In 2016, we introduced the concept of model‐predictive safety (MPS; Ahooyi et al, AIChE J. 2016; 62:2024‐2042). MPS is a proposed innovation in functional safety systems to methodically account for process nonlinearities and variable interactions to enable predictive, prescriptive actions, while existing functional safety systems generally react when individual process variables exceed thresholds. MPS systematically utilizes a dynamic process model to detect imminent and potential future operation hazards in real time and to take optimal preventive and mitigative actions proactively. This work expands the concept of MPS and formulates two min–max optimization problems, offline solutions of which are the optimal proactive preventive and mitigating actions that MPS takes online, in response to predicted process operation hazards. A nested particle‐swarm optimization (PSO) algorithm is proposed to solve the min–max optimization problems. The application and performance of the min–max optimization formulations, the PSO algorithm, and MPS, applied to two chemical process examples, are shown through numerical simulations.

中文翻译：

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对模型进行预测的安全性最佳措施，以检测和处理过程操作中的危险

2016年，我们引入了模型预测安全性的概念（MPS; Ahooyi等人，AIChE J。2016; 62：2024-2042）。MPS是功能安全系统中的一项创新提议，旨在系统地考虑过程非线性和变量交互作用，以实现预测性，规定性措施，而现有功能安全系统通常会在单个过程变量超过阈值时做出反应。MPS系统地利用动态过程模型来实时检测即将发生的和潜在的未来操作危险，并主动采取最佳的预防和缓解措施。这项工作扩展了MPS的概念，并提出了两个最小-最大优化问题，其中的离线解决方案是MPS对预期的过程操作危害做出在线响应的最佳主动预防和缓解措施。提出了一种嵌套粒子群优化（PSO）算法来解决最小-最大优化问题。