This paper presents an explicit mapping of the conceptual activities that constitute a “process design task” into a series of well-posed, complete, and general formal simulation procedures. Part I of this series of two papers dealt with numerical procedures for process simulation and showed that structure independence and modularity are two prerequisites for a general-purpose simulator. Part II approaches the problem from a completely different point of view and considers the question: Is it possible to derive a general set of design guidelines that can be implemented into a knowledge-based system and result in an automatic, computer-assisted process design procedure? This problem is different in character from that tackled in part I. First, it is by its own nature qualitative, i.e., it requires conceptual rather than numerical answers. Second, it is formulated at a higher level (in Artificial Intelligence terms, at a metalevel). Its solution is clearly in the domain of the logic of process design and, therefore, embeds (contains) all possible quantitative numerical schemes and does not depend on any of them for either its position or its solution. If the answer to this question is affirmative, the resulting code would be a sort of “Expert Assistant” to the engineer in the sense that it would suggest what process can be best suited for the particular application under consideration. The study proceeds by trying to detect conceptual similarities in different design procedures, to construct a suitable knowledge base, and to implement a general macro-procedure that could automatically assist the engineer in the largest possible number of process design operations. The contention here is that the most recent developments of AI-based methods make it possible to extract from human experts all the essential knowledge that pertains to “engineering design,” with the final goal of transferring this body of knowledge—in a form suitable to machine communication—to an “Expert System for Process Design,” which can then be applied (interactively or on a stand-alone basis) with a high degree of confidence to a variety of particular process simulations. A prototype version of an Expert System Assistant is briefly discussed, and an application is analyzed in detail. The code is called COLOMBO and is available as a research tool from the author. Finally, Part II builds on Part I of this series of papers. In particular, it is assumed that a general, modular, numerical Process Simulation Package exists and that it is capable of executing the quantitative mass and energy balance operations described in Part I.

1.
A. Bejan, G. Tsatsaronis, and M. Moran, Thermal Design and Optimization, Wiley, 1995.
2.
E. Charnak, and D. McDemmott, Introduction to Artificial Intelligence, Addison-Wesley, 1985.
3.
M. De Marco, M. F. Falcetta, R. Melli, B. Paoletti, and E. Sciubba, “COLOMBO: an expert system for process design of thermal powerplants,” ASME AES-Vol. 1/10, 1993.
4.
R. B. Evans, “Thermoeconomic isolation and essergy analysis,” Energy, Vol. 5, No. 8–9, 1980.
5.
C. A. Frangopoulos, “Intelligent functional approach: a method for analysis and optimal synthesis-design-operation of complex systems,” IJEEE, Vol. 1, No. 4, 1991.
6.
C. A. Frangopoulos, “Optimal synthesis and operation of thermal systems by the thermoeconomic functional approach,” ASME JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER, Vol. 114, Oct. 1992.
7.
A. S. Kott, J. H. May, and C. C. Hwang, “An autonomous Artificial Designer for thermal energy systems,” ASME JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER, Vol. 111, Oct. 1989.
8.
R. Melli, B. Paoletti, and E. Sciubba, “SYSLAM: an interactive expert system approach to Powerplant design and optimization,” IJEEE, Vol. 2, No. 3, 1992.
9.
B. Olsommer, M. R., Von Spakovsky, and D. Favrat, “An approach for the time-dependent thermoeconomic modeling and optimization of energy system synthesis, design and operation,” Proc. TAIES’97, World Publishing Co., Beijing, China, 1997.
10.
B. Paoletti, and E. Sciubba, “Artificial Intelligence in Thermal Systems Design: concepts and applications,” in: Developments in the Design of Thermal Systems, R. Boehm ed., Cambridge Univ. Press, 1997.
11.
P. Y. Papalambros, and D. J. Wilde, Principles of optimal design: Modeling and Computation, Cambridge University Press, 1988.
12.
W. C. Press, Numerical Recipes, McGraw-Hill, 1985.
13.
G. V. Reklaitis, A. Ravindran, and K. M. Ragsdell, Engineering Optimization, Wiley, 1983.
14.
E. Rich, Artificial Intelligence, McGraw-Hill, 1983.
15.
M. R. Von Spakovsky and R. B. Evans, “Engineering functional analysis,” ASME Journal of Energy Resources Technology, Vol. 115, No. 2, 1993.
16.
L. E. Widman, K. A. Loparo, and N. R. Nielsen, Artificial Intelligence, Simulation and Modeling, Wiley, 1989.
This content is only available via PDF.
You do not currently have access to this content.