. However, Protocol 2 considers the transfer of gas phase hydrocarbons evolving in the CREC Riser Simulator. This partial transfer of hydrocarbons is based on total pressure differences, limited to 2 s time periodic measurements. 4.3.2. Application in the CREC Riser Simulator As shown in the previous sections, the GCM method can be used for both 1-Hexene and 1,3,5-TIPB monitoring. However, the use of 1,3,5 TIPB is considered a much more suitable option for FCC kinetic modelling studies. In this regard, Table 4.1 reports that the 1,3,5 TIPB model compound involves a C15 molecule with well-balanced aromatic and isoparaffinic functionalities. As well, the 1,3,5 TIPB critical molecular diameter of 9.4 Å [143] is larger than the 7.4 Å of the Y zeolites. Furthermore and as shown by Equations 4.7 and 4.8, the GCM allows one to evaluate up to 60% conversions of 1,3,5 TIPB from the Absorption Cross Section Coefficient or the Integrated Absorption Band Intensity measurements. On the other hand, the 1-hexene displays a 5 Å critical molecular diameter, and is therefore, not diffusionally constrained in the Y-zeolites. Thus, the 1,3,5 TIPB model compound is considered more adequate than the 1-hexene to study the application of MIR in the CREC Riser Simulator, revealing intracrystallite diffusional constraints. Chapter 4 62 Table 4.1. Comparison of 1-Hexene and 1,3,5 TIPB. Chemical Species 1-Hexene 1,3,5-TIPB Bond Type Single / Double Aromatic / Single Carbon Number 6 15 C-H Bond Number 12 24 Critical Molecular Diameter(Å) 5 9.4 Conversion Threshold as Reported 40% 60% in Figures 4.6 and 4.13 (%) Experimental catalytic cracking runs can be developed in a mini-fluidized bed CREC Riser Simulator. Figure 4.14 reports a schematic diagram of the CREC Riser Simulator. The CREC Riser Simulator operates in conjunction with other accessories, such as a vacuum box, a gas chromatograph (GC), and a series of sampling valves, a timer, two pressure transducers and two temperature controllers. The vacuum box, which is a stainless-steel cylinder, is connected to the reactor by a four-port valve that enables the connection-isolation of the reactor and the vacuum box. A timer is connected to an actuator, which operates the four- port valve. This timer is used to set the reaction time for every experimental run. A run starts with a feed manual injection and is completed when the preset time is reached. At this point, the reactor contents are evacuated to the vacuum box through the four-port valve. The evacuation process is almost instantaneous. This is given the significant pressure difference between reactor and vacuum box. Once the evacuation of the reactor contents is complete, the hydrocarbon sample is transferred to a GC through a transfer line to be analyzed. GC analysis establishes the sample hydrocarbon composition and allows the determination of hydrocarbon conversion. Figure 4.14. Schematic Diagram of the CREC Riser Simulator Experimental Set-up [167] Chapter 4 63 Regarding the significance of transport processes in the FCC catalysts in the CREC Riser Simulator, they were confirmed in the present study limiting the vacuum pressure in the vacuum box prior to sampling to 14.7 psi instead of 1.5 psi. This led to a collected gas phase sample better representing the gas phase composition in the CREC Riser Simulator. Figure 4.15 reports the mass fractions of 1,3,5-TIPB and propylene, with the significant lower 1,3,5- TIPB conversions obtained using 13 psi in the vacuum box. Figure 4.15. Molar Fractions of 1,3,5 TIPB and Propylene in the CREC Riser Simulator for: i) Vacuum (1.5 psi) and ii) 14.7 psi. Note: Reported values represent averages for at least 3 repeats. Blue lines represent measurements at 14.7 psi and black lines represents measurements at 1.5 psi. Given these findings and the need of continuous and undisturbed pressure measurements, the use of MIR coupled with the GCM is proposed to measure gas phase conversions in the CREC Riser Simulator. This approach includes the significant major advantage that Absorption Cross Section Coefficients are not temperature dependent. As a result, MIR optical sensors does not require calibration or recalibration. Figure 4.16 describes the proposed modified CREC Riser Simulator setup with a HeNe laser selected as a light source. In this setup, the combined MIR and GCM yield Absorption Cross Chapter 4 64 Section Coefficients. As well with Eq. (10), 1,3,5 TIPB conversions can be assessed in the 0%-60% conversion range, with 60% conversion being the threshold for the MIR-GCM method application. Figure 4.16. CREC Riser Simulator Coupled with MIR Measurements. The enlarged view of the CREC Riser Simulator annulus highlights the measuring volume and MIR beam. As considered in Figure 4.16, optical fibbers can be strategically placed in the CREC Riser Simulator annulus. Using this approach, it can be shown that optical measurements can be implemented without particle interference in the MIR detection volume. In fact, catalyst particles always remain contained in the catalyst basket as described in Figure 4.16. Figure 4.17 reports calculated 1,3,5, TIPB conversions via emulated MIR-GCM measurements in the annular region of the CREC Riser Simulator and their comparison with experimentally observed 1,3,5, TIPB conversions. Two conditions are reported: a) 1,3,5, TIPB conversions at 1.5 psi (complete evacuation of the CREC Riser Simulator) and b) 1,3,5, TIPB conversions at 14.7 psi (very limited evacuation of the CREC Riser Simulator). One can observe that gas phase 1,3,5, TIPB conversions in these two cases, are very close similar. This validates and confirms the good reliability of the proposed MIR-GCM method. Chapter 4 65 Figure 4.17. MIR Predicted Results for the 1,3,5-TIPB Catalytic Cracking Conversion and its Comparison with Experimental Data from the CREC Riser Simulator. The solid line indicates 1:1 correlation. Note: Calculated conversion uses Eq(10) emulating mix HeNe laser measurements. Given that the calculated conversions in Figure 4.17 are established considering eq(2) and the further assumptions involved in Eq(10), the close fitting reported validates the GCM model for assessing TIPB conversion using mix. As a result, one can claim that the MIR-GCM provides a reliable methodology to assess catalytic cracking kinetics with restricted intraparticle diffusion transport in the Y zeolitic catalysts. One should note that gas phase concentration measurements using the reactor evacuation (Aponte et al, 2016) are inherently invasive. This method disturbs somewhat both total pressure and species concentrations. On the other hand, the new MIR-GCM is non-intrusive and free of pressure and concentration induced sampling changes. The MIR- GCM method does not require calibration and provides a host of valuable data for every run. On this basis, it is postulated that this MIR-GCM method with a measuring volume and MIR beam placed strategically in the CREC Riser Simulator annulus, could be advantageously applied for the evaluation of the kinetics of catalytic cracking hindered by intra-crystallite diffusional transport in FCC catalysts. The data obtained can also be employed to establish Chapter 4 66 gas phase hydrocarbon molar densities at various reaction times. This data is most valuable for accurate computational fluid dynamic calculations in risers and downers where one requires accurate estimation of gas phase fluid molar densities. 4.4. Conclusions a) The MIR-GCM provides in the specific case of the cracking of 1,3,5 TIPB, Weighted Absorption Cross Section Coefficients and Integrated Absorption Band Intensities which are governed by the 1,3,5 TIPB and the propylene molar fractions. This makes the 1,3,5 TIPB a model compound of choice for MIR “in-situ-free of particle” measurements. b) Emulated measurements in the annulus of a CREC Riser Simulator demonstrate both the viability and the applicability of the MIR method for the development of accurate catalytic cracking kinetic models. Thus, the validated MIR-GCM provides an excellent methodology, for the evaluation of the kinetics of catalytic cracking affected by intra-crystallite diffusional transport. This is a very relevant phenomenon still intriguing FCC kinetic modelling researchers. 5. Prediction of Light gases and Gasoline Lump Contents Abstract A new Group Contribution Method (GCM) based on molecular functionality was proposed for the prediction of hydrocarbon absorption spectra in the MIR region (3200−2800 cm−1) in our previous work [10]. The method was demonstrated to be valuable for the prediction of the Mid-Infrared (MIR) spectra between 3200-2800 cm-1 of 21 different gasolines previously studied by Klingbeil [22]. This method is now applied to the lumps molar fraction prediction for light gases and gasolines. A good determination coefficient is obtained in all cases (R2 > 0.9). The lumps considered for light gases were paraffins (including n-paraffins and iso-paraffins) and the lumps for gasolines were n-paraffins, iso-paraffins, olefins and aromatics , The results for the average relative absolute error were lower than 10% in all cases. A methodology for the light gases and gasoline lumps prediction at industry level employing the coupled MIR-GCM is proposed. Keywords Gasoline, Light Gases, Group Contribution Method, Mid-IR, Fluid Catalytic Cracking 5.1. Introduction The online control of both light gases (LG) and gasoline quality is important for the Fluid Catalytic Cracking (FCC) unit operation. In fact, establishing how the FCC process is performing with higher time resolution will help considerably on the establishment on industrial unit performance. Mid-Infrared (MIR) have been previously used for the measurement of hydrocarbons because it relates with the strong C-H stretch vibrational transitions [26]. This chapter reports the implementation of MIR measurements in the region 3200-2800 cm-1 together with the group contribution method (GCM) that was developed in our previous work [10]. The decomposition of the resulting MIR spectra allows close prediction of the paraffins, iIso-paraffins, olefins, naphthenes and aromatics (PIONA) lumps. Chapter 5 68 Emulated experiments are reported to illustrate the use of the MIR-GCM methodology for the prediction of the different lumps for gasolines. Comparison of Regular and Premium gasolines from Klingbeil [22] is first attempted. Results for regular gasolines are not satisfactory, probably because of the uncertain definition of the chemical species data as assumed by Klingbeil [22]. As a result a more accurate data base from Kraemer’s research developed at the CREC-UWO [168] for 48 runs in the CREC Riser Simulator is attempted. In this way, the proposed “in situ-MIR-free of particle” methodology is validated for the prediction of lumps for both, premium and regular gasolines with less than 10% average relative absolute error. 