NEURAL NETWORKS FOR PROCESS FAULT DIAGNOSIS AND SAFETY

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The major goal of this proposal is to research and demonstrate a new approach based on the emerging technology of Neural Networks in the discipline of 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. 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. In this project, we propose a novel methodology using Neural Networks that address the important issues which are central to the development of knowledge-based systems for process fault diagnosis and hazard control. Our methodology for developing knowledge-based systems rests on the important characteristics of neural networks, namely, their ability to automatically classify and learn associations between input and output data and to be able to handle noisy data. We have already successfully tested our approach on a prototypical case study based on real-plant data, that of fluidized catalytic cracking unit (FCCU) in operation in an Exxon refinery. We plan to investigate this approach further by experimenting with larger prototypes of chemical process plants with more realistic, complex, models for the various units such as reactors, heat exchangers, distillation columns etc. We also plan to explore the robustness of such systems for incomplete and uncertain data.