Francisco Javier López-Flores; Rogelio Ochoa-Barragán; Alma Yunuen Raya-Tapia; César Ramírez-Márquez; José Mar Ponce-Ortega Elsevier Science (2025) Pehmeäkantinen kirja
Fernanda Alicia Dávila Parra; Francisco Javier Almendariz Tapia; María Concepción de la Cruz Leyva Editorial Academica Espanola (2020) Pehmeäkantinen kirja
Elsevier Science Sivumäärä: 352 sivua Asu: Pehmeäkantinen kirja Julkaisuvuosi: 2025, 01.05.2025 (lisätietoa) Kieli: Englanti
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 modelling and optimization techniques. The 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 modelling, 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.
The text highlights the importance of correctly applying machine learning tools in data collection, model development, training, testing, and implementing decision support systems