5.2. Application of the GCM for the Prediction of Lumps in Premium and Regular Gasolines The GCM method is proposed for the prediction of lumps in premium and regular gasolines. Hydrocarbons can be considered as lumped species consisting of n-paraffins, iso-paraffins, olefins, naphthenes and aromatics (PIONA). The definition of gasolines given by Klingbeil [22] does not consider the napthenes, so only n-paraffins, iso-paraffins, olefins and aromatics are analyzed in this first part. To validate this approach, the MIR experimental spectra results of the 10 premium and 11 regular gasolines reported by Klingbeil [22] were analyzed using the following regression method: a) the MIR experimental spectra and the average lump content as determined by Klingbeil [22] were used, b) the functional group fractions were calculated by minimizing the differences between the experimental spectra and the GCM predicted expectra. These constrained nonlinear multivariable minimizations were developed using MatLab®, with lump molar fractions being adjusted. Imposed constraints were as follows: a) all lump molar fractions were always positive, b) their summation was always equal to 1, c) Isoparaffinic and n-paraffinic molar fraction ratios complied with being in the 1.58 < xiso-paraffin / xn-paraffins < 1.6 range for regular gasolines and in the 6.10 < xiso-paraffin / xn-paraffins < 6.18 range for premium gasolines. Initial guesses for gasoline lumps were set as: xaromatics=30%, xolefins=20%, xn-paraffins=20%, xiso-parafins=30%. Figure 5.1 reports the parity plot displaying the predicted and experimental molar fraction values of paraffins, iso-paraffins, olefins and aromatics. Table 5.1 also reports the statistical indicators for the lumps composition. One can notice a very good determination coefficient in the case of premium gasolines (R2>0.9) with this being especially noticeable for n-paraffin, isoparaffin and aromatic predictions. Average relative absolute errors lower than 8% and RER values of approximately 9 were observed for n-paraffins and isoparaffins in premium gasolines. Regarding the standard deviations for the average absolute errors, Chapter 5 69 they were lower than 4% for molar fractions in the premium gasolines and lower than 8% for the molar fractions in regular gasolines. Figure 5.1. Parity Plot for the Prediction of the different Lumps in Gasoline. Blue color represents regular gasolines while black is used for premium gasolines. Note: (o) n-Paraffins, (*) Iso-Paraffins, (+) Olefins and (x) Aromatics Table 5.1. Statistical Indicators for the Prediction of Regular and Premium Gasolines Lumps. Type Regular Premium # Gasolines 11 10 Iso- All Paraffins Iso-Paraffins Oleffins Aromatics All Paraffins Oleffins Aromatics Paraffins R2 0.647 0.130 0.133 0.338 0.461 0.936 0.939 0.941 0.469 0.905 Average Absolute 0.0561 0.0305 0.0484 0.0699 0.0756 0.0403 0.0066 0.0405 0.0627 0.0514 Error Average Signed Error 0.0000 0.0214 0.0343 0.0131 -0.0689 -2.5*10-5 0.0041 0.0213 0.0233 -0.0487 or BIAS Average Relative 0.3572 0.1344 0.1341 0.8940 0.2664 0.3828 0.0729 0.0730 1.1603 0.2250 Absolute Error Average Relative -0.0095 0.1013 0.1017 0.0058 -0.2469 0.0879 0.0376 0.0295 0.4912 -0.2069 Signed Error RMSEP 0.0790 0.0396 0.0636 0.0891 0.1069 0.0531 0.0088 0.0532 0.0686 0.0605 SEP 0.0800 0.0350 0.0562 0.0924 0.0858 0.0538 0.0082 0.0514 0.0680 0.0378 RER 5.30 2.23 2.19 1.94 2.55 13.56 9.35 9.13 3.10 9.60 Chapter 5 70 Thus, based on the results obtained, one can postulate that the proposed regression method using GCM data provides an applicable tool for quantifying the quality of the premium gasoline on the basis paraffinic and iso-paraffins lump content. Satisfactory results are obtained for premium gasolines; however, the prediction for regular fraction is not acceptable. Section 5.3 discuss the difference between premium and regular gasolines and the reason of this disparity by means of the parametric sensitivity analysis. Section 5.4 proposes a methodology that can be adaptable to both, premium and regular gasolines by using previous results from chromatographic experiments to predict emulated experimental spectra of regular gasolines. 5.3. Parametric Sensitivity Analysis for Gasoline Lumps Parametric sensitivity has been used extensively for the evaluation of desirable operational conditions of chemical reactors [169]. This approach can be extended for the assessment of the effect of gasoline lumps molar fractions on a RMSEP objective function. In this case, the RMSEP function (as indicated in Table 2.1) relates the experimental and GCM predicted spectra when the value of molar fraction of the respective lump changes. Figure 5.2 reports dF/dxi or the derivative of the RMSEP objective function with respect to the “i” molar lump fraction at “xi” molar fraction lumps levels. The highest effect of dF/dxi in the MIR spectra between 3200-2800 cm-1 corresponds to paraffins and iso-paraffin lumps. The main difference between the regular and premium gasolines, as reported by Klingbeil [22] are the n-paraffins and iso-paraffins contents. When compared with premium gasolines, regular gasolines present a higher content of n-paraffins and a lower content of iso-paraffins. Chapter 5 71 Figure 5.2. Changes of dF/dxi for various premium gasoline molar fraction lumps: a) n-Paraffins, b) iso-paraffins, c) olefins, d) aromatics. In this sense, the unacceptable results obtaining for the prediction of paraffinic, iso- paraffinic, olefinic and aromatic lumps in regular gasolines could be related to a bad specification of the model compounds of each lump considered by Klingbeil [22]. A better definition of the components of each lump could improve the predictions for olefins content and include the naphthenic lump. In this case, the use of gas chromatography for the definition of the compounds to be included in each lump in the MIR-GCM would increase the applicability. In session 5.4, the product distribution from gas chromatography reported by Kraemer [168] is considered for the MIR-GCM application. 5.4. Applicability of MIR-GCM for the Prediction of Lumps in FCC The catalytic cracking of vacuum gas oil (VGO) is a process of great industrial interest. FCC kinetics are frequently based on lumps such as light gases (C1-C4, 17-21wt%), gasoline (C5- C12, 50-56 wt%), light cycle oil (LCO, C12-C20, ±15 wt%), gas oil (>C20, ±8 wt%) , and coke (4-6 wt%) [2,144]. Chapter 5 72 To be successful in the on-line measurement of FCC products by MIR laser techniques, a sampling system, designated as CREC MIR Gasoline Sampling System (CREC-MIRGSS) was designed. This system can be operated in conjunction with an FCC industrial scale unit in a “free of particle environment”. The CREC-MIRGSS sampling system as described in Figure 5.3, is constituted by two chambers: a) a first chamber operated at 70 kPa about 130 C allowing condensation of all hydrocarbons except those with carbon numbers smaller than C12 (gasoline and light gases). This first chamber is equipped with an upstream filter preventing catalyst particles to enter, b) a second chamber operated at close to atmospheric pressure (130 kPa) and 0 C keeping all light gas hydrocarbons in the gas phase. Thus, the proposed methodology considers MIR measurements in a “free of particle environment” taking in this manner full advantage of infrared measurements. Figure 5.3. CREC-MIR-GSS in a “free of particle” environment as considered to be implemented in an FCC industrial Unit. Codes: PD: Photodetector, DAQ: Data Acquisition The proposed CREC-MIR-GSS system allows, following several minutes of wait, to collect the MIR spectra for: a) combined light gas and gasoline fractions in Chamber 1, b) light gas fraction spectra in Chamber 2. Figure 5.3 reports the anticipated MIR absorption cross section coefficients in Chamber 1 for combined light gases and gasoline, and in Chamber 2 for light gases. Thus, the MIR absorption spectra difference yields the MIR absorption cross section coefficients for Chapter 5 73 gasoline range hydrocarbons C5-C12. One can notice that a HeNe laser alone does not discriminate enough between lumps and that a MIR spectrum covering the whole region between 3200-2800 cm-1 is required. Figure 5.4. Representation of Adsorption Cross Section Coefficient for Gasolines, Light Gases and the mixture Light Gases + Gasolines Main products for light gases (components 1 to 11) and gasoline fraction (components 12 to 41) during FCC experiments in the CREC Riser Simulator; as reported by gas chromatography analysis [168] are summarized in Table 5.2. Chapter 5 74 Table 5.2. Products distribution for Kraemer experiments [168] # Compound # Compound 1 Methane 25 n-Heptane 2 Ethylene 26 C7-olefins, naphthenes; C8 i-paraffins 3 Ethane 27 toluene 4 Propylene 28 C8 isoparaffins 5 Propane 29 2- and 3- methylheptane, C8 i-parafins 6 i-Butane 30 C7 and C8 naphthenes, C9 i-para 7 i-Butylene 31 n-Octane 8 1-Butene 32 C9 olefins; C8, C9 naph., C9 i-para 9 n-Butane 33 Ethylbenzene 10 t, 2-Butene 34 C9 naphthenes and i-paraffins 11 c, 2-Butene 35 p-Xylene and m-Xylene 12 3-Methyl-1-Butene 36 C9 i-paraffins and naphthenes 13 i-Pentane 37 o-Xylene 14 C5 Olefins 38 n-Nonane, C9 naphthenes 15 n-Pentane 39 C9 aromatics; C10 i-para and naphthenes 16 C5 Olefins, naphthenes, C6 olefins, i-para 40 n-Decane 17 2- and 3- methylpentane, C6-isoparafins 41 n-C10; C9, C10 arom; C10, C11 i-para., naph 18 C6 olefins 42 Light paraffins 19 n-Hexane 43 Light naphthenes 20 C6 olefins, naph.; C7 olefi., i-para, naph. 44 Light aromatics 21 Benzene 45 Heavy paraffins 22 C6 naphthene, C7 olef., i-para, naph. 46 Heavy naphthenes 23 2 and 3-methylhexane, C7 i-paraffins 47 Heavy aromatics 24 C7 olefins, naph, i-para.; c8 I-para. 48 Coke In order to proceed with calculations the product distribution of 48 experiments in the CREC Riser Simulator as reported by Kraemer [168] are considered. This is effected to validate the applicability of the in situ MIR-GCM in the prediction of lumps for FCC. With this end, the spectra of different individual compounds was obtained from: a) the PNNL database [80] when data was available (refer to Appendix A), b) the GCM when data was not available in the PNNL database. Furthermore, when chemical species were partially defined only, such as the case of C5 olefins, the information provided Table 5.3 was considered. Chapter 5 75 Table 5.3. Main products for Gasoline fraction C6 iso-Paraffins 2-Methylpentane (16), 3-Methylpentane (17) C7 iso-Paraffins 3-Methylhexane (18) C8 iso-Paraffins Isooctane (21) Paraffins C9 iso-Paraffins 2,2,5-trimetilhexano (GCM) - Iso C10 iso-Paraffins 2-metilnonano (GCM) C11 iso-Paraffins 2,3-dimetilnonane (GCM) 1-Pentene (25), 2-Methyl-1-butene (32), 3- Methyl-1-butene (33), 2-Methyl-2-butene (34), C5 Olefins cis-2-Pentene (35), trans-2-Pentene (36). 