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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减轻危害的集成方法

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摘要

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：为了避免或减轻可能导致巨大的经济损失，环境污染和人身伤害的危险事故，现代工程系统对安全至关重要。本文提出了一种使用隐马尔可夫模型和贝叶斯网络的综合方法来估算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以及导致事故的多个过程变量的联合概率。除了系统级别阈值之外，它还估计组件的安全阈值。这有助于确定需要维护以增强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。最佳安全阈值是使用遗传算法估算的，该算法旨在在有限的时间范围内最小化系统运行成本。本文以田纳西州伊士曼化学过程为例，以证明所提出的优化过程安全阈值的方法。

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