Maintenance can improve the availability of aging production systems and prevent process safety incidents. However, because of system complexity, resource allocation is nontrivial. This research developed and applied a framework to obtain optimal future-failure aware and safety-conscious production and maintenance schedules. Ensembles of nonlinear support vector machine classification models were leveraged to predict the time and probability of future equipment failure from equipment condition data. Multiobjective optimization of expected profit and a safety metric was then used to determine optimal process and maintenance schedules. The results of this research were that the ensemble models had an average accuracy and an F1-score of 0.987, that the ensemble models were more accurate and sensitive than the individual classifiers by 3 percentage points, and that the Pareto-optimal process and maintenance schedules were obtained, providing alternative solutions to the decision maker. This research described optimal resource allocation to help improve safety and system effectiveness.

Prescriptive maintenance can improve system effectiveness and system safety via integrated production and maintenance optimization. However due to system disruptions there is potential for abnormal operations and an undesirable increased occurrence of process safety incidents. This research provides a multiparametric‐based framework for safety‐aware, maintenance‐aware, and disruption‐aware process control. It leverages ensemble classification via machine learning classifiers for fault detection, mixed‐integer nonlinear programming for integrated safety‐aware production and maintenance scheduling, as well as hybrid multiparametric model predictive control for fault‐tolerant setpoint tracking. The results show that the ensemble classifier outperforms the individual classifiers in terms of fault detection accuracy, sensitivity, and specificity. Furthermore, it is seen that the developed controllers are able to reconfigure the control actions based on process disruption information. The framework is illustrated with a chemical complex system, and a cooling water system. The approach can be used to help improve the safety and productivity of industrial processes.

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