1-Hexene (26), 4-Methyl-1-pentene (37), 2- Methyl-2-pentene (38), cis-4-Methyl-2-pentene Olefins C6 Olefins (39) C7 Olefins n-Heptene (27) C9 Olefins 1-Nonene (29) C5 Naphthenes Cyclopentane (58), Cyclopentene (63) C6 Naphthenes Cyclohexane (59), Cyclohexene (64) C7 Naphthenes Cycloheptane (60), Cycloheptene (65) C8 Naphthenes Cyclooctane (61), Cyclooctene (GCM) C9 Naphthenes Cyclononane (GCM), Cyclononene (GCM) Naphthenes C10 Naphthenes Cyclodecane (62), Cyclodecene (GCM) C11 Naphthenes Cycloundecane (GCM), Cycloundecene (GCM) 2-Ethyltoluene (49), 3-Ethyltoluene (50), 1,3,5- Trimethylbenzene (51), 4-Ethyltoluene (52), C9 Aromatics Isocumene (53) 1,2,3,4-Tetramethylbenzene (54), 1,2,3,5- Aromatics C10 Aromatics Tetramethylbenzene (55), sec-Butylbenzene (56), tert-Butylbenzene (57) On this basis, prediction of light gases and gasoline lumps by using emulated experiments based on Kraemer [168] products distribution was attempted. To provide realistic conditions of MIR measurement, a 3% random error in the spectra of light gases (LG) and 6% random error in the case of gasolines was included. Chapter 5 76 5.4.1. Calculation Methodology Regarding data used in the calculations and regarding the main difference from the Kraemer [168] experiments and the Klingbeil [22] experiments, one can notice the significant difference in the product distribution. In the case of Klingbeil [22], only the total composition of the lumps was reported for each experiment, and model compounds were represented as lumps. In the case of Kraemer [168], a more detailed product distribution was given, as presented in Table 5.2. This extra information can be used for a better description of the emulated experimental spectra. The emulated experimental spectra for the first chamber (Figure 5.3) of each experiment was calculated by taking into account the compounds from 1 to 41 as explained in Table 5.2. Compounds 1 to 11 were considered in the case of the emulated spectra for light gases in chamber 2. The difference between the 2 spectra corresponds to the gasoline spectra as shown by equations 5.1 and 5.2. The molar fractions for the LG can be obtained from the pressure, volume and temperature conditions of the cambers 1 and 2. The molar fraction of gasolines is obtained by difference (푦퐺푎푠표푙푖푛푒 = 1 − 푦퐿퐺). 휎휆,total ≈ 푦퐿퐺휎휆,LG + 푦퐺푎푠표푙푖푛푒휎휆,Gasoline Equation 5.1 휎휆,total − 푦퐿퐺휎휆,LG 휎휆,Gasoline = Equation 5.2 1 − 푦퐿퐺 The selected lumps for light gases were paraffins (A), including n-paraffins and iso-paraffins, and olefins (B). Equation 5.3 represents the calculation for the Absorption Cross Section Coefficient. For gasoline, the lumps were stablished as: n-paraffins (A), iso-paraffins (B), olefins (C), naphthenes (D) or aromatics (E). Equation 5.4 is used in this case. In both cases, Equation 2.5 can still be used. 휎휆,LG = 푦퐴휎휆,A + 푦퐵휎휆,B Equation 5.3 휎휆,Gasoline = 푦퐴휎휆,A + 푦퐵휎휆,B + 푦퐶휎휆,C + 푦퐷휎휆,D + 푦퐸휎휆,E Equation 5.4 To validate this approach, the spectra decomposition was performed by using the following method: a) The emulated MIR experimental spectra for chamber 2 was done by using the components 1 to 11 from Table 5.2 and inducing a 5% error; b) calculated spectra for each lump (paraffins or olefins) in the case of LG was calculated according to the corresponding compound from 1 to 11; Chapter 5 77 c) the functional group fractions were calculated by minimizing the differences between the experimental spectra and the GCM predicted spectra; d) The emulated MIR experimental spectra for gasoline was done by using the components 12 to 41 from Table 5.2 and inducing a 3% error; e) calculated spectra for each lump (n-paraffins, iso-olefins, olefins, naphthenes or aromatics) in the case of gasolines was calculated according to the corresponding compound from Table 5.2; f) the functional group fractions were calculated by minimizing the differences between the experimental spectra and the GCM predicted spectra. These constrained nonlinear multivariable minimizations were developed using MatLab®, with lump molar fractions being adjusted. Imposed constraints were as follows: i) all lump molar fractions were always positive, and ii) their summation was always equal to 1. For the solution of this problem, a MIR spectra with points every 2 cm-1 was assumed, with a total of 201 data between 3200-2800 cm-1. The degrees of freedom for LG will be 199, with 2 parameters to be calculated and for gasolines will be 196, with 5 parameters to be calculated. This over-specification helps for the accuracy in the comparison of the emulated spectra and the calculated one. The constrains included in the function help in the convergence of the solution within physically logical results 5.4.2. Experimental error estimation As stated in Session 5.4.1, experimental errors were induced to the spectra. To calculate the error associated which each spectrum, the condition at the CREC MIR Gasoline Sampling System (CREC-MIRGSS) were simulated using Hysis®. Figure 5.5 presents the Hysis® simulation. The conditions at the first chamber were stablished as: Temperature: 130 °C and Pressure: 70 kPa. The second chamber was set at: Temperature: 0 °C and Pressure: 130 kPa. Given the higher contribution of n-paraffins species in the spectrums, the composition for each carbon number (From C1 to C30) were assumed as n-paraffins. Chapter 5 78 Figure 5.5. Hysis simulation of the CREC MIR Gasoline Sampling System (CREC-MIRGSS) conditions The output stream in Figure 5.5 contains some contamination of higher carbon number compounds, as expected in the industrial FCC process. With this information, the emulated experimental compositions were used for the calculation of the simulated spectra. The average relative absolute error for the first chamber, where the combined spectra of Gasolines and light gases is measured, is expected to be 2.92% while for the second chamber, where the Light Gases spectra is taken, the average relative absolute error is expected to be 4.34%. In that way, the selected experimental errors for session 5.4.1 were decided as 3% for the first chamber (Light gases + gasolines) and 5% for the second chamber (Light gases). 5.4.3. Results Figure 5.6 described results for paraffins (including n-paraffins and iso-paraffins) and olefins prediction in the case of LG. The parity plots displays the predicted and experimental molar fraction values of paraffins (including n-paraffins and iso-paraffins) and olefins. Table 5.4 reports the statistical indicators for the light gases and gasoline predictions. One can notice a very good determination coefficient (R2>0.9). Average relative absolute errors are 2% and 2.8%, respectively. In the case of RER, the values are higher than 15 showing that results are good for quantification. Low errors are also find in the case of average absolute error, BIAS Chapter 5 79 and average relative signed error. For the lumps molar fraction predictions, in the case of light gases, the average relative absolute error for paraffins (including n-paraffins and iso- paraffins) and olefins, is 1.99% and 2.18% respectively. Figure 5.6. Parity plot results for Light Gases, a) paraffins and b) olefins Table 5.4. Statistic parameters for Light Gases and Gasoline lumps predictions Type Light Gases Gasoline R2 0.9416 0.9957 Average Absolute Error (휀) 0.0103 0.0047 Average Signed Error or BIAS (휀푠푔푛) 2.54*10-17 8.56*10-18 Average Relative Absolute Error (휉) 0.0209 0.0284 Average Relative Signed Error (휉푠푔푛) -4.53*10-4 1.51*10-3 RMSEP 0.0122 0.0061 RER 15.3982 66.7504 Chapter 5 80 Figure 5.7 reports the parity plots for the gasoline including n-paraffins, isoparaffins, aromatics, naphthenics and olefins as grouped by the PIANO designation. One can observe an encouraging average relative absolute error of: 3.78% for n-paraffins, 2.40% for iso- paraffins, 3.75% for olefins, 3.38% for naphthenes and 0.92% for aromatics. Figure 5.7. Parity plot for gasoline, a) paraffins, b) olefins, c) naphthenes and d) aromatics 5.5. Conclusions a) The MIR-GCM as described here, is especially valuable for refineries, as a quick and effective assay for establishing the lumps content in Light Gases and Gasolines. b) A good prediction of the lumps contents was obtained with average relative absolute errors lower than 10% in every case. c) The RER value for light gases and gasoline was higher than 15, which shows that the proposed methodology is good for quantification. d) The CREC MIR Gasoline Sampling System (CREC-MIRGSS) in performing in a “free of particle environment” is useful for the quick determination of lumps present in Light Gases and Gasolines in an industrial FCC unit. Chapter 5 81 6. General conclusions and perspectives 6.1. General conclusions Fluid catalytic cracking (FCC) is a technology used to convert heavy