Artificial Intelligence in Process Engineering by Michael L. Mavrovouniotis

By Michael L. Mavrovouniotis

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And Brown, J. S. A Qualitative Physics Based on Confluences. Al Journal, 24, p. 7 (1984). , and Georgakis, C. Batch Reactor Optimization by U s e of Tendency Models. Computers and Chemical Engineer­ ing, 13 ( 1 / 2 ) , pp. 3 5 - 4 8 (1986). Filippi-Bosey, C , Greife, J. , Barnay, J. L . , and Georgakis, C. Tendency Modeling of Semibatch Reactors for Optimization and Control. Computers and Chemical Engineering, (41) (4), pp. 9 1 3 - 9 2 0 (1989). Forbus, K. Qualitative Process Theory. Al Journal, 24 p.

Dalle Molle and T. F. Edgar systems with incomplete knowledge. While traditional methods of analysis require a complete description of the process and generate a unique prediction, qualitative simulation produces all possible behaviors of a system from qualitative descriptions of the physical system. In addition, quantitative information can be included in qualitative simulation to re­ duce the number of possible behaviors and to provide a more descriptive prediction of the behavior of the system.

Since the discrete 46 Jane Τ. Malin, Bryan D. Basham and Richard A. Harris event simulation mechanism takes the place of constraint propagation, qualitative models must incorporate time and directed causality. To develop this new discrete event simulation approach, a number of new concepts and methods were developed. These concepts and methods include a definition of a component model, a definition of the types of links connecting components (relations and variable clusters), new state transi­ tion structures (processes), methods for representing qualitative and quan­ titative functions (process language), and a new simulation control approach.

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