Artificial Intelligence in Biomass Conversion and Utilization - Hardcover

Über die Autorin bzw. den Autor

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).

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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.