Machine Learning and Clustering for a Sustainable Future
Description
This book explores cutting-edge machine learning and clustering techniques to tackle critical challenges in engineering, environmental science, and sustainability. The book provides an in-depth examination of clustering methodologies, covering unsupervised and supervised techniques, data preprocessing, distance metrics, and cluster validation methods such as the elbow and silhouette techniques.
Readers will find practical insights into applying these methods to real-world problems, including clustering greenhouse gas emissions, optimizing energy systems, and analyzing the energy-food nexus in the context of global crises. By integrating theoretical foundations with hands-on applications, this book serves as a valuable resource for researchers, engineers, and professionals seeking data-driven solutions for sustainability challenges.
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Persons
Alma Yunuen Raya-Tapia, PhD student in chemical engineering at the Universidad Michoacana de San Nicolás de Hidalgo. She obtained in 2021 the title of Master of Science in chemical engineering with honors. She has worked in the area of materials synthesis, photocatalysis, dye degradation and wastewater treatment. She is currently working on the strategic planning of the water, energy and food nexus, the application of machine learning techniques and the simulation and optimization of energy systems. She has published 10 scientific articles and 7 presentations at international and national conferences.
Francisco Javier López-Flores, He 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 2021 and 2025, respectively. His research interests focus on machine learning, process optimization, energy integration, and planning strategies. To date, he has published 15 scientific articles, contributed to 2 book chapters, and presented his research at 20 national and international conferences.
César Ramírez-Márquez , Postdoctoral Fellow at the Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He received his PhD degree from the University of Guanajuato (Mexico) in 2020. Currently published contributions focus on the production of materials for the solar energy industry and base chemicals in the chemical industry. He has published more than 60 journal papers, and 10 book chapters, has presented his work at more than 18 international/regional conferences, and has registered 4 patents.
José María Ponce-Ortega, Professor at the Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He obtained his Ph.D. and Master degrees in Chemical Engineering from the Institute of Technology of Celaya in Mexico in 2009 and 2003, respectively. He stayed as a postdoctoral researcher at Texas A&M University, USA, and as a visiting scholar at Carnegie Mellon University, USA. Dr. Ponce-Ortega is a full professor at the Universidad Michoacana de San Nicolás de Hidalgo, and he is a member of the National Research System of Mexico. The research interest of Dr. Ponce-Ortega is in the areas of optimization of chemical processes, sustainable design, energy, mass, water and property integration, and supply chain optimization. Dr. Ponce-Ortega has published over 320 papers, 7 books, and 50 book chapters. He also has supervised 30 Ph.D. and 45 master's students. He also has 15 funded research projects for about $US 1,000,000.00. Dr. Ponce-Ortega is a member of the editorial board of the journals Clean Technologies and Environmental Policy, Process Integration and Optimization for Sustainability, and Industrial and Engineering Chemistry Research. He is also a subject editor in the journal Sustainable Production and Consumption.
Content
Chapter 1. Artificial Intelligence, Machine Learning, and Clustering in Sustainability.- Chapter 2. Fundamentals of Clustering: Methods, Metrics, and Optimization.- Chapter 3. Programming for Clustering: Python, R, MATLAB, and Anaconda Libraries.- Chapter 4. Clustering Applications in Process Systems Engineering.- Chapter 5. Greenhouse Gas Emissions Clustering for Net Zero Goals.- Chapter 7. The Energy-Food Nexus: Geopolitical and Health Crises Analysis.- Chapter 8: Clustering Urban Zones: A Study of Gentrification.- Chapter 10: Spatiotemporal Clustering of Dam Filling Patterns.