gas oils with a high number of C-H bonds into shorter molecules with higher commercial value. The use of an online in-situ-free-of-particles laser technique for monitoring the reaction progress of catalytic cracking is of great interest. This approach overcomes the issues of low time resolution techniques used until today which were not able to elucidate the diffusional problems in FCC catalysts. In this sense, the MIR region is of practical importance due to appearance of C−H stretching bands of hydrocarbon species. A characteristic increase of the absorbance with the number of C-H bonds in the considered molecule is noticed in the region between 3200-2800 cm−1, with the type of C-H bond having a significant influence on the absorbance in this region. This work proposes a new Group Contribution Method (GCM) useful for the prediction of the absorption cross section coefficients of pure components and mixtures. This GCM coupled with MIR was applied successfully to predict of the conversions of 1- hexene and 1,3,5 TIPB. In this respect, the use of a HeNe laser working at its 2949.85 cm-1 wavenumber (3.39 µm wavelength) was proposed. The value of the coupled GCM_MIR methodology resides not only in its high-resolution time, but also in the possibility it carries to elucidate in situ, the gas phase conversions related to the catalytic cracking reaction. Additionally, a methodology for an in-situ-free-of-particle applications in the 3200-2800 cm−1 MIR is proposed in this, to predict the molar fractions of light gases and gasoline lumps in commercial FCC units. To accomplish this in the present study, the utilization of the coupled methodology GCM- MIR, used to follow the reaction progress of catalytic cracking reactions and to understand the FCC lumps molar fractions for LG and gasoline, was investigated. Given all the above, the following are the conclusions of this doctoral thesis research: A new Group Contribution Method (GCM) useful to calculate both the Absorption Cross Section and the Integrated Band Intensity for individual species or Chapter 6 84 hydrocarbon mixtures spectra in the MIR range between 3200-2800 cm-1 was proposed. The proposed GCM method has the ability to predict the entire MIR spectra of n- paraffins, iso-paraffins, olefins, naphthenes and aromatics in the 3200-2800 cm-1 region. The method was validated for the prediction of absorption cross-sections coefficients and integrated band intensities of pure components and gasolines. The coupled methodology designated as GCM-MIR provides, in the specific cases of the catalytic cracking of 1-hexene and 1,3,5 TIPB, a prediction of the reaction conversion by means of the measurement of the absorption cross sections or the integrated band intensities. The use of a 2949.85 cm-1 wavenumber (3.39 µm wavelength) is proposed. The implementation of the proposed CREC MIR Gasoline Sampling System (CREC- MIRGSS) in FCC is useful for the prediction of LG and gasolines lumps. 6.2. Recommendations for Future work The following future perspectives of this doctoral thesis can be mentioned: Include additional compounds to the database used in the GCM would allow the consideration of more kind of α and β carbons and increase the applicability of the method. Given the established value of the coupled GCM-MIR methodology to determine catalytic cracking reaction progress with high time resolution, in the CREC Riser Simulator, its further experimental application is strongly recommended. Given the demonstrated value of the proposed GCM applied in conjunction with MIR in free of particle environments, it is suggested to consider this method for other chemical reactions (e.g pyrolysis reactors) in riser and downers where changes in C- H number can be associated with the reaction progress. Given the valuable results obtained with a laser system able to measure the MIR spectra in the 3200-2800 cm-1 range, its further development for an industrial setting as the one of an industrial FCC plant is strongly recommended. 7. Contributions related to this thesis The following contributions were prepared during the Ph.D. research: 7.1. Papers . Lopez-Zamora, S., Alkhlel, A., & de Lasa, H. (2018). Monitoring the progress of catalytic cracking for model compounds in the mid-infrared (MIR) 3200–2800 cm−1 range. Chemical Engineering Science, 192, 788–802. https://doi.org/10.1016/J.CES.2018.08.021 Lopez-Zamora, S., & de Lasa, H. Application of the Mid Infrared (MIR) in the 3200- 2800 cm-1 Range for the Monitoring of Gasolines in Fluid Catalytic Cracking. To be submitted to International Journal of Chemical Reaction Engineering, November 2018. 7.2. Conferences . Sandra Milena López Zamora, Hugo de Lasa, Alejandro Molina. Characterization of the progress of the fluid catalytic reaction with a 3.39 µm HeNe laser. 29º Congreso Colombiano de Ingeniería Química y Profesiones afines. Manizales, Colombia 2017. . Sandra Milena López Zamora, Hugo de Lasa, Alejandro Molina. A Group Contribution Method for the Prediction of the Mid Infrared (MIR) Absorption Spectra of Species Involved in Fluid Catalytic Cracking (FCC). 2017 AIChE Annual Meeting. Minneapolis, USA 2017 . Sandra Milena López-Zamora, Hugo de Lasa, Alejandro Molina. In-situ characterization of chemical species function groups in FCC units. Future Fuels Workshop. Thuwal, Saudi Arabia 2016. References [1] C.H. Bartholomew, R.J. Farrauto, Fundamentals of industrial catalytic processes, 2nd ed., John Wiley and Sons, Inc, New Jersey, 2006. doi:10.1016/S0015-6264(82)80243- 5. [2] R. Sadeghbeigi, Fluid Catalytic Cracking Handbook, Butterworth-Heinemann, 2012. [3] Á. Ibarra, A. Veloso, J. Bilbao, J.M. Arandes, P. Castaño, Dual coke deactivation pathways during the catalytic cracking of raw bio-oil and vacuum gasoil in FCC conditions, Appl. Catal. B Environ. (2015). doi:10.1016/j.apcatb.2015.09.044. [4] R.M. Lyn, Worldwide refining, Oil Gas J. 108 (2010) 52. [5] A. Corma, F. V. Melo, L. Sauvanaud, F. Ortega, Light cracked naphtha processing: Controlling chemistry for maximum propylene production, Catal. Today. 107–108 (2005) 699–706. doi:10.1016/j.cattod.2005.07.109. [6] R. Pujro, M. Falco, U. Sedran, Formation of aromatics in heavy gasoline and Light LCO ends in FCC, Appl. Catal. A Gen. 489 (2015) 123–130. doi:10.1016/j.nbt.2011.03.018. [7] A. Corma, L. Sauvanaud, FCC testing at bench scale: New units, new processes, new feeds, Catal. Today. 218–219 (2013) 107–114. doi:10.1016/j.cattod.2013.03.038. [8] P. O’Connor, Chapter 15 Catalytic cracking: The Future of an Evolving Process, Stud. Surf. Sci. Catal. 166 (2007) 227–251. doi:10.1016/S0167-2991(07)80198-4. [9] B. Stuart, Infrared Spectroscopy: Fundamentals and applications, John Wiley and Sons, Ltd, 2004. [10] S. Lopez-Zamora, A. Alkhlelb, H. de Lasa, Monitoring the progress of catalytic cracking for model compounds in the mid-infrared (MIR) 3200–2800 cm−1 range, Chem. Eng. Sci. 192 (2018) 788–802. doi:https://doi.org/10.1016/j.ces.2018.08.021. [11] S. Lopez-Zamora, H. de Lasa, Application of the Mid Infrared (MIR) in the 3200-2800 cm-1 Range for the Monitoring of Gasolines in Fluid Catalytic Cracking. (2019). To be submited. [12] G. Jiménez-García, R. Aguilar-López, R. Maya-Yescas, The fluidized-bed catalytic cracking unit building its future environment, Fuel. 90 (2011) 3531–3541. doi:10.1016/j.fuel.2011.03.045. [13] J.G. Speight, B. Ozum, Petroleum Refining Processes, Marcel Dekker Inc., New York, 2002. [14] X. Li, C. Li, J. Zhang, C. Yang, H. Shan, Effects of Temperature and Catalyst to Oil Weight Ratio on the Catalytic Conversion of Heavy Oil to Propylene Using ZSM-5 and USY Catalysts, J. Nat. Gas Chem. 16 (2007) 92–99. doi:10.1016/S1003-9953(07)60033-4. [15] A. Sanchez, S. Ramirez, W. Silva, J.F. Espinal, Prediction of 1,3,5-triisopropylbenzene References 88 cracking pattern through thermodynamic evaluation of products and protonation intermediates, Mol. Catal. 466 (2019) 13–18. doi:10.1016/j.mcat.2018.12.019. [16] J. Verstraete, V. Coupard, C. Thomazeau, P. Etienne, Study of direct and indirect naphtha recycling to a resid FCC unit for maximum propylene production, Catal. Today. 106 (2005) 62–71. doi:10.1016/j.cattod.2005.07.183. [17] G. Wang, C. Xu, J. Gao, Study of cracking FCC naphtha in a secondary riser of the FCC unit for maximum propylene production, Fuel Process. Technol. 89 (2008) 864–873. doi:10.1016/j.fuproc.2008.02.007. [18] R. Siripuram, Worldwide demand for propylene to rise to 130 million tonnes by 2023, says IHS, Chem. Technol. (2014). https://www.chemicals- technology.com/news/newsworldwide-demand-for-propylene-to-rise-to-130- million-tonnes-by-2023-says-ihs-4356137/. [19] D. Bamford, D. Cook, S. Sharpe, A. Van Pelt, Widely-Tunable Rapid-Scanning Mid-IR Laser Spectrometer for Industrial Gas Process Stream Analysis, in: Laser Appl. to Chem. Secur. Environ. Anal., 2006: p. 4. [20] M. Lackner, Tunable Diode Laser Absorption Spectroscopy (TDLAS) in the Process Industries – a Review, Rev. Chem. Eng. 23 (2007) 65–147. doi:10.1515/REVCE.2007.23.2.65. [21] P. Werle, A review of recent advances in semiconductor laser based gas monitors, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 54 (1998) 197–236. doi:10.1016/S1386-1425(97)00227-8. [22] A.E. Klingbeil, MID-IR Laser absorption diagnostics for hydrocarbon vapor sensing in harsh environments, Standford University, 2007. [23] J. Coates, Interpretation of Infrared Spectra, A Practical Approach, in: R.A. Meyers (Ed.), Encycl. Anal. Chem., John Wiley and Sons, Ltd, Chichester, 2000: pp. 10815– 10837. doi:10.1002/9780470027318. [24] L.G. Weyer, S.C. Lo, Spectra–structure correlations in the Near-infrared, in: J.M. Chalmers, P.R. Griffiths (Eds.), Handb. Vib. Spectrosc., Wiley, Chichester, UK, 2002: pp. 1817–1837. [25] M.P. Gómez-Carracedo, J.M. Andrade-Garda, D. Prada-Rodríguez, Differentiation of weathered oils using infrared indexes and self-organizing maps, Fuel. 158 (2015) 57– 65. doi:10.1016/j.fuel.2015.04.064. [26] J.M. Porter, Laser Based Diagnostics for Hydrocarbon Fuel in the Liquid and Vapor Phases, Stanford University, 2009. [27] R.S.M. Chrystie, E.F. Nasir, A. Farooq, Propene concentration sensing for combustion gases using quantum-cascade laser absorption near 11μm, Appl. Phys. B. 120 (2015) 317–327. doi:10.1007/s00340-015-6139-4. [28] J. Egermann, T. Seeger, A. Leipertz, Application of 266-nm and 355-nm Nd:YAG laser radiation for the investigation of fuel-rich sooting hydrocarbon flames by raman scattering., Appl. Opt. 43 (2004) 5564–5574. doi:10.1364/AO.43.005564. [29] M. Sirignano, A. Collina, M. Commodo, P. Minutolo, A. D’Anna, Detection of aromatic hydrocarbons and incipient particles in an opposed-flow flame of ethylene by spectral References 89 and time-resolved laser induced emission spectroscopy, Combust. Flame. 