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Intelligent systems in process engineering
1 online resource (xxxi, 313-625 pages) : illustrations
Volumes 21 and 22 of Advances in Chemical Engineering contain ten prototypical paradigms which integrate ideas and methodologies from artificial intelligence with those from operations research, estimation andcontrol theory, and statistics. Each paradigm has been constructed around an engineering problem, e.g. product design, process design, process operations monitoring, planning, scheduling, or control. Along with the engineering problem, each paradigm advances a specific methodological theme from AI, such as: modeling languages; automation in design; symbolic and quantitative reason.
Front Cover; Advcances in Chemical Engineering, Volume 22; Copyright Page; Contents; Contributors to Volume 22; Prologue; Chapter 1. Nonmonotonic Reasoning: The Synthesis of Operating Procedures in Chemical Plants; I. Introduction; II. HierarchicalModeling of Processes and Operations; III. Nonmonotonic Planning; IV. Illustrations of Modeling and Nonmonotonic Operations Planning; V. Revamping Process Designs to Ensure Feasibility of Operating Procedures; VI. Summary and Conclusions; References; Chapter 2. Inductive and Analogic Learning: Data-Driven Improvement of Process Operations.
I. IntroductionII. General Problem Statement and Scope of the Learning Task; III. A Generic Framework to Describe Learning Procedures; IV. Learning with Categorical Performance Metrics; V. Continuous Performance Metrics; VI. Systems with Multiple Operational Objectives; VII. Complex Systems with Internal Structure; VIII. Summary and Conclusions; References; Chapter 3. Empirical Learning through Neural Networks: The Wave-Net Solution; I. Introduction; II. Formulation of the Functional Estimation Problem; III. Solution to the Functional Estimation Problem.
IV. Applications of the Learning AlgorithmV. Conclusions; VI. Appendices; References; Chapter 4. Reasoning in Time: Modeling, Analysis, and Pattern Recognition of Temporal Process Trends; I. Introduction; II. Formal Representation of Process Trends; III. Wavelet Decomposition: Extraction of Trends at Multiple Scales; IV. Compression of Process Data through Feature Extraction and Functional Approximation; V. Recognition of Temporal Patterns for Diagnosis and Control; VI. Summary and Conclusions; References.
Chapter 5. Intelligence in Numerical Computing: Improving Batch Scheduling Algorithms through Explanation-Based LearningI. Introduction; II. Formal Description of Branch-and-Bound Framework; III. The Use of Problem-Solving Experience in Synthesizing New Control Knowledge; IV. Representation; V. Learning; VI. Conclusions; References; Index; Contents of Volume in This Serial.
edited by George Stephanopoulos, Chonghun Han.
Includes bibliographical references and index.
Intelligent systems in process engineering. Part II, Paradigms from process operations