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| INTELLIGENT SYSTEMS IN PROCESS ENGINEERING, PART II: PARADIGMS FROM PROCESS OPERATIONS, 22
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To order this title, and for more information, click here
John Anderson, Carnegie Mellon University
George Stephanopoulos, Massachusetts Institute of Technology, Cambridge, U.S.A.
Chonghun Han, Massachusetts Institute of Technology
Included in series
Advances in Chemical Engineering,
Description
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.
Audience
Academic and industrial researchers in chemical engineering.
Contents
C. Han, R. Lakshmanan, B.R. Bakshi, and G. Stephanopoulos, Nonmonotonic Reasoning: The Synthesis of Operating Procedures in Chemical
Plants. P.M. Saraiva, Inductive and Analogic Learning: Data-Driven Improvement ofProcess Operations. A. Koulouris, B.R.
Bakshi, and G. Stephanopoulos, Empirical Learning through Neural Networks: The Wave-Net Solution. B.R. Bakshi and G. Stephanopoulos,
Reasoning in Time: Modeling, Analysis, and Pattern Recognition of Temporal Process Trends. M.J. Realff, Intelligence in Numerical
Computing: Improving Batch Scheduling Algorithms through Explanation-Based Learning. References. Subject Index for Volumes 21 &22. Contents
of Volumes in this Serial.
| Bibliographic details |
Hardbound, 314 pages, publication date: OCT-1995
ISBN-13: 978-0-12-008522-4
ISBN-10: 0-12-008522-4
Imprint: ACADEMIC PRESS
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| Price and Ordering |
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Last update: 3 Oct 2009
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