159 (2012) 1663–1669. doi:10.1016/j.combustflame.2011.11.005. [30] M.J.T. Milton, P.T. Woods, B.W. Jolliffe, N.R.W. Swann, T.J. McIlveen, Measurements of toluene and other aromatic hydrocarbons by differential-absorption LIDAR in the near-ultraviolet, Appl. Phys. B Photophysics Laser Chem. 55 (1992) 41–45. doi:10.1007/BF00348611. [31] Z. Chi, B.M. Cullum, D.L. Stokes, J. Mobley, G.H. Miller, M.R. Hajaligol, T. Vo-Dinh, Laser-induced fluorescence studies of polycyclic aromatic hydrocarbons (PAH) vapors at high temperatures, Spectrochim. Acta - Part A Mol. Biomol. Spectrosc. 57 (2001) 1377–1384. doi:10.1016/S1386-1425(00)00479-0. [32] R. Sun, N. Zobel, Y. Neubauer, C. Cardenas Chavez, F. Behrendt, Analysis of gas-phase polycyclic aromatic hydrocarbon mixtures by laser-induced fluorescence, Opt. Lasers Eng. 48 (2010) 1231–1237. doi:10.1016/j.optlaseng.2010.06.009. [33] A. Lantz, Application of Laser Techniques in Combustion Environments of Relevance for Gas Turbine Studies, Lund University, 2012. [34] H.R. Melroy, E.M. Adkins, M.J. Pause, J.H. Miller, Species measurements in a nitrogen- diluted, ethylene air diffusion flame using direct sampling mass spectrometry and tunable diode laser absorption spectroscopy, Proc. Combust. Inst. 35 (2015) 3749– 3755. doi:10.1016/j.proci.2014.08.022. [35] M. Kroll, J.A. McClintock, O. Ollinger, Measurement of gaseous oxygen using diode laser spectroscopy, Appl. Phys. Lett. 51 (1987) 1465. doi:10.1063/1.98999. [36] H.E. Schlosser, J. Wolfrum, V. Ebert, B.A. Williams, R.S. Sheinson, J.W. Fleming, In situ determination of molecular oxygen concentrations in full-scale fire-suppression tests using tunable diode laser absorption spectroscopy, Proc. Combust. Inst. 29 (2002) 353–360. doi:10.1016/S1540-7489(02)80047-5. [37] Y. Fujii, S. Tsushima, S. Hirai, In-situ Measurement of Water Vapor and Oxygen Concentration in a PEMFC Channel by Tunable Diode Laser Absorption Spectroscopy Techniques, in: ECS Trans., ECS, 2007: pp. 451–459. doi:10.1149/1.2780959. [38] S. Neethu, R. Verma, S.S. Kamble, J.K. Radhakrishnan, P.P. Krishnapur, V.C. Padaki, Validation of wavelength modulation spectroscopy techniques for oxygen concentration measurement, Sensors Actuators B Chem. 192 (2014) 70–76. doi:10.1016/j.snb.2013.10.070. [39] R. Verma, S. Neethu, S.S. Kamble, J.K. Radhakrishnan, P.P. Krishnapur, V.C. Padki, Tunable Diode Laser Absorption Spectroscopy based Oxygen Sensor, in: 2012 Sixth Int. Conf. Sens. Technol., IEEE, 2012: pp. 130–135. doi:10.1109/ICSensT.2012.6461655. [40] J. Yan, C. Zhai, X. Wang, W. Huang, The Research of Oxygen Measurement by TDLAS Based on Levenberg-Marquardt Nonlinear Fitting, Spectrosc. Spectr. Anal. 35 (2015) 1497–1500. http://www.gpxygpfx.com/qikan/epaper/zhaiyao.asp?bsid=22162 (accessed September 20, 2015). [41] G. Gao, T. Cai, B. Hu, T. Jia, Measurement of oxygen concentration using multimode diode laser absorption spectroscopy, Guang Pu Xue Yu Guang Pu Fen Xi. 35 (2015) References 90 34–37. http://europepmc.org/abstract/med/25993815 (accessed September 20, 2015). [42] T.A. Crisp, B.M. Lerner, E.J. Williams, P.K. Quinn, T.S. Bates, T.H. Bertram, Observations of gas phase hydrochloric acid in the polluted marine boundary layer, J. Geophys. Res. Atmos. 119 (2014) 6897–6915. doi:10.1002/2013JD020992. [43] C. Dene, J.T. Pisano, T.D. Durbin, K. Bumiller, K. Crabbe, L.J. Muzio, Laboratory testing of a continuous emissions monitor for hydrochloric acid, J. Air Waste Manage. Assoc. 64 (2014) 670–678. doi:10.1080/10962247.2014.880755. [44] J. Hodgkinson, R.D. Pride, Methane-specific gas detectors: the effect of natural gas composition, Meas. Sci. Technol. 21 (2010) 105103. doi:10.1088/0957- 0233/21/10/105103. [45] J. Yalong, C. Tingli, Z. Yuquan, Methane monitoring system based on absorption spectroscopy of tunable diode laser, J. Electron. Meas. Instrum. 25 (2011) 265–271. http://en.cnki.com.cn/Article_en/CJFDTOTAL-DZIY201103014.htm (accessed September 20, 2015). [46] W.-H. Zhang, W.-Q. Wang, L. Zhang, X. Dai, X.-L. Liu, L. Jiang, Methane Gas Detection Based on Tunable Diode Laser Absorption Spectroscopy and Optical Fiber Sensing, in: 2014 7th Int. Conf. Intell. Comput. Technol. Autom., IEEE, 2014: pp. 365–368. doi:10.1109/ICICTA.2014.95. [47] Z. Zhang, L. Zhang, TDLAS-based methane telemetry technology, J. Chang. Univ. 21 (2011) 9–12. http://caod.oriprobe.com/articles/27688424/TDLAS_based_methane_telemetry_te chnology.htm (accessed September 21, 2015). [48] J. Cao, K. Zhang, Z. Wang, R. Yang, Y. Wang, Optic Fiber Methane Gas Sensor Based on Tunable Diode Laser Absorption Spectroscopy, in: Symp. Photonics Optoelectron., IEEE, 2010: pp. 1–5. doi:10.1109/SOPO.2010.5504039. [49] A. Sepulveda-Jauregui, K. Martinez-Cruz, A. Strohm, K.M. Walter Anthony, F. Thalasso, A new method for field measurement of dissolved methane in water using infrared tunable diode laser absorption spectroscopy, Limnol. Oceanogr. Methods. 10 (2012) 560–567. doi:10.4319/lom.2012.10.560. [50] A. Berezin, S. Malyugin, A. Nadezhdinskii, D. Namestnikov, Y. Ponurovskii, D. Stavrovskii, Y. Shapovalov, I. Vyazov, Detection of trace amounts of water in ammonia by means of TDLS technique, in: 5th Int. Conf. Tunable Diode Laser Spectrosc., 2005: pp. 67–68. [51] A. Wood, O. Wright, D. Clayton, Detecting single-digit parts per-billion levels of moisture in ammonia gas, 2004. [52] K. Bertness, Application of Diode Laser Spectroscopy for Moisture Contamination Measurement in Semiconductor Processing, in: Overv. IEEE/LEOS Summer Top. Opt. Sens. Semicond. Manuf., Aventura, USA, 2000: pp. II7-II8. [53] M.W. Markus, State-of-the-art of diode-laser based gas analysis in process industries, in: 5th Int. Conf. Tunable Diode Laser Spectrosc., 2005: pp. 22–23. [54] M.A. Bolshov, Y.A. Kuritsyn, Laser Analytical Spectroscopy, in: Ullmann’s Encylopedia References 91 Ind. Chem., Wiley-VCH Verlag GmbH and Co, Weinheim, Germany, 2006: pp. 1–27. [55] A. Pogány, S. Wagner, O. Werhahn, V. Ebert, Development and metrological characterization of a tunable diode laser absorption spectroscopy (TDLAS) spectrometer for simultaneous absolute measurement of carbon dioxide and water vapor., Appl. Spectrosc. 69 (2015) 257–68. doi:10.1366/14-07575. [56] D.R. Haylett, D.F. Davidson, R.D. Cook, Z. Hong, W. Ren, S.H. Pyun, R.K. Hanson, Multi- species time-history measurements during n-hexadecane oxidation behind reflected shock waves, Proc. Combust. Inst. 34 (2013) 369–376. doi:10.1016/j.proci.2012.06.014. [57] D. Zou, W. Chen, Z. Du, H. Jia, R. Qi, H. Li, Y. Zhen, Y. Hou, K. Xu, Selection of Digital Filtering in the Escaping Ammonia Monitoring with TDLAS, Spectrosc. Spectr. Anal. 32 (2012) 2322–2326. doi:http://dx.doi.org/10.3964/j.issn.1000-0593(2012)09-2322- 05. [58] Z. Zhang, D. Zou, W. Chen, H. Zhao, K. Xu, Online Monitoring of Escaped Ammonia Based on TDLAS, in: Int. Photonics Optoelectron. Meet., OSA, Washington, D.C., 2013: p. NSa3A.22. doi:10.1364/N3.2013.NSa3A.22. [59] K.D. Casey, R.S. Gates, R.C. Shores, E.D. Thoma, D.B. Harris, Ammonia Emissions from a U.S. Broiler House—Comparison of Concurrent Measurements Using Three Different Technologies, J. Air Waste Manage. Assoc. 60 (2010) 939–948. doi:10.3155/1047-3289.60.8.939. [60] D.J. Bamford, D.J. Cook, S.J. Sharpe, A.D. Van Pelt, Widely tunable rapid scanning mid- infrared laser spectrometer for industrial gas process stream analysis, Appl. Opt. 46 (2007) 3958. doi:10.1364/AO.46.003958. [61] C.N. Harward, R.E. Baren, M.E. Parish, Determination of molecular parameters for 1,3-butadiene and propylene using infrared tunable diode laser absorption spectroscopy, Spectrochim. Ada Part A Mol. Biomol. Spectrosc. 60 (2004) 3421–3429. [62] A. Serdioutchenko, I. Möller, H. Soltwisch, Infrared diode laser absorption Spectroscopy of C2H2 and C2H6 in capacitively coupled methane RF discharges, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 60 (2004) 3311–3318. [63] D.F. Davidson, Z. Hong, G.L. Pilla, A. Farooq, R.D. Cook, R.K. Hanson, Multi-species time-history measurements during n-heptane oxidation behind reflected shock waves, Combust. Flame. 157 (2010) 1899–1905. doi:10.1016/j.combustflame.2010.01.004. [64] E. Tomita, N. Kawahara, A. Nishiyama, M. Shigenaga, In situ Measurement of Fuel Concentration of Hydrocarbon near Spark Plug in an Engine Cylinder by 3.392 µm Infrared Laser Absorption Method: Application to Actual Engine, Meas. Sci. Technol. 14 (2003) 1357–1363. doi:10.1299/kikaib.70.518. [65] E. Tomita, N. Kawahara, M. Shigenaga, A. Nishiyama, In situ Measurement of Fuel Concentration of Hydrocarbon near Spark Plug in an Engine Cylinder by 3.392 µm Inflamed Laser Absorption Method: Discussion of Applicability with Homogeneous Methane-Air Mixture, Meas. Sci. Technol. 