Data ‐Driven Prescriptive Maintenance toward Fault‐Tolerant Multi‐Parametric Control

AbstractPrescriptive 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 multi-parametric-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 multi-parametric 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.
Source: AIChE Journal - Category: Science Authors: Tags: PROCESS SYSTEMS ENGINEERING Source Type: research