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Description
The book provides a comprehensive overview of artificial intelligence (AI) applications in different stages of biomass conversion, from fundamental research to practical applications. It explores the role of AI in biomass conversion to create low-carbon materials, fuels, and chemicals. It addresses key research challenges like inadequate thermodynamic databases, unreliable models, and inefficient multi-objective optimization. It helps readers gain a comprehensive understanding of how AI can revolutionize biomass conversion, leading to more sustainable and efficient processes. INTRODUCTION
AI INTEGRATION IN BIOMASS CONVERSION
INTELLIGENT ANALYSIS OF BIOMASS PROPERTIES: FROM TRADITIONAL MODELS TO DATA-DRIVEN NEW PARADIGM
MECHANISM-GUIDED AI MODELING
MULTI-OBJECTIVE OPTIMIZATION IN COMPLEX BIOMASS CONVERSION PROCESSES
AI-ASSISTED POST-PROCESSING FOR BIOMASS PRODUCT VALORIZATION
SUMMARY AND OUTLOOK Jiahua Zhu is a Professor and Vice Dean of Chemical Engineering at Nanjing Tech University, China. Dr. Zhu received his Ph.D. degree of Chemical Engineering from Lamar University in 2013. He joined the Department of Chemical & Biomolecular Engineering at the University of Akron in 2013 as an Assistant Professor and he was early promoted to tenured Associate Professor in 2018. He has authored more than 200 peer-reviewed journal articles and 4 book chapters. He has received Young Leader Development Award from Functional Material Division of The Minerals, Metals & Materials Society (2015), Early Career Award from Polymer Processing Society (2017) and Early Career Investigator Award from ECS Electrodeposition Division (2017).
Contents
List of Figures ix
List of Tables xiii
Preface xv
Acronyms and Abbreviations xvii
1 Introduction 1
Peng Jiang and Jiahua Zhu
1.1 Biomass Valorization Technologies 3
1.1.1 Traditional Conversion Technologies 3
1.1.2 High-value Conversion Technologies 4
1.2 Machine Learning Methods 6
1.3 AI/ML-integrated Biomass Valorization 8
1.3.1 AI-assisted Data Acquisition 10
1.3.2 AI-assisted Process Modeling 12
1.3.3 AI-assisted System Optimization 12
References 14
2 AI Integration in Biomass Conversion 21
Shuangjun Li, Yuanming Li, Yan Xie, and Xiangzhou Yuan
2.1 Introduction 21
2.2 Multidimensional Framework of AI Integration 29
2.2.1 Technical Dimension 29
2.2.2 Process Dimension 32
2.2.3 Decision Dimension 35
2.3 AI Integration in Various Biomass Conversion Processes 37
2.3.1 Thermochemical Approaches 37
2.3.2 Chemical Approaches 50
2.3.3 Biochemical Approaches 56
2.4 Conclusions and Outlooks 63
References 65
3 Intelligent Analysis of Biomass Properties: From Traditional Models to
Data-driven New Paradigm 79
Long Cheng and Yuanhui Ji
3.1 Introduction 79
3.2 Prediction of Biomass Thermodynamic Parameters 80
3.2.1 Basic Thermodynamic Property Parameters 81
3.2.2 Thermochemical Conversion Process Parameters 81
3.2.3 Derived and Correlated Parameters 82
3.3 Estimation of Biomass HHV 82
3.3.1 Empirical Correlations in HHV Estimation 82
3.3.2 ML in HHV Prediction 86
3.4 Prediction of Biomass Standard Entropy 100
3.5 Prediction of Biomass Heat Capacity 101
3.6 Prediction of Biomass Exergy 104
3.7 Prediction of Biomass Activation Energy 107
3.8 Summary and Outlooks 114
References 115
4 Mechanism-guided AI Modeling 121
Peng Jiang, Minjiao Chen, Han Lin, Jiahua Zhu, and Tuo Ji
4.1 Introduction 121
4.2 Mechanism-driven Modeling 122
