Intelligent Systems in Process Engineering Part II: Paradigms from Process Operations

✍ Scribed by George Stephanopoulos and Chonghun Han (Eds.)

• Publisher: Academic Press
• Year: 1995
• Tongue: English
• Leaves: 347
• Series: Advances in Chemical Engineering 22
• Edition: 1
• Category: Library

✦ Synopsis

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 reasoning; inductive and deductive reasoning; searching spaces of discrete solutions; non-monotonic reasoning; analogical learning;empirical learning through neural networks; reasoning in time; and logic in numerical computing. Together the ten paradigms of the two volumes indicate how computers can expand the scope, type, and amount of knowledge that can be articulated and used in solving a broad range of engineering problems. Key Features Sets the foundations for the development of computer-aided tools for solving a number of distinct engineering problems Exposes the reader to a variety of AI techniques in automatic modeling, searching, reasoning, and learning The product of ten-years experience in integrating AI into process engineering Offers expanded and realistic formulations of real-world problems

✦ Table of Contents

Content:
Series Editors
Page ii

Edited by
Page iii

Copyright page
Page iv

Dadication
Page v

Contributors Volume 22
Page xi

Contributors Volume 21
Page xvii

Prologue
Pages xix-xxxi
George Stephanopoulos, Chonghun Han

Nonmonotonic Reasoning: The Synthesis of Operating Procedures in Chemical Plants Original Research Article
Pages 313-376
Chonghun Han, Ramachandran Lakshmanan, Bhavik Bakshi, George Stephanopoulos

Inductive and Analogical Learning: Data-Driven Improvement of Process Operations Original Research Article
Pages 377-435
Pedro M. Saraiva

Empirical Learning Through Neural Networks: The Wave-Net Solution Original Research Article
Pages 437-484
Alexandros Koulouris, Bhavik R. Bakshi, George Stephanopoulos

Reasoning in Time: Modeling, Analysis, and Pattern Recognition of Temporal Process Trends Original Research Article
Pages 485-548
Bhavik R. Bakshi, George Stephanopoulos

Intelligence in Numerical Computing: Improving Batch Scheduling Algorithms Through Explanation-Based Learning Original Research Article
Pages 549-610
Matthew J. Realff

Index
Pages 611-620