14 (2003) 1350–1356. doi:10.1299/kikaib.70.511. References 92 [66] S. Hyun Pyun, MID-IR Laser-Based Diagnostics for hydrocarbon fuel vapor sensing and Decomposition Species Measurements, Standford University, 2012. [67] J.M. Porter, J.B. Jeffries, R.K. Hanson, Mid-infrared laser-absorption diagnostic for vapor-phase fuel mole fraction and liquid fuel film thickness, Appl. Phys. B Lasers Opt. 102 (2011) 345–355. doi:10.1007/s00340-010-3942-9. [68] R.C. Sharma, M. Blitz, R. Wada, P.W. Seakins, HCl yield and chemical kinetics study of the reaction of Cl atoms with CH3I at the 298K temperature using the infra-red tunable diode laser absorption spectroscopy., Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 128 (2014) 176–82. doi:10.1016/j.saa.2014.02.065. [69] F. Stritzke, O. Diemel, S. Wagner, TDLAS-based NH3 mole fraction measurement for exhaust diagnostics during selective catalytic reduction using a fiber-coupled 2.2μm DFB diode laser, Appl. Phys. B. 119 (2015) 143–152. doi:10.1007/s00340-015-6073- 5. [70] Z. Hong, D.F. Davidson, E.A. Barbour, R.K. Hanson, A new shock tube study of the H+O2→OH+O reaction rate using tunable diode laser absorption of H2O near 2.5μm, Proc. Combust. Inst. 33 (2011) 309–316. doi:10.1016/j.proci.2010.05.101. [71] W. Feng, J.F. Hershberger, Infrared diode laser study of the kinetics of the NCCO+O2 reaction, Chem. Phys. Lett. 488 (2010) 140–144. doi:10.1016/j.cplett.2010.02.029. [72] J.K. Lefkowitz, M. Uddi, B.C. Windom, G. Lou, Y. Ju, In situ species diagnostics and kinetic study of plasma activated ethylene dissociation and oxidation in a low temperature flow reactor, Proc. Combust. Inst. 35 (2015) 3505–3512. doi:10.1016/j.proci.2014.08.001. [73] H. Li, Near-infrared diode laser absorption spectroscopy with applications to reactive systems and combustion control, Standford University, 2007. http://hanson.stanford.edu/dissertations/Li_2007.pdf. [74] Teledyne Analytical Instruments, Laser in Situ Online Gas Analysis System Application Guide for the Petrochemical Industry, (2006) 1–29. [75] M.B. Frish, M.C. Laderer, R.T. Wainner, A.O. Wright, A.H. Patel, J. Stafford-Evans, J.R. Morency, M.G. Allen, B.D. Green, The next generation of TDLAS analyzers, in: C.D. Brown, M.A. Druy, J.P. Coates (Eds.), Opt. East 2007, International Society for Optics and Photonics, 2007: pp. 676506-676506–10. doi:10.1117/12.738195. [76] M. Lackner, Gas sensing in industry by tunable diode laser spectroscopy (TDLS), Verlag ProcessEng Engineering GmbH, 2008. [77] N. Jacquinet-Husson, L. Crepeau, R. Armante, C. Boutammine, a. Chédin, N. a. Scott, C. Crevoisier, V. Capelle, C. Boone, N. Poulet-Crovisier, a. Barbe, a. Campargue, D. Chris Benner, Y. Benilan, B. Bézard, V. Boudon, L.R. Brown, L.H. Coudert, a. Coustenis, V. Dana, V.M. Devi, S. Fally, a. Fayt, J.M. Flaud, a. Goldman, M. Herman, G.J. Harris, D. Jacquemart, a. Jolly, I. Kleiner, a. Kleinböhl, F. Kwabia-Tchana, N. Lavrentieva, N. Lacome, L.H. Xu, O.M. Lyulin, J.Y. Mandin, a. Maki, S. Mikhailenko, C.E. Miller, T. Mishina, N. Moazzen-Ahmadi, H.S.P. Müller, a. Nikitin, J. Orphal, V. Perevalov, a. Perrin, D.T. Petkie, a. Predoi-Cross, C.P. Rinsland, J.J. Remedios, M. Rotger, M. a H. Smith, K. Sung, S. Tashkun, J. Tennyson, R. a. Toth, a. C. Vandaele, J. Vander Auwera, References 93 The 2009 edition of the GEISA spectroscopic database, J. Quant. Spectrosc. Radiat. Transf. 112 (2011) 2395–2445. doi:10.1016/j.jqsrt.2011.06.004. [78] L.S.S. Rothman, I.E.E. Gordon, R.J.J. Barber, H. Dothe, R.R.R. Gamache, A. Goldman, V.I. Perevalov, S.A. a. Tashkun, J. Tennyson, HITEMP, the high-temperature molecular spectroscopic database, J. Quant. Spectrosc. Radiat. Transf. 111 (2010) 2139–2150. doi:10.1016/j.jqsrt.2010.05.001. [79] P.M. Chu, F.R. Guenther, G.C. Rhoderick, W.J. Lafferty, The NIST quantitative infrared database, J. Res. Natl. Inst. Stand. Technol. 104 (1999) 59. doi:10.6028/jres.104.004. [80] S.W. Sharpe, T.J. Johnson, R.L. Sams, P.M. Chu, G.C. Rhoderick, P.A. Johnson, Gas- phase databases for quantitative infrared spectroscopy., Appl. Spectrosc. 58 (2004) 1452–61. doi:10.1366/0003702042641281. [81] M.A. Alrefae, Mid-IR Absorption Cross-Section Measurements of Hydrocarbons, King Abdullah University of Science and Technology, 2013. [82] M. Alrefae, E.T. Es-Sebbar, A. Farooq, Absorption cross-section measurements of methane, ethane, ethylene and methanol at high temperatures, J. Mol. Spectrosc. 303 (2014) 8–14. doi:10.1016/j.jms.2014.06.007. [83] D.K. Edwards, W. a. Menard, Comparison of Models for Correlation of Total Band Absorption, Appl. Opt. 3 (1964) 621. doi:10.1364/AO.3.000621. [84] M.F. Campbell, K.G. Freeman, D.F. Davidson, R.K. Hanson, FTIR measurements of mid- IR absorption spectra of gaseous fatty acid methyl esters at T=25-500 °C, J. Quant. Spectrosc. Radiat. Transf. 145 (2014) 57–73. doi:10.1016/j.jqsrt.2014.04.017. [85] F. Träger, Springer Handbook of Laser and Optics, Springer, Berlin, 2012. [86] K. Thyagarajan, A. Ghatak, Lasers: Fundamentals and Applications, 2010. doi:10.1017/CBO9781107415324.004. [87] R. Mével, P.A. Boettcher, J.E. Shepherd, Absorption Cross Section at 3.39 μm of Alkanes, Aromatics and Substituted Hydrocarbons, Chem. Phys. Lett. 531 (2012) 22– 27. [88] S.D. Williams, T.J. Johnson, S.W. Sharpe, V. Yavelak, R.P. Oates, C.S. Brauer, Quantitative vapor-phase IR intensities and DFT computations to predict absolute IR spectra based on molecular structure: I. Alkanes, J. Quant. Spectrosc. Radiat. Transf. 129 (2013) 298–307. doi:10.1016/j.jqsrt.2013.07.005. [89] J.M. Andrade, M.S. Sánchez, L.A. Sarabia, Applicability of high-absorbance MIR spectroscopy in industrial quality control of reformed gasolines, Chemom. Intell. Lab. Syst. 46 (1999) 41–55. doi:10.1016/S0169-7439(98)00156-7. [90] A. Iob, M.A. Ali, B.S. Tawabini, N.M. Abbas, Hydrocarbon group (PONA) analysis of reformate by FT-I.R. spectroscopy, Fuel. 75 (1996) 1060–1064. doi:10.1016/0016- 2361(96)00080-4. [91] J. Laxalde, N. Caillol, F. Wahl, C. Ruckebusch, L. Duponchel, Combining near and mid infrared spectroscopy for heavy oil characterisation, Fuel. 133 (2014) 310–316. doi:10.1016/j.fuel.2014.05.041. [92] E.D. Yalvac, M.B. Seasholtz, S.R. Crouch, Evaluation of Fourier Transform Near- Infrared for the Simultaneous Analysis of Light Alkene Mixtures, Appl. Spectrosc. 51 References 94 (1997) 1303–1310. doi:10.1366/0003702971942303. [93] M.Z. Nikos Pasadakis, Giorgos Livanos, Deconvolving the absorbance of methyl and methylene groups in the FT-IR 3000-2800 cm-1 band of petrοleum fractions, Trends Appl. Spectrosc. 10 (2013) 25–35. [94] G. Livanos, M. Zervakis, N. Pasadakis, M. Karelioti, G. Giakos, Deconvolution of petroleum mixtures using mid-FTIR analysis and non-negative matrix factorization, Meas. Sci. Technol. 27 (2016) 114005. doi:10.1088/0957-0233/27/11/114005. [95] G. Livanos, M. Zervakis, M. Kerelioti, Application of Non-Negative Matrix Factorization for the Deconvolution of Petroleum Mixtures Using Mid FTIR analysis, (2015). [96] M.D. Ruiz, I.T. Bustamante, A. Dago, N. Hernández, A.C. Núñez, D. Porro, A multivariate calibration approach for determination of petroleum hydrocarbons in water by means of IR spectroscopy, J. Chemom. 24 (2010) 444–447. doi:10.1002/cem.1284. [97] N. Pasadakis, A.A. Kardamakis, Identifying constituents in commercial gasoline using Fourier transform-infrared spectroscopy and independent component analysis, Anal. Chim. Acta. 578 (2006) 250–255. doi:10.1016/j.aca.2006.06.072. [98] M.N. Siddiqui, Estimating Methyl/Methylene Groups in Asphaltene and Other Fractions of Asphalt, Pet. Sci. Technol. 26 (2008) 2048–2057. doi:10.1080/10916460701428995. [99] R.R. Coelho, I. Hovell, M.B. De Mello Monte, A. Middea, A.L. De Souza, Characterisation of aliphatic chains in vacuum residues (VRs) of asphaltenes and resins using molecular modelling and FTIR techniques, Fuel Process. Technol. 87 (2006) 325–333. doi:10.1016/j.fuproc.2005.10.010. [100] N. Kosal, A. Bhairi, M.A. Ali, Determination of hydrocarbon types in naphthas, gasolines and kerosenes: a review and comparative study of different analytical procedures, Fuel. 69 (1990) 1012–1019. doi:10.1016/0016-2361(90)90013-G. [101] J.J. Workman, NIR Spectroscopy Calibration Basics, in: Handb. Near-Infrared Anal., 2007: pp. 123–150. [102] A. International, ASTM E1655-05(2012) Standard Practices for Infrared Multivariate Quantitative Analysis, West Conshohocken, 2012. [103] D.J.M. Hayes, M.H.B. Hayes, J.J. Leahy, Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions, Fuel. 150 (2015) 261–268. doi:10.1016/j.fuel.2015.01.094. [104] H.M. Hulburt, S. Katz, Some problems in particle technology, Chem. Eng. Sci. 19 (1964) 555–574. doi:10.1016/0009-2509(64)85047-8. [105] S.W. Benson, F.R. Cruickshank, D.M. Golden, G.R. Haugen, H.E. O’Neal, a S. Rodgers, R. Shaw, R. Walsh, Additivity rules for the estimation of thermochemical properties, 1969. doi:10.1021/cr60259a002. [106] K.G. Joback, R.C. Reid, Estimation of Pure-Component Properties from Group- Contributions, Chem. Eng. Commun. 57 (1987) 233–243. doi:10.1080/00986448708960487. [107] E.R. Ritter, J.W. Bozzelli, THERM: Thermodynamic property estimation for gas phase References 95 radicals and molecules, Int. J. Chem. Kinet. 23 (1991) 767–778. doi:10.1002/kin.550230903. [108] M. Sattari, A. Kamari, H. Hashemi, A.H. Mohammadi, D. Ramjugernath, A group contribution model for prediction of the viscosity with temperature dependency for fluorine-containing ionic liquids, J. Fluor. Chem. 186 (2016) 19–27. doi:10.1016/j.jfluchem.2016.04.001. [109] T. He, S. Li, Y. Chi, H.-B. Zhang, Z. Wang, B. Yang, X. He, X. You, An adaptive distance- based group contribution method for thermodynamic property prediction, Phys. Chem. Chem. Phys. Phys. Chem. Chem. Phys. 18 (2016) 23822–23830. doi:10.1039/c6cp02929a. [110] H.K. Hansen, P. Rasmussen, A. Fredenslund, M. Schiller, J. Gmehling, Vapor-liquid equilibria by UNIFAC group contribution. 5. Revision and extension, Ind. Eng. Chem. Res. 30 (1991) 2352–2355. doi:10.1021/ie00058a017. [111] Y. Nannoolal, Development of a group contribution method for the prediction of normal boiling points of non-electrolyte organic compounds, University of Kwazulu- Natal Durban, 2004. [112] J.F. Pankow, W.E. Asher, SIMPOL.1: a simple group contribution method for predicting vapor pressures and enthalpies of vaporization of multifunctional organic compounds, Atmos. Chem. Phys. 8 (2008) 2773–2796. doi:10.5194/acpd-7-11839- 2007. [113] J. Gmehling, D. Constantinescu, B. Schmid, Group Contribution Methods for Phase Equilibrium Calculations, Annu. Rev. Chem. Biomol. Eng. 6 (2015) 267–292. doi:10.1146/annurev-chembioeng-061114-123424. [114] Q. Jia, Q. Wang, P. Ma, S. Xia, F. Yan, H. Tang, Prediction of the Flash Point Temperature of Organic Compounds with the Positional Distributive Contribution Method, J. Chem. Eng. Data. 57 (2012) 3357–3367. doi:10.1021/je301070f. [115] D. Mathieu, T. Alaime, Insight into the contribution of individual functional groups to the flash point of organic compounds, J. Hazard. Mater. 