Symbolic Simulation of Chemical Production Processes

Winkelbauer, L. (1987). Symbolic Simulation of Chemical Production Processes. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-87-045

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Abstract

Applied systems analysis is -- or should be -- a tool in the hands of planners and decision makers who have to deal with the complex and growing problems of modern society. There is, however, an obvious gap between the ever-increasing complexity and volume of scientific and technological information and tools of analysis relevant to large socio-technical and environmental systems, and the information requirements at a strategic planning and policy level.

The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology, and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way.

As an example of this approach to information and decision support systems, a major component of an R&D project sponsored by the CEC's EURATOM Joint Research Centre (JRC) at Ispra, Italy, in the area of hazardous substances and industrial risk management, is described in this paper. It focuses on the symbolic simulation of chemical production processes which provides an easy-to-understand explanatory system. It shows -- by tracing the intermediate substances of the production process -- how certain chemical production processes work. It is interconnected with several other simulation or optimization modules and databases of the larger information and DSS, and provides information about the feedstocks required, the interim products, the final products and the wastes of the chemical production process simulated.

By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, we attempt to make models of complex processes understandable and available to non-technical users. This is a step toward the increased direct practical usability of IIASA's research results.

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