An automaton-based approach to evaluate and improve online diagnosis schemes for multi-failure scenarios in batch chemical processes

Abstract Online diagnosis has been considered as an important measure for improving operational safety in many batch chemical plants. Specifically, the state transition behaviors of all hardware items (components) in the given batch process and their failure mechanisms are modeled systematically with automata. The system model is then assembled by connecting the component models on the basis of a generic hierarchical structure. A “diagnoser” can be constructed accordingly for the purpose of determining various qualitative and quantitative performance indices. Guided by these indices, two performance enhancement approaches can be effectively applied: (1) installing additional sensors which are not included in the piping and instrumentation diagram (P&ID) and (2) executing extra operation steps which are not specified in the sequential function chart (SFC). Three examples are presented in this paper to demonstrate the feasibility of the proposed approach.

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