Sadasivuni / Bacanin / Kim Harnessing Automation and Machine Learning for Resource Recovery and Value Creation

From Waste to Value
1. Auflage 2025
ISBN: 978-0-443-27375-9
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 500 Seiten

Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future. - Provides insights into the potential of automation and machine learning in waste management inspiring readers to adopt sustainable waste management practices - Offers a comprehensive understanding of how waste management can be transformed into a profitable business by adopting innovative and sustainable solutions - Offers an opportunity to explore case studies from different industries and regions to showcase the revolutionary applications of automation and machine learning in waste management - Provides guidance for waste management professionals, policymakers, and business leaders to optimize waste management processes and improve their bottom line

Autoren/Hrsg.

Sadasivuni, Kishor Kumar

Bacanin, Nebojsa

Kim, Jaehwan

Vashisht, Neha B