Apply artificial intelligence (AI) to optimize biomass conversion and utilization processes
Biomass conversion technologies have advanced significantly yet face persistent challenges in industrialization, including inadequate thermodynamic databases, unreliable models, and inefficient multi-objective optimization. Artificial Intelligence in Biomass Conversion and Utilization addresses these barriers by detailing how AI and machine learning methods can predict biomass properties, model conversion processes, and optimize systems for energy output, economics, and environmental performance.
The book covers AI applications across every stage of biomass conversion, from fundamental research through practical deployment. Topics include the production of low-carbon materials, fuels, and chemicals from biomass feedstocks, alongside methods for rapid assessment and smart decision-making. Discussions of carbon neutralization strategies and circular economy frameworks demonstrate how computational intelligence supports both process efficiency and environmental sustainability goals.
Readers will also find:
- Approaches for integrating machine learning with thermochemical and biochemical biomass conversion pathways to improve process prediction accuracy
- Methods for multi-objective optimization balancing energy yield, economic viability, and environmental impact across biomass utilization systems
- Strategies for addressing inadequate thermodynamic databases through AI-driven data augmentation and predictive modeling techniques
- Coverage of AI applications in producing low-carbon materials, sustainable fuels, and platform chemicals from diverse biomass sources
- Frameworks connecting biomass conversion with carbon neutralization goals and circular economy principles for industrial-scale deployment
Designed for process engineers, chemical engineers, materials scientists, biotechnologists, and environmental chemists, this reference provides the computational and domain-specific knowledge needed to apply AI methods across biomass conversion workflows, from property prediction through system-level optimization for sustainable energy and materials production.
Table of 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
About the Author :
Jiahua Zhu, PhD, is a Professor of Chemical Engineering at Nanjing Tech University, China. Previously at the University of Akron, where he earned early promotion to tenured Associate Professor, he has authored more than 200 peer-reviewed journal articles. His awards include the Young Leader Development Award from TMS, the Early Career Award from the Polymer Processing Society, and the Early Career Investigator Award from ECS Electrodeposition Division.
