Zainuddin A. Manan, Gholamreza Zahedi, Ana Najwa Mustapa

Modeling, Simulation, and Optimization of Supercritical and Subcritical Fluid Extraction Processes (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book provides a complete guide on tools and techniques for modeling of supercritical and subcritical fluid extraction (SSFE) processes and phenomena. It provides details for SSFE from managing the experiments to modeling and optimization. It includes the fundamentals of SSFE as well as the necessary experimental techniques to validate the models. The optimization section includes the use of process simulators, conventional optimization techniques and state-of-the-art genetic algorithm methods. Numerous practical examples and case studies on the application of the modeling and optimization techniques on the SSFE processes are also provided. Detailed thermodynamic modeling with and without co-solvent and non equilibrium system modeling is another feature of the book.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 288
• Erscheinungstermin: 20. September 2021
• Englisch
• ISBN-13: 9781119303190
• Artikelnr.: 62709941

Autorenporträt

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).

Inhaltsangabe

Preface xiii

Nomenclature xvii

1 Fundamentals of Supercritical and Subcritical Fluid Extraction 1

1.1 Introduction 1

1.2 Supercritical Fluid Properties 2

1.3 Subcritical Condition 3

1.4 Physical Properties of Subcritical Fluid 5

1.5 Principles of Sub- and Supercritical Extraction Process 7

1.5.1 Solid Sample Extraction 8

1.5.2 Liquid Sample Extraction 9

1.6 Applications of SCF Extraction 11

1.6.1 Decaffeination of Coffee and Tea 11

1.6.2 Removal of FFA in Fats and Oils 15

1.6.3 Enrichment of Tocopherols 17

1.6.4 Carotenes from Crude Palm Oil and from Palm Fatty Acid Esters 18

1.7 Solubility of Solutes in SCFs 18

1.8 Solute-Solvent Compatibility 20

1.9 Solubility and Selectivity of Low-Volatility Organic Compounds in SCFs 21

1.10 Method of Solubility Measurement 24

1.10.1 Static Method 24

1.10.2 Dynamic Method 25

1.11 Determination of Solvent 27

1.11.1 Carbon Dioxide (CO2) 30

1.11.2 1,1,1,2-Tetrafluoroethane (R134a) as a Solvent 31

1.12 Important Parameters Affecting Supercritical Extraction Process 36

1.12.1 Pressure and Temperature 36

1.12.2 Solvent Flowrate 38

1.12.3 Cosolvent 39

1.12.4 Moisture Content 40

1.12.5 Raw Material 42

1.13 Profile of Extraction Curves 43

1.14 Design and Scale Up 45

2 Modeling and Optimization Concept 47

2.1 SFE Modeling 47

2.1.1 Importance of Knowing the Solid Matrix and Selecting a Suitable Model 48

2.1.2 Different Modeling Approaches in SFE 48

2.1.2.1 Experimental Models 49

2.1.2.2 Models Which Are Based on Similarity between Heat and Mass Transfer 49

2.1.2.3 Models Based on Conservation Balance Equations 49

2.2 First Principle Modeling 49

2.2.1 The Equation of Continuity 50

2.2.2 The Equation of Motion in Terms of tau 50

2.2.3 The Equation of Energy in Terms of q 52

2.3 Hybrid Modeling or Gray Box 53

2.4 ANN 55

2.4.1 Simple Neural Network Structure 55

2.4.1.1 Transfer Function 57

2.4.1.2 Activation Functions 57

2.4.1.3 Learning Rules 57

2.4.2 Network Architecture 58

2.5 Fuzzy Logic 61

2.5.1 Boolean Logic and Fuzzy Logic 61

2.5.2 Fuzzy Sets 62

2.5.3 Membership Function 63

2.5.3.1 Membership Function Types 63

2.5.4 Fuzzy Rules 64

2.5.4.1 Classical Rules and Fuzzy Rules 65

2.5.5 Fuzzy Expert System and Fuzzy Inference 66

2.5.5.1 Mamdani FIS 66

2.5.5.1.1 Fuzzification 66

2.5.5.1.2 Fuzzy Logical Operation and Rule Evaluation 66

2.5.5.1.3 Implication Method 67

2.5.5.1.4 Aggregation of the Rule Outputs 67

2.5.5.1.5 Defuzzification 67

2.5.5.2 Sugeno Fuzzy Inference 67

2.6 Neuro Fuzzy 68

2.6.1 Structure of a Neuro Fuzzy System 69

2.6.2 Adaptive Neuro Fuzzy Inference System (ANFIS) 69

2.6.2.1 Learning in the ANFIS Model 71

2.7 Optimization 72

2.7.1 Traditional Optimization Methods 73

2.7.2 Evolutionary Algorithm 74

2.7.3 Simulated Annealing Algorithm 74

2.7.4 Genetic Algorithm 75

2.7.4.1 Genetic Algorithm Definitions 75

2.7.4.2 Genetic Algorithms Overview 76

2.7.4.3 Preliminary Considerations 77

2.7.4.4 Overview of Genetic Programming 78

2.7.4.5 Implementation Details 79

2.7.4.5.1 Selection Operat