This book provides a systematic and application-oriented guide to understanding how multimodal industrial data can be transformed into reliable process perception and intelligent operational decisions. Drawing on more than a decade of research and engineering experience, it focuses on salt lake chemical processes as a representative complex industrial system and addresses five key topics: recovery of incomplete process data, visual-based concentrate grade estimation, acoustic-based concentrate yield estimation, multimodal working condition recognition, and intelligent operational optimization under multiple working conditions. The book integrates data-driven modeling, multimodal information fusion, machine learning, and intelligent decision-making methods, with particular emphasis on their practical use in challenging industrial environments. A rich collection of process flowcharts, network architectures, comparative experiments, tables, and visualization results helps readers understand both methodological principles and engineering implementation. By connecting data quality, intelligent perception, state recognition, and operational optimization within a unified framework, the book offers a valuable reference for researchers, engineers, practitioners, graduate students, and advanced undergraduates working in industrial artificial intelligence, process systems engineering, automation, and intelligent manufacturing.

Yalin Wang is currently a Full Professor, Dean of the School of Automation, Central South University. Prof. Yalin Wang received the B.Eng. Degree in Industrial Automation and the Ph.D. in Control Theory and Control Engineering from Central South University, China in 1995 and 2001 respectively. From 2011 to 2012, she was a Senior Visiting Scholar at the University of Alberta, Canada. Her research interests mainly include intelligent modeling and optimization control of complex industrial processes, and smart manufacturing of process industries. She has received funding in more than 20 scientific research projects, including the National Key Research and Development Program of China, major project of the National Natural Science Foundation of China, and key project of the National Natural Science Foundation of China, etc. She has won 2 national science and technology second prizes and 6 provincial and ministerial science and technology first prizes. Her publications include more than 200 papers with 100 articles in international peer reviewed journals, such as IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Industrial Informatics, Journal of Process Control, Control Engineering Practice and so on. She is a Fellow of Chinese Automation Society and a senior member of Institute of Electrical and Electronics Engineers.

Chenliang Liu is currently an Associate Professor at the School of Automation, Central South University. Prof. Chenliang Liu received the Ph.D. degree in control science and engineering from the School of Automation, Central South University, Changsha, China, in 2024. From 2023 to 2024, he was a joint training Ph.D. student with the School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore. His research interests include deep learning and modeling and optimal control of complex industrial process. He is a recipient of the second prize in Natural Sciences from the Chinese Automation Society and the first prize in Artificial Intelligence Science from the Hunan Artificial Intelligence Society. He is a member of Institute of Electrical and Electronics Engineers and Chinese Automation Society.