Multimodal Intelligent Perception and Optimization Decision-Making for Process Industries
Applications in Salt Lake Chemical Process
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Beschreibung
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.
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.
Elaborates multimodal perception and optimization decision-making methods for salt lake chemical processes Integrates visual, acoustic, and device modalities to overcome limitations of traditional single-modality modeling Provides a framework for exploring multimodal perception and decision-making in other complex industrial scenarios
Autor*in
Yalin Wang
Themen in »Multimodal Intelligent Perception and Optimization Decision-Making for Process Industries«
Multimodal intelligent perception Optimization decision-making Industrial process Salt lake chemical process Intelligent decision-making in process control Working condition recognition in mineral processes Reinforcement learning for process optimization Multimodal deep learning in chemical industry Data-driven soft sensor modeling Multisource data fusion for industrial analytics