Advances in Chemical Engineering: Intelligent Systems in Process Engineering - Paradigms from Design and Operations v. 21 & v. 22

Advances in Chemical Engineering: Intelligent Systems in Process Engineering - Paradigms from Design and Operations v. 21 & v. 22

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The ten prototypical paradigms in this volume integrate ideas and methodologies from artificial intelligence with those from operations research, estimation and control theory, and statistics. Each paradigm has been constructed around an engineering problem, eg product design, process design, process operations monitoring, planning, scheduling or control. As well as the engineering problem, each paradigm advances a specific methodological theme from AI, such as: modelling 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.
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Product details

  • Paperback | 625 pages
  • 150 x 224 x 26mm | 621.42g
  • Academic Press Inc
  • San Diego, United States
  • English
  • illustrations, index
  • 0126662401
  • 9780126662405

Table of contents

Modelling languages - declarative and imperative descriptions of chemical reactions and processing systems, C.J. Nagel et al; automation in design - the conceptual synthesis of chemical processing schemes, C. Han et al; symbolic and quantitative reasoning - design of reaction pathways through recursive satisfaction of constraints, M.L. Mavrovouniotis; inductive and deductive reasoning - the case of identifying potential hazards in chemical processes, C.J. Nagel and G. Stephanopoulos; searching spaces of discrete solutions - the design of molecules possessing desired physical properties, K.G. Joback and G. Stephanopoulos; non-monotonic reasoning - the synthesis of operating procedures in chemical plants, C.Han et al; inductive and analogic learning - data-driven improvement of process operations, P.M. Saraiva; empirical learning through neural networks - the wave-net solution, A. Koulouris et al; reasoning in time - modelling, analysis and pattern recognition of temporal process trends, B.R. Bakshi and G. Stephanopoulos; intelligence in numerical computing - improving batch sceduling algorithms through explanation-based learning, M.J. Realff.
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