ARTIFICIAL INTELLIGENCE IN PROCESS PLANT SAFETY

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The major goal of this proposal is to research and demonstrate new approaches based on Artificial Intelligence (AI) towards the design of chemical process hazard detection, prevention, and control systems. Such systems are extremely important for improving the occupational safety of chemical plants owing to the complexity of modern process plants. Industrial statistics show that even though major catastrophies and disasters from chemical plant failures are infrequent, minor accidents are very common, occurring on a day to day basis, resulting in many occupational injuries and illnesses, costing the society billions of dollars every year. The proposed project is aimed at the prevention and control of such frequent, day to day, accidental events in the industry. Past approaches in fault diagnostic systems did not properly include the human expert's reasoning strategies and experience and hence were not adequate in efficient and correct trouble- shooting. We propose to improve this status by designing systems that would have better modeling of the problem-solving process through an appropriate representation of the domain knowledge through the use of causal modeling and reasoning from first principles, similar to human experts, by exploiting some of the recent advances in Artificial Intelligence. We propose a methodology that aids the development of expert systems which are process-independent, transparent in their reasoning, resilient under unforeseen fault combinations, and capable of diagnosing a wide diversity of faults. The domain knowledge of the system is based on a library of fault and causal models of process equipments as well as on the physical interconnections between equipment units and causal relationships among process state variables. The inference strategy uses model-based reasoning for analyzing the plant behavior. We describe a prototype expert system, called MODEX, based on our methodology. The system has performed successfully on test cases of prototypical chemical process plants and looks promising. However, before a successful transfer of this methodology to the industry can be initiated a number of research issues outlined in the proposal need to be resolved by experimenting with larger prototypical chemical plants.