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An integrated approach for real-time hazard mitigation in complex industrial processes

机译：复杂工业过程中实时危害缓解的综合方法

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Modern engineering systems give paramount importance to safety in order to avoid or mitigate hazardous accidents which can lead to huge economic losses, environmental contamination, and human injuries. This paper proposes an integrated approach that uses both Hidden Markov Model and Bayesian Network to estimate an optimum safety-threshold for complex industrial processes. In order to estimate the safety threshold, the proposed approach considers different cost factors and the joint probabilities of multiple process variables leading to an accident. In addition to the system level threshold, it also estimates the safety-threshold for components. This helps in identifying the component that needs maintenance to enhance system performance and safety. Furthermore, it proposes a dynamic risk assessment methodology based on multiple real-time process variables. The optimum safety-thresholds are estimated using Genetic Algorithm which aims at minimizing the system running cost over a finite time horizon. A case study on Tennessee Eastman Chemical Process is presented to demonstrate the proposed methodology for optimizing process safety-threshold.
机译：现代工程系统至关重要，以避免或减轻可能导致巨大经济损失，环境污染和人类伤害的危险事故。本文提出了一种综合方法，它使用隐马尔可夫模型和贝叶斯网络来估计复杂工业过程的最佳安全阈值。为了估计安全阈值，所提出的方法考虑了不同的成本因素和多个过程变量的联合概率导致事故。除系统级别阈值外，它还估计组件的安全阈值。这有助于识别需要维护以提高系统性能和安全性的组件。此外，它提出了一种基于多个实时过程变量的动态风险评估方法。使用遗传算法估计最佳安全阈值，该算法旨在使系统运行成本最小化在有限时间范围内。提出了对田纳西州伊斯坦德化学过程的案例研究，以证明了优化过程安全阈值的提出方法。

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