4.2.1 Pyrolysis Process Modeling 123
4.2.2 Gasification Process Modeling 130
4.2.3 Other Biomass Conversion Modeling 133
4.3 Hybrid Modeling 142
4.3.1 Direct Hybrid Modeling 143
4.3.2 Reduced-order Models 148
4.3.3 Physics-informed Neural Networks 149
4.3.4 Other Hybrid Modeling 151
References 154
5 Multi-objective Optimization in Complex Biomass Conversion
Processes 165
Zezong Chen, Yinghua Fu, Jiafa Chen, Yaohui Zhang, Hui Li, and Yize Li
5.1 Introduction 165
5.1.1 Multi-energy Integration Trends 165
5.1.2 Complexity and Conflicts in Biomass Conversion 166
5.1.3 Advantages of MOO 167
5.1.4 Integrated Energy Systems 168
5.1.5 Structure and Technical Road map 168
5.2 Methodology of MOO 171
5.2.1 Core Concepts 171
5.2.2 Mathematical Modeling Framework 171
5.2.3 Objective Conflict Analysis 172
5.2.4 Comparison of Solution Strategies 173
5.2.5 Post-processing and Decision Support 176
5.2.6 Uncertainty in MOO 177
5.3 Biomass Conversion and System Modeling 178
5.3.1 Kinetic and Heat-mass Models 178
5.3.2 Renewable-hydrogen System Coupling Models 185
5.3.3 Multiscale Modeling 187
5.3.4 Simulation Platforms and Data Interfaces 188
5.3.5 Digital Twin for Online MOO 189
5.4 High-performance Computing for MOO 190
5.4.1 Software Environment 190
5.4.2 Algorithm Performance Assessment 191
5.4.3 Surrogate-assisted MOO 193
5.4.4 High-performance Parallel Computing 194
5.4.5 Engineering-scale Validation with Pilot Data 195
5.5 Application and Evaluation Cases 196
5.5.1 Process-level Optimization Examples 196
5.5.2 System-level Demonstration 199
5.5.3 Life Cycle and Sustainability Assessment 202
5.6 Trends, Challenges, and Deployment 203
5.6.1 High-dimensional and Real-time Optimization Issues 203
5.6.2 Data-model Uncertainty Management 204
5.6.3 Automated Workflows and Continual Learning 205
5.6.4 Digital Twin for Operation and Maintenance 206
5.6.5 Promoting Green Multi-objective Design 206
References 207
6 AI-assisted Post-processing for Biomass Product Valorization 221
Binwang Chen, Qinchen Huang, Huaze Sun, Yongguang Yu, Peng Jiang,
Leonidas Matsakas, and Liwen Mu
6.1 Introduction 221
6.2 AI-assisted Biomass Fractionation and Dissolution 223
6.2.1 Conventional Solvent System 223
6.2.2 Green Solvent System 226
6.2.3 Other Biomass Processing 229
6.3 AI-assisted Cellulose Products 232
6.3.1 Conventional Bulk Products 233
6.3.2 Cellulose-derived Chemicals 235
6.3.3 Cellulose-derived Materials 236
6.4 AI-assisted Lignin Products 240
6.4.1 Lubricating Additives 240
6.4.2 Antioxidants 241
6.4.3 Dispersants and Ethers 243
6.5 AI-assisted Hemicellulose Products 243
6.5.1 Sugars 244
6.5.2 Pharmaceutical Delivery Carriers 244
6.6 Bio-oil and Biochar Products 245
6.6.1 Bio-oil 245
6.6.2 Biochar 246
6.7 Summary and Remarks 247
References 248
7 Summary and Outlook 261
Jiahua Zhu
7.1 Summary 261
7.1.1 Complexities in Biomass Conversion 261
7.1.2 Gaps to Be Filled in Biomass Conversion 262
7.1.3 AI/ML Integration in Biomass Valorization 263
7.1.4 Challenges and Opportunities 265
7.2 Outlook 265
7.2.1 Biomass-specific Data, Databases, and Descriptors 265
7.2.2 ML and Mechanistic Integration for Biomass Process Modeling 266
7.2.3 Data Generation and Simulation Support 266
7.2.4 Co-conversion Strategies and MOO Frameworks 267
7.2.5 AI-driven High-throughput Experimentation 267
7.2.6 System Integration in Biomass Biorefining 267
7.2.7 Application of Large Language Models and Prompt Engineering 268
References 268
Index 271
