A reliability‐cost optimisation model for maintenance scheduling of wastewater treatment's power generation engines. (22nd July 2021)

• Record Type: Journal Article
• Authors: Gorjian Jolfaei, Neda; Jin, Bo; van der Linden, Leon; Gunawan, Indra; Gorjian, Nima
• Is Part Of: Quality and reliability engineering international. Volume 38:Number 1(2022)
• Page Start: 2
• Page End: 17
• Publication Date: 2021-07-22
• Subjects: optimisation -- power generation engines -- quasi‐Newton algorithm and genetic algorithm -- reliability‐cost model; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2956
• Languages: English
• ISSNs: 0748-8017

Abstract

Abstract: Power generation engines in wastewater treatment plants (WWTPs) are critical and strategic assets as major elements of an effective energy management system. Therefore, water utilities seek a smart tool to optimise the maintenance program on overall cost and reliability of these assets. In the previous study, a reliability model with consideration of redundancy was developed to estimate failure modes, reliability and availability of power generation engines in a WWTP. This study examines a joint model of reliability index using the Weibull model, operating maintenance cost using the steepest descent method and quasi‐Newton algorithm. This research is the first application of this joint model to power generation engines in WWTPs. A genetic algorithm has been employed to analyse and validate the modelling results as a global optimisation method. Results of the optimal solution are compared with conventional maintenance regimes of these engines based on recommended original equipment manufacturer (OEM). Results show an average of 22.3% reduction in maintenance costs of three engines after the implementation of the proposed cost‐optimization model. In addition, the manufacturer's recommended maintenance regime is not an appropriate maintenance strategy due to differences between the assumed and actual operating conditions. Also, hazard and reliability estimate using historical failure, operating and maintenance cost data has been ignored in the recommended OEM maintenance strategy.