267 (2014) 169–174. doi:10.1016/j.jhazmat.2013.12.047. [116] T.A. Albahri, MNLR and ANN structural group contribution methods for predicting the flash point temperature of pure compounds in the transportation fuels range, Process Saf. Environ. Prot. 93 (2015) 182–191. doi:10.1016/j.psep.2014.03.005. [117] T.A. Albahri, Prediction of the Upper Flammability Limit of Pure Compounds, Asian J. Appl. Sci. Eng. 5 (2016) 59–70. [118] T.A. Albahri, Method for predicting the standard net heat of combustion for pure hydrocarbons from their molecular structure, Energy Convers. Manag. 76 (2013) 1143–1149. doi:10.1016/j.enconman.2013.09.019. [119] T.A. Albahri, Prediction of the aniline point temperature of pure hydrocarbon liquids and their mixtures from molecular structure, J. Mol. Liq. 174 (2012) 80–85. doi:10.1016/j.molliq.2012.07.012. [120] T.A. Albahri, R.S. George, Artificial Neural Network Investigation of the Structural Group Contribution Method for Predicting Pure Components Auto Ignition References 96 Temperature, Ind. Eng. Chem. Res. 42 (2003) 5708–5714. doi:10.1021/ie0300373. [121] M. Dahmen, W. Marquardt, A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons, Energy & Fuels. 29 (2015) 5781–5801. doi:10.1021/acs.energyfuels.5b01032. [122] T. a. Albahri, Structural Group Contribution Method for Predicting the Octane Number of Pure Hydrocarbon Liquids, Ind. Eng. Chem. Res. 42 (2003) 657–662. doi:10.1021/ie020306+. [123] P. Ghosh, K.J. Hickey, S.B. Jaffe, Development of a detailed gasoline composition- based octane model, Ind. Eng. Chem. Res. 45 (2006) 337–345. doi:10.1021/ie050811h. [124] M.V. Maylin, M.V. Kirgina, E.V. Sviridova, B.V. Sakhnevitch, E.D. Ivanchina, Calculation of Gasoline Octane Numbers Taking into Account the Reaction Interaction of Blend Components, Procedia Chem. 10 (2014) 477–484. doi:10.1016/j.proche.2014.10.080. [125] A.E. Klingbeil, J.B. Jeffries, R.K. Hanson, Temperature- and pressure-dependent absorption cross sections of gaseous hydrocarbons at 3.39 µm, Meas. Sci. Technol. 17 (2006) 1950–1957. doi:10.1088/0957-0233/17/7/038. [126] A.E. Klingbeil, J.B. Jeffries, R.K. Hanson, Temperature-dependent mid-IR absorption spectra of gaseous hydrocarbons, J. Quant. Spectrosc. Radiat. Transf. 107 (2007) 407– 420. doi:10.1016/j.jqsrt.2007.03.004. [127] S.A. Francis, Absolute Intensities of Characteristic Infra-Red Absorption Bands of Aliphatic Hydrocarbons, J. Chem. Phys. 18 (1950) 861. [128] R.N. Jones, The intensities of the infra-red absorption bands of n-paraffin hydrocarbons, Spectrochim. Acta. 9 (1957) 235–251. doi:10.1016/0371- 1951(57)80137-4. [129] A.S. Wexler, Infrared determination of structural units in organic compounds by integrated intensity measurements: Alkanes, alkenes and monosubstituted alkyl benzenes, Spectrochim. Acta. 21 (1965) 1725–1742. doi:10.1016/0371- 1951(65)80085-6. [130] R. Rericha, P. Jarolímek, M. Horák, Determination of the degree of branching in alkanes by infrared spectroscopy. II. Variation of the intensity of the antisymmetrical stretching vibration bands of C-H bonds in CH3 and CH2 groups, Collect. Czechoslov. Chem. Commun. 32 (1967) 1903–1912. [131] L.W. Pinkley, P.P. Sethna, D. Williams, Infrared band intensities of saturated hydrocarbons, J. Phys. Chem. 82 (1978) 1532–1537. doi:10.1021/j100502a014. [132] M. Gussoni, S. Abbate, B. Dragoni, G. Zerbi, Prediction of infrared intensities of n- alkanes by electrooptical parameters, J. Mol. Struct. 61 (1980) 355–359. [133] M. Gussoni, P. Jona, G. Zerbi, Characterization of the C-H bond in hydrocarbons by infrared intensities, J. Mol. Struct. 80 (1982) 355–358. [134] M. Gussoni, Infrared intensities: A new tool in chemistry., J. Mol. Struct. 141 (1986) 63–92. doi:10.1016/0022-2860(86)80313-1. [135] C. Castiglioni, M. Gussoni, G. Zerbi, Characteristic infrarred intensities of C-H bonds, References 97 J. Mol. Struct. 141 (1986) 341–346. [136] B. Galabov, T. Dudev, Bond Properties and Molecular Conformation from Vibrational Intensity Analysis, Croat. Chem. Acta. 61 (1988) 569–587. [137] D. Steele, K. Hamilton, J. Stevens, The use of absolute vibrational band intensities in structural analysis. I. n-Alkanes and derived ketones, Croat. Chem. Acta. 61 (1988) 551–567. [138] B. Mistry, A handbook of Spectroscopic Data Chemistry (UV, IR, PMR, CNMR and Mass Spectroscopy), Oxford Book Company, Jaipur, India, 2009. [139] J.S. Buchanan, Reactions of model compounds over steamed ZSM-5 at simulated FCC reaction conditions, Appl. Catal. 74 (1991) 83–94. doi:10.1016/0166-9834(91)90010- 6. [140] R. Hughes, G. Hutchings, C.L. Koon, B. McGhee, C.E. Snape, Catalyst Deactivation 1994, Proceedings of the 6th International Symposium, Elsevier, 1994. doi:10.1016/S0167-2991(08)62763-9. [141] S. Al-Khattaf, H. de Lasa, The role of diffusion in alkyl-benzenes catalytic cracking, Appl. Catal. A Gen. 226 (2002) 139–153. doi:10.1016/S0926-860X(01)00895-X. [142] G. Tonetto, J. Atias, H. de Lasa, FCC catalysts with different zeolite crystallite sizes: acidity, structural properties and reactivity, Appl. Catal. A Gen. 270 (2004) 9–25. doi:10.1016/j.apcata.2004.03.042. [143] G. Jimeńez-García, H. de Lasa, R. Quintana-Soloŕzano, R. Maya-Yescas, Catalyst activity decay due to pore blockage during catalytic cracking of hydrocarbons, Fuel. 110 (2013) 89–98. doi:10.1016/j.fuel.2012.10.082. [144] M. Al-Sabawi, J. a. Atias, H. de Lasa, Kinetic modeling of catalytic cracking of gas oil feedstocks: Reaction and diffusion phenomena, Ind. Eng. Chem. Res. 45 (2006) 1583– 1593. doi:10.1021/ie050683x. [145] S. Al-Khattaf, H.I. de Lasa, Diffusion and reactivity of gas oil in FCC catalysts, Can. J. Chem. Eng. 79 (2001) 341–348. doi:10.1002/cjce.5450790306. [146] S. Al-Khattaf, J. A. Atias, K. Jarosch, H. de Lasa, Diffusion and catalytic cracking of 1,3,5 tri-iso-propyl-benzene in FCC catalysts, Chem. Eng. Sci. 57 (2002) 4909–4920. doi:10.1016/S0009-2509(02)00277-4. [147] J.A. Atias, G. Tonetto, H. de Lasa, Catalytic Conversion of 1,2,4-Trimethylbenzene in a CREC Riser Simulator. A Heterogeneous Model with Adsorption and Reaction Phenomena, Ind. Eng. Chem. Res. 42 (2003) 4162–4173. doi:10.1021/ie0300878. [148] J.A. Atias, H. de Lasa, Adsorption and catalytic reaction in FCC catalysts using a novel fluidized CREC riser simulator, Chem. Eng. Sci. 59 (2004) 5663–5669. doi:10.1016/j.ces.2004.07.100. [149] S. Al-Khattaf, H. de Lasa, Catalytic Cracking of Alkylbenzenes. Y-zeolites with Different Crystal Sizes, Elsevier, 2001. doi:10.1016/S0167-2991(01)82327-2. [150] G. Jiménez-García, H. de Lasa, R. Maya-Yescas, Simultaneous estimation of kinetics and catalysts activity during cracking of 1,3,5-tri-isopropyl benzene on FCC catalyst, Catal. Today. 220–222 (2014) 178–185. doi:10.1016/j.cattod.2013.10.026. [151] M.E. MacDonald, Decomposition Kinetics of the Rocket Propellant RP-1 and Its References 98 Chemical Kinetic Surrogates, Stanford University, 2012. [152] Z. Hong, K.Y. Lam, D.F. Davidson, R.K. Hanson, A comparative study of the oxidation characteristics of cyclohexane, methylcyclohexane, and n-butylcyclohexane at high temperatures, Combust. Flame. 158 (2011) 1456–1468. doi:10.1016/j.combustflame.2010.12.019. [153] H. de Lasa, Novel Riser Reactor Simulator, Canadian Patent 1 284 017, 1991. [154] Y. Aponte, G. Che-Galicia, H. de Lasa, A fluidizable Zn-offretite for selective thiophenic species adsorption. Additive performance under FCC conditions, Fuel. 186 (2016) 222–234. doi:10.1016/j.fuel.2016.08.020. [155] S. Al-Ghamdi, M. Volpe, M.M. Hossain, H. de Lasa, VOx/c-Al2O3catalyst for oxidative dehydrogenation of ethane to ethylene: Desorption kinetics and catalytic activity, Appl. Catal. A Gen. 450 (2013) 120–130. doi:10.1016/j.apcata.2012.10.007. [156] S.A. Al-Ghamdi, H.I. de Lasa, Propylene production via propane oxidative dehydrogenation over VOx/γ-Al2O3 catalyst, Fuel. 128 (2014) 120–140. doi:10.1016/j.fuel.2014.02.033. [157] M.Y. Khan, S. Al-Ghamdi, S.A. Razzak, M.M. Hossain, H. de Lasa, Fluidized bed oxidative dehydrogenation of ethane to ethylene over VOx/Ce-ΓAl2O3 catalysts: Reduction kinetics and catalyst activity, Mol. Catal. 443 (2017) 78–91. doi:10.1016/j.mcat.2017.09.025. [158] A.A.H. Elbadawi, M.S. Ba-Shammakh, S. Al-Ghamdi, S.A. Razzak, M.M. Hossain, H.I. de Lasa, A fluidizable VOx/γ-Al2O3-ZrO2 catalyst for the ODH of ethane to ethylene operating in a gas phase oxygen free environment, Chem. Eng. Sci. 145 (2016) 59–70. doi:10.1016/j.ces.2016.01.050. [159] S.A. Al-Ghamdi, M.M. Hossain, H.I. de Lasa, Kinetic modeling of ethane oxidative dehydrogenation over VO x/Al2O3 catalyst in a fluidized-bed riser simulator, Ind. Eng. Chem. Res. 52 (2013) 5235–5244. doi:10.1021/ie303305c. [160] J. Mazumder, H.I. de Lasa, Steam gasification of a cellulosic biomass surrogate using a Ni/La2O3-ΓAl2O3catalyst in a CREC fluidized riser simulator. Kinetics and model validation, Fuel. 216 (2018) 101–109. doi:10.1016/j.fuel.2017.11.074. [161] E. Salaices, B. Serrano, H. De Lasa, Biomass catalytic steam gasification thermodynamics analysis and reaction experiments in a CREC riser simulator, Ind. Eng. Chem. Res. 49 (2010) 6834–6844. doi:10.1021/ie901710n. [162] S. Al-Khattaf, S. Rabiu, N.M. Tukur, R. Alnaizy, Kinetics of toluene methylation over USY-zeolite catalyst in a riser simulator, Chem. Eng. J. 139 (2008) 622–630. doi:10.1016/j.cej.2007.09.019. [163] N.M. Tukur, S. Al-Khattaf, Comparison studies of xylene isomerization and disproportionation reactions between SSZ-33, TNU-9, mordenite and ZSM-5 zeolite catalysts, Chem. Eng. J. 166 (2011) 348–357. doi:10.1016/j.cej.2010.11.004. [164] S. Rabiu, S. Al-Khattaf, Kinetics of Toluene Methylation over ZSM-5 Catalyst in a Riser Simulator, Ind. Eng. Chem. Res. 47 (2007) 39–47. doi:10.1021/ie071038o. [165] Z. Nawaz, X. Tang, J. Zhu, F. Wei, S. Naveed, Catalytic Cracking of 1-Hexene to Propylene Using SAPO-34 Catalysts with Different Bulk Topologies, Chinese J. Catal. References 99 30 (2009) 1049–1057. doi:10.1016/S1872-2067(08)60137-0. [166] A. Alkhlel, Catalytic cracking of hydrocarbon in a CREC riser simulator using various catalysts. Assesing the catalyst/oil ratio effect. (In Progress), The University of Western