Optimization of Preventive Maintenance in Chemical Process Plants

Abstract

In this article, we use a genetic algorithm to obtain an economically optimal preventive maintenance frequency for different equipment, the parts inventory policy (number and type of spare parts to keep in stock), and labor allocation in process plants. To assess cost, we improved a previously published Monte Carlo simulation-based maintenance model (Nguyen et al. Ind. Eng. Chem. Res. 2008, 47(6), 1910−1924). Two examples, a Tennessee Eastman example and a fluid catalytic cracking unit in a refinery, are provided.

Asset maintenance optimisation approaches in the chemical and process industries – A review

“… Nguyen and Bagajewicz (2010) proposed a Monte-Carlo simulation-based maintenance optimisation model, with the objective to minimise the maintenance costs for a 2 y time horizon of plant operations. The model is an extension of a previous analysis framework ( Nguyen and Bagajewicz, 2008 ), including more features, such as spare parts inventory policies, human labour allocations based on their specific skills and imperfect maintenance model.…”

Towards fault detection and self-healing of chemical processes over wireless sensor networks

“…Modern engineering systems and manufacturing processes are becoming increasingly complex, and operating in highly dynamic environments. In the age of high competition and stringent environmental and safety regulations, the role of maintenance as an effective tool to increase profit margin, improve plant reliability and reduce safety and environmental hazards has become increasingly important (Nguyen & Bagajewicz, 2010). Fault detection and diagnosis are important issues in chemical engineering applications.…”

Integrated condition-based planning of production and utility systems under uncertainty

“…Some representative works on the maintenance planning for production systems are presented below. Nguyen and Bagajewicz (2010) developed preventive maintenance planning models for chemical process plants. Huang and Yu, (2016) studied the maintenance planning problem with the objectives to reduce energy consumption and minimize makespan.…”