Data-driven optimization of processes with degrading equipment

TL;DR

This paper presents a data-driven, robust optimization framework for balancing maintenance costs and equipment availability in chemical processes with degrading equipment, integrating sensor data and stochastic models.

Contribution

It introduces a novel integrated optimization approach combining condition-based maintenance models with process-level mixed-integer optimization using robust and Bayesian optimization techniques.

Findings

Efficiently balances equipment availability and maintenance costs.

Reduces computational expense with analytical and data-based approximations.

Demonstrates effectiveness on multiple State-Task-Network instances.

Abstract

In chemical and manufacturing processes, unit failures due to equipment degradation can lead to process downtime and significant costs. In this context, finding an optimal maintenance strategy to ensure good unit health while avoiding excessive expensive maintenance activities is highly relevant. We propose a practical approach for the integrated optimization of production and maintenance capable of incorporating uncertain sensor data regarding equipment degradation. To this end, we integrate data-driven stochastic degradation models from Condition-based Maintenance into a process level mixed-integer optimization problem using Robust Optimization. We reduce computational expense by utilizing both analytical and data-based approximations and optimize the Robust optimization parameters using Bayesian Optimization. We apply our framework to five instances of the State-Task-Network and demonstrate that it can efficiently compromise between equipment availability and cost of maintenance.