Machine Learning Tools for Chemical Engineering: Methodologies and Applications - Brossura

Lpez-flores, Francisco Javier; Ochoa-barragn, Rogelio; Raya-tapia, Alma Yunuen; Ramrez-mrquez, Csar; Ponce-ortega, Jos Maria

Brossura

ISBN 10: 044329058X ISBN 13: 9780443290589

Casa editrice: Elsevier Science Ltd, 2025

Riassunto

Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.
ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modeling and optimization techniques. This book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management.
Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.

Su questo libro

Informazioni sugli autori

Francisco Javier López-Flores received his Master’s and Ph.D. degrees from the Chemical Engineering Department at the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2020 and 2024, respectively. His research interests include process optimization, energy integration, planning strategies, and machine learning. He has published more than ten scientific papers and presented his research at ten international and regional conferences.

Rogelio Ochoa-Barragán earned his Ph.D. and Master’s degrees in Chemical Engineering from the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2020 and 2024, respectively. His current research focuses on process optimization, energy network management, social justice, and machine learning. His work has been presented at eleven international and regional conferences. He has published more than ten scientific articles and contributed to two books.

Alma Yunuen Raya-Tapia is currently pursuing a Ph.D. in Chemical Engineering at the Universidad Michoacana de San Nicolás de Hidalgo. She earned her Master of Science in Chemical Engineering in 2021 with honors, following a degree in Chemical Engineering from the Technological Institute of Lázaro Cárdenas in 2019. Her research focuses on materials synthesis, photocatalysis, dye degradation, wastewater treatment, and the strategic planning of the water-energy-food nexus, combined with machine learning techniques. She has published more than ten scientific articles and presented her work at five international and national conferences.

César Ramírez-Márquez is a Postdoctoral Fellow at the Chemical Engineering Department of the Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He earned his Ph.D. from the University of Guanajuato, Mexico, in 2020. His current research focuses on the production of materials for the solar energy industry and base chemicals in the chemical industry. He has published more than 55 journal papers, six book chapters, presented his work at over fifteen international and regional conferences, and holds four patents.

José María Ponce-Ortega is a Professor in the Chemical Engineering Department at the Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He earned his Ph.D. and Master’s degrees in Chemical Engineering from the Institute of Technology of Celaya, Mexico, in 2009 and 2003, respectively. He completed postdoctoral research at Texas A&M University, USA, and served as a visiting scholar at Carnegie Mellon University, USA. Dr. Ponce-Ortega is a full professor and a member of the National Research System of Mexico. His research focuses on the optimization of chemical processes, sustainable design, energy, mass, water, and property integration, and supply chain optimization. He has published more than 310 papers, five books, and 60 book chapters. He has supervised 30 Ph.D. students and 50 Master’s students and secured funding for 15 research projects totaling approximately $1,000,000. Dr. Ponce-Ortega serves on the editorial boards of Clean Technologies and Environmental Policy and Process Integration and Optimization for Sustainability, and is a subject editor for Sustainable Production and Consumption, as well as associate editor in Frontiers in Chemical Engineering

Dalla quarta di copertina

Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.
ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modeling and optimization techniques. This book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management.
Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.

Dettagli dell'edizione

• Editore: Elsevier Science Ltd
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 044329058X
• ISBN 13: 9780443290589
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 352
• Contatto del produttore: ELSEVIER; https://www.elsevier.com/es-es; ; Mooswaldallee 6; Freiburg; 79108; Germania