Maintenance in the downstream petroleum industry: A review on methodology and implementation. (April 2023)

• Record Type: Journal Article
• Authors: Wari, Ezra; Zhu, Weihang; Lim, Gino
• Is Part Of: Computers & chemical engineering. Volume 172(2023)
• Publication Date: 2023-04
• Subjects: Maintenance -- Downstream petroleum industry -- Policy optimization -- Criticality analysis; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2023.108177
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Present a detailed review on manufacturing system maintenance operations in the downstream petroleum industry. Identify the policies, optimization methodologies, implementation frameworks, and other maintenance efficiency enhancement efforts in the industry to provide general guidance for practitioners and researchers. Organize maintenance optimization approaches into two broad categories, i.e., based on the criticality of the equipment and based on tools. Track the recent trends in the maintenance implementation approach, identify gaps, and recommend future research directions to advance the industry's maintenance functions. Abstract: This paper presents a literature review on maintenance operations in the downstream petroleum industry. This process industry comprises facilities ranging from processing units to distribution networks, which makes the maintenance activities diverse. Maintenance optimization approaches from over 120 articles have been organized into two broad categories. The first category implemented maintenance operations according to the criticality of equipment by applying methods like American Petroleum Institute, Analytical Hierarchy Process, and Failure Modes and Effect Analysis. The second category applied various optimal policies by adopting different tools such as mathematical models (probability, statistics, linear or nonlinear optimization methods, fuzzy logic), heuristic (metaheuristics) algorithms (genetic algorithm, firefly algorithm), data analytics (machine learning), and Internet of Things. The review also included maintenance implementation frameworks, planning & scheduling methods, safety, mechanization, and evaluation procedures. It also tracks the recent trends in the maintenance implementation approach, identifies gaps, and recommends future research directions.