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Model-predictive safety optimal actions to detect and handle process operation hazards
Journal article

Model-predictive safety optimal actions to detect and handle process operation hazards

Masoud Soroush, Leila Samandari Masooleh, Warren D. Seider, Ulku Oktem and Jeffrey E. Arbogast
AIChE journal, v 66(6)
01 Jun 2020
DOI: https://doi.org/10.1002/aic.16932

Abstract

Engineering Engineering, Chemical Science & Technology Technology
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.

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9 citations in Web of Science
10 citations in Scopus

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Collaboration types
Industry collaboration
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Chemical
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