Modeling, Simulation, and Optimization of Supercritical and Subcritical Fluid Extraction Processes

ISBN-13: 9781118460177 / Angielski / Twarda / 2021 / 288 str.

Ana Najwa (College of Engineering at Universiti Teknologi Ma

Spis treści

Preface xiiiNomenclature xvii1 Fundamentals of Supercritical and Subcritical Fluid Extraction 11.1 Introduction 11.2 Supercritical Fluid Properties 21.3 Subcritical Condition 31.4 Physical Properties of Subcritical Fluid 51.5 Principles of Sub- and Supercritical Extraction Process 71.5.1 Solid Sample Extraction 81.5.2 Liquid Sample Extraction 91.6 Applications of SCF Extraction 111.6.1 Decaffeination of Coffee and Tea 111.6.2 Removal of FFA in Fats and Oils 151.6.3 Enrichment of Tocopherols 171.6.4 Carotenes from Crude Palm Oil and from Palm Fatty Acid Esters 181.7 Solubility of Solutes in SCFs 181.8 Solute-Solvent Compatibility 201.9 Solubility and Selectivity of Low-Volatility Organic Compounds in SCFs 211.10 Method of Solubility Measurement 241.10.1 Static Method 241.10.2 Dynamic Method 251.11 Determination of Solvent 271.11.1 Carbon Dioxide (CO2) 301.11.2 1,1,1,2-Tetrafluoroethane (R134a) as a Solvent 311.12 Important Parameters Affecting Supercritical Extraction Process 361.12.1 Pressure and Temperature 361.12.2 Solvent Flowrate 381.12.3 Cosolvent 391.12.4 Moisture Content 401.12.5 Raw Material 421.13 Profile of Extraction Curves 431.14 Design and Scale Up 452 Modeling and Optimization Concept 472.1 SFE Modeling 472.1.1 Importance of Knowing the Solid Matrix and Selecting a Suitable Model 482.1.2 Different Modeling Approaches in SFE 482.1.2.1 Experimental Models 492.1.2.2 Models Which Are Based on Similarity between Heat and Mass Transfer 492.1.2.3 Models Based on Conservation Balance Equations 492.2 First Principle Modeling 492.2.1 The Equation of Continuity 502.2.2 The Equation of Motion in Terms of tau 502.2.3 The Equation of Energy in Terms of q 522.3 Hybrid Modeling or Gray Box 532.4 ANN 552.4.1 Simple Neural Network Structure 552.4.1.1 Transfer Function 572.4.1.2 Activation Functions 572.4.1.3 Learning Rules 572.4.2 Network Architecture 582.5 Fuzzy Logic 612.5.1 Boolean Logic and Fuzzy Logic 612.5.2 Fuzzy Sets 622.5.3 Membership Function 632.5.3.1 Membership Function Types 632.5.4 Fuzzy Rules 642.5.4.1 Classical Rules and Fuzzy Rules 652.5.5 Fuzzy Expert System and Fuzzy Inference 662.5.5.1 Mamdani FIS 662.5.5.1.1 Fuzzification 662.5.5.1.2 Fuzzy Logical Operation and Rule Evaluation 662.5.5.1.3 Implication Method 672.5.5.1.4 Aggregation of the Rule Outputs 672.5.5.1.5 Defuzzification 672.5.5.2 Sugeno Fuzzy Inference 672.6 Neuro Fuzzy 682.6.1 Structure of a Neuro Fuzzy System 692.6.2 Adaptive Neuro Fuzzy Inference System (ANFIS) 692.6.2.1 Learning in the ANFIS Model 712.7 Optimization 722.7.1 Traditional Optimization Methods 732.7.2 Evolutionary Algorithm 742.7.3 Simulated Annealing Algorithm 742.7.4 Genetic Algorithm 752.7.4.1 Genetic Algorithm Definitions 752.7.4.2 Genetic Algorithms Overview 762.7.4.3 Preliminary Considerations 772.7.4.4 Overview of Genetic Programming 782.7.4.5 Implementation Details 792.7.4.5.1 Selection Operator 792.7.4.5.2 Crossover Operator 792.7.4.5.3 Mutation