Petri-net-based deductive reasoning strategy for fault identification in batch processes

In implementing any hazard analysis method, there is always a need to reason deductively to identify all possible fault origins that could lead to an undesirable consequence. Because of the complex time-variant cause-and-effect relations between events and states in batch chemical processes, a rigorous risk assessment study is labor- and time-consuming, and its results are often error-prone. The aim of this paper is thus to develop deductive reasoning algorithms on the basis of Petri nets for automating such cause-finding procedures. The effectiveness and correctness of this approach is successfully demonstrated with a number of practical examples.