Process resilience based upset events prediction analysis: Application to a batch reactor
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2019 Recently, there has been a growing interest in resilience-based systems approaches in order to address the challenges of process safety and risk management. One such approach, namely the Process Resilience Analysis Framework (PRAF), considers the processes as socio-technical systems that are complex in nature and deteriorate as a function of time. This approach considers two factors. On the technical side, aspects such as equipment malfunction and process parameter variations are considered, whilst human and organizational aspects are the main area of focus of the social side. This work presents the application of PRAF to predict process upsets. The large number of factors that may cause a system upset, as well as their complex interactions, is the main challenge of predicting and hedging against them. In this work we (i) use a process model, (ii) consider parameter uncertainty and (iii) employ resilience metrics to predict process upsets or unsafe zones of operation. A Global Sensitivity Analysis (GSA) on the process model parameters identifies the critical parameters that can potentially cause unsafe operation. Data from in silico experiments and Bayesian analysis are used to estimate the range of the aforementioned parameters for which safe operation can be guaranteed. This novel and interdisciplinary integration of high fidelity modeling, GSA and Bayesian analysis experiments for process safety is shown on a batch reactor commonly used in chemical and pharmaceutical industry for specialty products.
