Artificial Intelligence for Biomass-based Biofuel Production : Current Status and Prospects

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Artificial Intelligence for Biomass-based Biofuel Production : Current Status and Prospects

Kuila, Arindam (EDT)/ Shastri, Anshuman (EDT)

ウェブストア価格 ¥39,029（本体¥35,481）
CRC Press（2025/12発売）
外貨定価 US$ 180.00
ポイント 354pt

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製本 Hardcover:ハードカバー版／ページ数 332 p.
言語 ENG
商品コード 9781032916354

Full Description

This book covers all current technological, industrial status, and future prospects of biofuel production with the concept of artificial intelligence including environmental and socioeconomic impact assessment of biofuel production from lignocellulosic biomass. It discusses the status and future scope of artificial intelligence for the advancement of biofuel research. It summarizes machine learning models in addressing the issues of biofuel supply chains, case studies, scientific challenges, and future directions.

Features:

Covers the use of machine learning within the context of the processing of advanced biofuel feedstocks for biofuel production.
Includes larger alcohols, ethers, levulinates, GTL fuels, and furans production using machine learning approach.
Discusses how machine learning and biomass-based biofuel production can be integrated.
Reviews sustainability and cost analysis of artificial intelligence-based biofuel production.
Explores prediction of the potentiality of lignocellulosic biomass for biofuel applications.

This book is aimed at researchers and graduate students in energy and fuels, chemical engineering, and machine learning.

Chapter 1. A overview of lignocellulosic biomass. Chapter 2. Lignocellulosic biomass-based biofuel production. Chapter 3. Present Status and Future Scope of Lignocellulosic Biomass-based Biofuel Production. Chapter 4. An Overview of Artificial Intelligence and Machine Learning. Chapter 5. Artificial intelligence and its application in biofuel production. Chapter 6. Machine learning and its application in biodiesel production. Chapter 7. Machine learning and its application in bioethanol production. Chapter 8. Artificial Intelligence in enhancement of bioethanol production from lignocellulosic biomass. Chapter 9. Current status of artificial intelligence-based biofuel research. Chapter 10. Life Cycle Assessment and Cost Analysis of Artificial Intelligence-Based Biofuel Production. Chapter 11. Artificial intelligence based microalgal biofuel production: Future prospect, limitation, and challenges. Chapter 12. Artificial Intelligence and Machine Learning in Biofuels as Tools for Advancing Efficiency and Sustainability. Chapter 13. AI-Driven Optimization Strategies for Enhanced Biobutanol Production. Chapter 14. Harnessing the potential of Microbial Electrochemical Systems with AI and ML