Ontario, 2019. [167] M.R. Quddus, A Novel Mixed Metallic Oxygen Carrier for Chemical Looping Combustion: Preparation, Characterization and Kinetic Modeling, University of Western Ontario, 2013. [168] D.W. Kraemer, Modelling Catalytic Cracking in a Novel Riser Simulator, The University of Western Ontario, 1991. [169] O. Bilous, N.R. Amundson, Chemical reactor stability and sensitivity: II. Effect of parameters on sensitivity of empty tubular reactors, AIChE J. 2 (1956) 117–126. doi:10.1002/aic.690020124. Appendix A: List of Components Employed for the Group Contribution Method CAS # BD Compound Name Chemical Class Molecular Formula Number 1 Ethane 74-84-0 Alkane C2H6 2 Propane 74-98-6 Alkane C3H8 3 n-Butane 106-97-8 Alkane C4H10 4 Pentane 109-66-0 Alkane C5H12 5 n-Hexane 110-54-3 Alkane C6H14 6 n-Heptane 142-82-5 Alkane C7H16 7 Octane 111-65-9 Alkane C8H18 8 n-Nonane 111-84-2 Alkane C9H20 9 n-Decane 124-18-5 Alkane C10H22 10 n-Undecane 1120-21-4 Alkane C11H24 11 n-Tridecane 629-50-5 Alkane C13H28 12 n-Pentadecane 629-62-9 Alkane C15H32 13 n-hexadecane 544-76-3 Alkane C16H34 14 Isobutane (2-methylpropane) 75-28-5 Alkane C4H10 15 Isopentane (2-methylbutane) 78-78-4 Alkane C5H12 16 2-Methylpentane 107-83-5 Alkane C6H14 17 3-Methylpentane 96-14-0 Alkane C6H14 18 3-Methylhexane 589-34-4 Alkane C7H16 19 2,2-Dimethyl butane 75-83-2 Alkane C6H14 20 2,3-Dimethylbutane 79-29-8 Alkane C6H14 21 Isooctane (2,2,4-trimethylpentane) 540-84-1 Alkane C8H18 22 Ethylene 74-85-1 Alkene C2H4 23 Propylene 115-07-1 Alkene C3H6 24 1-Butene 25167-67-3 Alkene C4H8 25 1-Pentene 109-67-1 Alkene C5H10 26 1-Hexene 592-41-6 Alkene C6H12 27 n-Heptene 592-76-7 Alkene C7H14 Appendix A 102 28 1-Octene 111-66-0 Alkene C8H16 29 1-Nonene 124-11-8 Alkene C9H18 30 2-Butene 107-01-7 Alkene C4H8 31 Isobutene 115-11-7 Alkene C4H8 32 2-Methyl-1-butene 563-46-2 Alkene C5H10 33 3-Methyl-1-butene 563-45-1 Alkene C5H10 34 2-Methyl-2-butene 513-35-9 Alkene C5H10 35 cis-2-Pentene 627-20-3 Alkene C5H10 36 trans-2-Pentene 646-04-8 Alkene C5H10 37 4-Methyl-1-pentene 691-37-2 Alkene C6H12 38 2-Methyl-2-pentene 625-27-4 Alkene C6H12 39 cis-4-Methyl-2-pentene 691-38-3 Alkene C6H12 40 2,4,4-Trimethyl-1-pentene 107-39-1 Alkene C8H16 41 2,4,4-Trimethyl-2-pentene 107-40-4 Alkene C8H16 42 Benzene 71-43-2 Aromatic C6H6 43 Toluene 108-88-3 Aromatic C7H8 44 Ethyl benzene 100-41-4 Aromatic C8H10 45 Cumene 98-82-8 Aromatic C9H12 46 m-Xylene 108-38-3 Aromatic C8H10 47 p-Xylene 106-42-3 Aromatic C8H10 48 o-Xylene 95-47-6 Aromatic C8H10 49 2-Ethyltoluene 611-14-3 Aromatic C9H12 50 3-Ethyltoluene 620-14-4 Aromatic C9H12 1,3,5-Trimethylbenzene Aromatic 51 (Mesitylene) 108-67-8 C9H12 52 4-Ethyltoluene 622-96-8 Aromatic C9H12 53 Isocumene 103-65-1 Aromatic C9H12 54 1,2,3,4-Tetramethylbenzene 488-23-3 Aromatic C10H14 55 1,2,3,5-Tetramethylbenzene 527-53-7 Aromatic C10H14 56 sec-Butylbenzene 138-98-9 Aromatic C10H14 57 tert-Butylbenzene 98-06-6 Aromatic C10H14 58 Cyclopentane 287-92-3 Naphthene C5H10 59 Cyclohexane 110-82-7 Naphthene C6H12 60 Cycloheptane 291-64-5 Naphthene C7H14 61 Cyclooctane 292-64-8 Naphthene C8H16 62 Cyclodecane 293-96-9 Naphthene C10H20 63 Cyclopentene 142-29-0 Naphthene C5H8 Appendix A 103 64 Cyclohexene 110-83-8 Naphthene C6H10 65 Cycloheptene 628-92-2 Naphthene C7H12 Appendix B: List of C-H Bond Classifications for Each Component According to the Group Contribution Method Single bond Double bond Aromatic ring 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # Compound Name -CH3 -CH2- -CH3 -CH2- -CH3 -CH2- -CH3 -CH2- >CH- >C< =CH2 =CH- =C< a=CH a=C< (α) (α) (α) (α) (α) (α) 1 Ethane 2 2 Propane 2 1 3 n-Butane 2 2 4 Pentane 2 3 5 n-Hexane 2 4 6 n-Heptane 2 5 7 Octane 2 6 8 n-Nonane 2 7 9 n-Decane 2 8 10 n-Undecane 2 9 11 n-Tridecane 2 11 Appendix B 106 12 n-Pentadecane 2 13 13 n-hexadecane 2 14 Isobutane (2- 14 1 3 methylpropane) Isopentane (2- 15 1 1 2 1 methylbutane) 16 2-Methylpentane 1 1 1 2 1 17 3-Methylpentane 2 1 1 2 18 3-Methylhexane 2 1 1 1 2 19 2,2-Dimethyl butane 1 1 3 1 20 2,3-Dimethylbutane 2 4 Isooctane (2,2,4- 21 1 1 1 4 1 trimethylpentane) 22 Ethylene 2 23 Propylene 1 1 1 24 1-Butene 1 1 1 1 25 1-Pentene 1 1 1 1 1 26 1-Hexene 1 2 1 1 1 27 n-Heptene 1 3 1 1 1 28 1-Octene 1 4 1 1 1 29 1-Nonene 1 5 1 1 1 30 2-Butene 2 2 31 Isobutene 1 1 2 32 2-Methyl-1-butene 1 1 1 1 1 33 3-Methyl-1-butene 1 2 1 1 Appendix B 107 34 2-Methyl-2-butene 1 1 3 35 cis-2-Pentene 1 2 1 1 36 trans-2-Pentene 1 2 1 1 37 4-Methyl-1-pentene 1 2 1 1 1 38 2-Methyl-2-pentene 1 1 1 2 1 39 cis-4-Methyl-2-pentene 1 2 2 1 2,4,4-Trimethyl-1- 40 1 3 1 1 1 1 pentene 2,4,4-Trimethyl-2- 41 1 3 1 1 2 pentene 42 Benzene 6 43 Toluene 5 1 1 44 Ethyl benzene 1 5 1 1 45 Cumene 1 2 5 1 46 m-Xylene 4 2 2 47 p-Xylene 4 2 2 48 o-Xylene 4 2 2 49 2-Ethyltoluene 1 4 2 1 1 50 3-Ethyltoluene 1 4 2 1 1 1,3,5-Trimethylbenzene 51 3 3 3 (Mesitylene) 52 4-Ethyltoluene 1 4 2 1 1 53 Isocumene 1 1 5 1 1 1,2,3,4- 54 2 4 4 Tetramethylbenzene Appendix B 108 1,2,3,5- 55 2 4 4 Tetramethylbenzene 56 sec-Butylbenzene 1 1 1 1 5 1 57 tert-Butylbenzene 1 3 5 1 58 Cyclopentane 5 59 Cyclohexane 6 60 Cycloheptane 7 61 Cyclooctane 8 62 Cyclodecane 10 63 Cyclopentene 3 2 64 Cyclohexene 4 2 65 Cycloheptene 5 2 Appendix C: Results for Cross Section at a 3.39 μm HeNe Wavelength and Integrated Band Intensities Calculated with GCM and Linear Approximations Cross section (σ) at 3.39 μm HeNe integrated band intensities (ψ) between 3200- wavelength 2800 cm-1 Linear GCM Linear GCM # BD PNNL PNNL tendency (This work) tendency (This work) 1 7.18 19.61 9.35 1826.26 1287.33 1586.37 2 21.16 23.42 14.69 2485.45 2045.78 2300.95 3 27.90 27.23 20.02 3118.45 2804.23 3015.52 4 34.79 31.05 25.36 3823.25 3562.67 3730.10 5 39.66 34.86 30.69 4440.25 4321.12 4444.68 6 41.91 38.67 36.03 4988.45 5079.57 5159.26 7 43.61 42.49 41.36 5566.83 5838.02 5873.83 8 46.96 46.30 46.70 6438.03 6596.46 6588.41 9 48.47 50.12 52.03 7033.38 7354.91 7302.99 10 51.32 53.93 57.37 7740.21 8113.36 8017.56 11 57.17 61.56 68.04 8999.49 9630.25 9446.72 12 63.93 69.18 78.71 10207.09 11147.15 10875.87 13 78.52 73.00 84.05 13810.80 11905.60 11590.45 14 40.09 27.23 25.46 2964.60 2804.23 2639.70 15 29.59 31.05 27.45 3541.05 3562.67 3406.56 16 35.19 34.86 32.78 4183.38 4321.12 4121.13 17 27.32 34.86 29.43 4128.19 4321.12 4173.41 18 32.09 38.67 34.77 4749.70 5079.57 4887.99 19 39.60 34.86 36.27 4005.65 4321.12 3948.21 20 30.33 34.86 33.95 3962.04 4321.12 3861.39 21 52.80 42.49 44.76 5125.97 5838.02 5169.90 22 0.43 -0.79 0.00 373.31 425.94 335.28 23 6.13 4.58 5.22 998.02 1047.82 1047.55 24 9.22 9.96 10.05 1679.82 1669.71 1561.35 25 13.18 15.33 15.39 1755.97 2291.60 2275.93 26 23.12 20.71 20.72 2958.30 2913.48 2990.51 Appendix C 112 27 28.82 26.08 26.06 3626.85 3535.37 3705.08 28 31.65 31.46 31.39 4179.75 4157.25 4419.66 29 33.77 36.83 36.73 4694.62 4779.14 5134.24 30 13.73 9.96 10.44 1735.23 1669.71 1759.81 31 8.53 9.96 7.94 1561.75 1669.71 1723.38 32 13.67 15.33 12.77 2255.16 2291.60 2237.19 33 14.31 15.33 18.59 2198.99 2291.60 2344.09 34 12.05 15.33 13.16 2400.83 2291.60 2435.64 35 14.19 15.33 15.27 2366.50 2291.60 2273.62 36 16.78 15.33 15.27 2522.65 2291.60 2273.62 37 18.98 20.71 22.35 2797.65 2913.48 2698.86 38 18.17 20.71 17.99 3119.10 2913.48 2949.45 39 20.45 20.71 23.81 2864.97 2913.48 3056.35 40 36.33 31.46 33.89 3859.16 4157.25 3916.35 41 32.35 31.46 35.35 4172.56 4157.25 4273.84 42 0.02 1.31 0.00 754.67 754.94 756.94 43 4.67 5.44 4.98 1286.24 1341.23 1320.40 44 10.53 9.58 9.97 1975.80 1927.52 2031.75 45 10.70 13.71 16.97 2501.42 2513.82 2639.48 46 8.84 9.58 9.96 1849.11 1927.52 1883.85 47 9.10 9.58 9.96 1967.44 1927.52 1883.85 48 12.49 9.58 9.96 1829.57 1927.52 1883.85 49 14.86 13.71 14.95 2474.04 2513.82 2595.21 50 14.88 13.71 14.95 2549.11 2513.82 2595.21 51 11.94 13.71 14.94 2377.60 2513.82 2447.30 52 14.56 13.71 14.95 2550.81 2513.82 2595.21 53 17.61 13.71 14.99 2626.49 2513.82 2828.16 54 21.69 17.84 19.93 2987.21 3100.11 3010.75 55 19.72 17.84 19.93 2994.71 3100.11 3010.75 56 12.54 17.84 18.96 3128.54 3100.11 3406.33 57 14.72 17.84 25.80 3058.06 3100.11 3181.13 58 38.24 34.60 26.68 3548.58 3368.91 3572.88 59 68.96 39.00 32.01 4529.23 4122.96 4287.46 60 44.22 43.40 37.35 5044.93 4877.01 5002.04 61 40.11 47.80 42.68 5551.51 5631.06 5716.61 62 54.72 56.60 53.35 6859.11 7139.16 7145.77 63 18.45 30.20 19.24 2315.39 2614.86 2635.24 64 32.52 34.60 24.57 3108.05 3368.91 3349.82 65 28.07 39.00 29.91 3783.67 4122.96 4064.40 Appendix D: Matlab script for the calculation of the Group Contribution Method parameters clc, clear all, close all; difs=2; %% Conversion factor F_np=9.28697e-16; % cm2/molecule Av_num=6.0221415e23; % molecule/mol Factor=F_np.*Av_num; % cm2/mol %% Load data: Bonds information Nombre=[ '../1 ExtraerInformacion/2 DatosTodos/EnlacesMetodoMejorado1.mat']; load(Nombre) %% Components Paraf=[2:14]; B_Paraf=[15:22]; Olef=[23:30]; B_Olef=[31:42]; Arom=[43:58]; Naph=[59:66]; Componentes2=[Paraf, B_Paraf, Olef, B_Olef, Arom, Naph]; Componentes=[1:66]; %% ::::::::::::::::::::: 1. Extract information :::::::::::::::::::::::::: T=50; Xs=[2800:difs:3200]; dxs=Xs(2)-Xs(1); for ii=1:length(Componentes) Nombre=[ '../1 ExtraerInformacion/1 EspectroCompleto/BD' num2str(Componentes(ii)) '_T_' num2str(T) '.mat']; load(Nombre) XX=A{1}; dxs2=XX(2)-XX(1); YYa=A{2}; YY=YYa.*(Factor./10000); % cm2/mol Appendix D 114 for jj=1:length(Xs) Ys(jj,ii)=interp1(XX,YY, Xs(jj)); end end %% ::::::::::::::::::::: 2. Calculate Parameters :::::::::::::::::::::::::: ubs=[1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3 1e3]; lbs=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ]; Co=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]; A=[]; b=[]; Aeq=[]; beq=[]; lb=lbs; ub=ubs; nonlcon=[]; Enlaces=EnlacesMetodoMejorado1(Componentes, :); options=optimoptions('lsqlin','ConstraintTolerance',1e- 12,'FunctionTolerance',1e-12, 'ConstraintTolerance',1e- 12,'OptimalityTolerance',1e-12); for ii=1:length(Xs) NN=[1:length(Co)]; [sigma(:,ii),resnorm,residual,exitflag,output,lambda] = lsqlin(Enlaces(Componentes2,:),Ys(ii,Componentes2)',A,b,Aeq,beq,lb,ub,[] ,options); feval(ii)=resnorm; Teo_i=0; for jj=1:length(Co) kk=NN(jj); Teo_i=Teo_i+sigma(jj,ii).*Enlaces(:,kk); end Teo(ii,:)=Teo_i; Co=sigma(:,ii); end