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. 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

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. 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

This book contains ten prototypical paradigms which integrate ideas and methodologies from artificial intelligence with those from operations research, estimation and control theory, and statistics. Each paradigm hasbeen constructed around an engineering problem, e.g., product design, process design, process operations monitoring, planning, scheduling, or control. Each paradigm also advances a specific methodological theme in AI, such as: Modeling languages Automation in design Symbolic and quantitative reasoning Inductive and deductive reasoning Searching spaces of discrete solutions Nonmonotonic reasoning Analogical learning Empirical learning through neural networks Reasoning in time Logic in numerical computing Together the ten paradigms of the two volumes indicate how computers can expand the scope, type, and amount of knowledge available to be used in solving a broad range of engineering problems. 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 Represents the product of a decade of experience in integrating AI into process engineering Offers expanded and realistic formulations of real-world problems

Tools for Chemical Product Design: From Consumer Products to Biomedicine describes the challenges involved in systematic product design across a variety of industries and provides a comprehensive overview of mathematical tools aimed at the design of chemical products, from molecular design to customer products. Chemical product design has become increasingly important over the past decade and includes a wide range of sectors including gasoline additives and blends in the petroleum industry, active ingredients and excipients in the pharmaceutical industry, and a variety of consumer products and specialty chemicals. Traditionally, such products have been designed through trial and error methods, which not only are time-consuming, but more importantly only provide limited knowledge that can be translated into next generation products. Features an impressive collection of contributions from leading researchers in the field Presents the latest tools available across a variety of industries Describes the challenges involved in systematic product design as well as the latest methods for solving such problems Covers a wide range of sectors including gasoline additives and blends in the petroleum industry, active ingredients and excipients in the pharmaceutical industry, and a variety of consumer products and specialty chemicals

This book constitutes the refereed proceedings of the 19th International Conference on Conceptual Modeling, ER 2000, held in Salt Lake City, Utah, USA in October 2000. The 37 revised full papers presented together with three invited papers and eight industrial abstracts were carefully reviewed and selected from a total of 140 submitted papers. The book offers topical sections on database integration, temporal and active database modeling, database and data warehouse design techniques, analysis patterns and ontologies, Web-based information systems, business process modeling, conceptual modeling and XML, engineering and multimedia application modeling, object-oriented modeling, applying object-oriented technology, quality in conceptual modeling, and application design using UML.

Looks at challenges for the future, including the broader challenge of extending design to include creating new value for a company, and a discussion of how emerging technologies, particularly increased computer speeds, may impact future design.