Operator 792.7.4.6 Effects of Genetic Operators 802.7.4.7 The Algorithms 803 Physical Properties of Palm Oil as Solute 833.1 Introduction 833.2 Palm Oil Fruit 833.3 Palm Oil Physical and Chemical Properties 843.3.1 Palm Oil Triglycerides 853.3.2 Minor Components in Palm Oil 893.4 Vegetable Oil Refining 913.5 Conventional Palm Oil Refining Process 913.5.1 Chemical Refining 933.5.2 Physical Refining 973.5.3 Effect of Palm Oil Refining 983.6 Conclusions 1004 First Principle Supercritical and Subcritical Fluid Extraction Modeling 101Part I: Modeling Methodology 1014.1 Introduction 1014.2 Phase Equilibrium Modeling 1014.3 The Redlich-Kwong-Aspen Equation of State 1024.3.1 Calculations of Pure Component Parameters for the RKA-EOS 1024.3.2 Binary Mixture Calculations 1034.4 Palm Oil System Characterization 1034.4.1 Palm Oil Triglycerides 1044.4.2 Free Fatty Acids 1064.4.3 Palm Oil Minor Components 1064.5 Development of Aspen Plus(r) Physical Property Database for Palm Oil Components 1074.5.1 Vapor Pressure Estimation 1074.5.2 Estimation of Pure Component Critical Properties 1084.5.2.1 Critical Properties Estimation Using Normal Boiling Point 1084.5.2.2 Critical Properties Estimation Using One Vapor Pressure Point 1104.6 Binary Interaction Parameters Calculations 1104.7 Supercritical Fluid Extraction Process Development 1134.7.1 Hydrodynamics of Countercurrent SFE Process 1134.7.2 Solubility of Palm Oil in Supercritical CO2 1154.7.3 Process Modeling and Simulation 1164.7.3.1 Simple Countercurrent Extraction 1184.7.3.2 Countercurrent Extraction with External Reflux 1184.7.4 Process Analysis and Optimization 119Part II: Results and Discussion 1204.8 Palm Oil Component Physical Properties 1204.8.1 Vapor Pressure of Palm Oil Components 1204.8.2 Pure Component Critical Properties 1224.9 Regression of Interaction Parameters for the Palm Oil Components-Supercritical CO2 Binary System 1224.9.1 Binary System: Triglyceride - Supercritical CO2 1234.9.2 Binary System: Oleic Acid - Supercritical CO2 1264.9.3 Binary System: alpha-Tocopherol - Supercritical CO2 1284.9.4 Binary System: ß-Carotene - Supercritical CO2 1304.9.5 Temperature-Dependent Interaction Parameters 1314.10 Phase Equilibrium Calculation for the Palm Oil-Supercritical CO2 System 1324.11 Ternary System: CO2 - Triglycerides - Free Fatty Acids 1334.12 Distribution Coefficients of Palm Oil Components 1344.13 Separation Factor Between Palm Oil Components 1384.13.1 Separation Factor Between Fatty Acids and Triglycerides 1394.13.2 Separation Factor Between Fatty Acids and alpha-Tocopherols 1404.14 Base Case Process Simulation 1414.14.1 Palm Oil Deacidification Process 1414.14.1.1 Solubility of Palm Oil in Supercritical CO2 1414.14.1.2 Palm Oil Deacidification Process: Comparison to Pilot Plant Results 1424.15 Conclusion 1455 Application of Other Supercritical and Subcritical Modeling Techniques 1475.1 Mass Transfer, Correlation, ANN, and Neuro Fuzzy Modeling of Sub-and Supercritical Fluid Extraction Processes 1475.2 Mass Transfer Model 1485.3 ANN Modeling 1535.4 Neuro Fuzzy Modeling 1535.5 ANFIS and Gray-box Modeling of Anise Seeds 1545.6 White Box SFE Modeling of Anise 1555.6.1 Gray Box