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Machine learning applications for bioenergy conversion

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning applications for bioenergy conversionby Nakorn Tippayawong, Thossaporn Onsree, James Moran.
Author:	Tippayawong, Nakorn.
other author:	Onsree, Thossaporn.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xxiv, 187 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Biomass energy.
Online resource:	https://doi.org/10.1007/978-981-96-8086-3
ISBN:	9789819680863$q(electronic bk.)

Machine learning applications for bioenergy conversion
Tippayawong, Nakorn.

Machine learning applications for bioenergy conversion[electronic resource] /by Nakorn Tippayawong, Thossaporn Onsree, James Moran. - Singapore :Springer Nature Singapore :2025. - xxiv, 187 p. :ill. (chiefly col.), digital ;24 cm. - Green energy and technology,1865-3537. - Green energy and technology..

1. Introduction -- 2. Machine Learning Algorithms for Bioenergy -- 3. Pre-treatment with Ionic Liquids -- 4. Torrefaction -- 5. Co-Pyrolysis -- 6. Biodiesel Production -- 7. Anaerobic Digestion -- 8. Hydrothermal carbonization -- 9. Hydrothermal Liquids.

This book explores the integration of machine learning algorithms with bioenergy conversion processes, providing an innovative approach to tackling the challenges of sustainable energy production. The authors have written dozens of peer reviewed papers on this topic over the past 8 years and this book is a culmination of their efforts. It delves into various machine learning techniques tailored to optimize and enhance the efficiency of bioenergy processes, making it a critical resource for researchers, engineers, and students in the fields of renewable energy and machine learning.

ISBN: 9789819680863$q(electronic bk.)

Standard No.: 10.1007/978-981-96-8086-3doiSubjects--Topical Terms:

276913
Biomass energy.

LC Class. No.: TP339

Dewey Class. No.: 662.88

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520 $a This book explores the integration of machine learning algorithms with bioenergy conversion processes, providing an innovative approach to tackling the challenges of sustainable energy production. The authors have written dozens of peer reviewed papers on this topic over the past 8 years and this book is a culmination of their efforts. It delves into various machine learning techniques tailored to optimize and enhance the efficiency of bioenergy processes, making it a critical resource for researchers, engineers, and students in the fields of renewable energy and machine learning.
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