Parameters 1565.6.2 ANFIS 1565.6.2.1 Preprocessing 1575.6.3 Gray Box 1585.7 Results and Discussion 1595.7.1 ANFIS 1595.7.2 Gray Box Modeling Results 1595.7.2.1 Black Box 1595.7.3 Comparison of ANFIS and Gray Box Models with ANN and White Box Models 1615.8 Introduction - Statistical versus ANN Modeling 1625.9 Supercritical Carbon Dioxide Extraction of Q. infectoria Oil 1645.9.1 Materials and Methods 1655.9.2 Experimental Design 1655.9.3 Artificial Neural Network Modeling 1685.10 Subcritical Ethanol Extraction of Java Tea Oil 1685.10.1 Artificial Neural Network Modeling 1725.11 SFE of Oil from Passion Fruit Seed 1735.11.1 Experimental Procedures 1735.11.2 RSM Statistical Modeling 1745.11.3 ANN Modeling of Passion Fruit Seed Oil Extraction with Supercritical Carbon Dioxide 1766 Experimental Design Concept and Notes on Sample Preparation and SFE Experiments 1796.1 Introduction 1796.2 Experimental Design 1796.3 Statistical Optimization 1806.4 Optimization of Palm Oil Subcritical R134a Extraction 1826.4.1 Effect of Temperature and Pressure 1846.4.2 Model Fitting 1876.4.3 Process Optimization 1896.5 Comparison of Subcritical R134a and Supercritical CO2 Extraction of Palm Oil 1906.5.1 Extraction Performance 1916.5.2 Economic Factor 1966.6 Sample Pretreatment 1976.6.1 Moisture Content Reduction 1986.6.2 Sample Size Reduction 1996.7 New Trends in Pretreatment 2006.8 Optimal Pretreatment 2037 Supercritical and Subcritical Optimization 205Part I: First Principle Optimization 2057.1 Introduction 2057.2 Evaluation of Separation Performance 2057.2.1 Effects of Temperature and Pressure 2067.2.2 Effect of the Number of Stages 2077.2.3 Effect of Solvent-to-Feed Ratio 2087.2.4 Effect of Reflux Ratio 2097.3 Parameter Optimization of Palm Oil Deacidification Process 2107.3.1 Simple Countercurrent Extraction (Without Reflux) 2127.3.2 Countercurrent Extraction with Reflux 2137.4 Proposed Flowsheet for Palm Oil Refining Process 2157.5 Conclusions 216Part II: ANN, GA Statistical Optimization 2177.6 Introduction 2177.7 Traditional Optimization 2177.8 Nimbin Extraction Process Optimization 2207.9 Genetic Algorithm for Mass Transfer Correlation Development 2237.10 Optimizing Chamomile Extraction 2257.11 Statistical and ANN Optimization 2277.12 Conclusion 232Appendix A Calculation of the Composition for Palm Oil TG (Lim et al. 2003) 233Appendix B Calculation of Distribution Coefficient and Separation Factor (Lim et al. 2003) 235Appendix C Calculation of Palm Oil Solubility in Supercritical CO2 (Lim et al. 2003) 237References 239Index 265

O autorze

Zainuddin A. Manan is Professor of Chemical Engineering, founding Director of Process Systems Engineering Centre, and founding Dean of Faculty of Chemical and Energy Engineering at Universiti Teknologi Malaysia.Gholamreza Zahedi is former Associate Professor at the Faculty of Chemical and Energy Engineering at the Universiti Teknologi Malaysia. He is Associate Editor of the Research Journal of Applied Sciences, Engineering, and Technology. He is currently working at Leprino Foods.Ana Najwa Mustapa is Senior Lecturer at the School of Chemical Engineering, College of Engineering at Universiti Teknologi Mara (UiTM).