Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

Alhamad, Khaled; Alardhi, Mohsen; Almazrouee, Abdulla I.

doi:10.1155/2015/282178

Cited by 8 publications

(13 citation statements)

References 37 publications

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“…For cogeneration systems, a panoply of optimization approaches varying from approximate to exact methods are available. Approximate approaches vary from simple heuristics, tabu search to genetic algorithms [12][13][14][16][17][18][19] whereas exact approaches are mostly integer programming based [20][21][22][23][24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

2021

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This paper considers the scheduling of preventive maintenance for the boilers, turbines, and distillers of power plants that produce electricity and desalinated water. It models the problem as a mathematical program (MP) that maximizes the sum of the minimal ratios of production to the demand of electricity and water during a planning time horizon. This objective encourages the plants’ production and enhances the chances of meeting consumers’ needs. It reduces the chance of power cuts and water shortages that may be caused by emergency disruptions of equipment on the network. To assess its performance and effectiveness, we test the MP on a real system consisting of 32 units and generate a preventive maintenance schedule for a time horizon of 52 weeks (one year). The generated schedule outperforms the schedule established by experts of the water plant; it induces, respectively, 16% and 12% increases in the surpluses while either matching or surpassing the total production. The sensitivity analysis further indicates that the generated schedule can handle unforeseen longer maintenance periods as well as a 120% increase in demand—a sizable realization in a country that heavily relies on electricity to acclimate to the harsh weather conditions. In addition, it suggests the robustness of the schedules with respect to increased demand. In summary, the MP model yields optimal systematic sustainable schedules.

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Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

2021

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“…In addition, factories [15][16][17][18][19] that contain machines and devices used in industry need preventive maintenance. Moreover, oil companies and power stations apply PM for their equipment [20][21][22][23][24]. The purpose of the PMS function is clearly to contribute to achieving the goals of the organization, which, in turn, requires the need for practical maintenance to be consistent with the organization objective.…”

Section: Introductionmentioning

confidence: 99%

“…Alhamad et al [23] applied genetic algorithms for a multi-cogeneration power system to create two preventive maintenance schedules, one for electricity and one for distillers. The aim of this model was to maximize the available number of operational units in each station.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Integer Programming for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants with Production

2022

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Preventive maintenance (PM) is a maintenance program with activities created at a determined interval or according to certain principles, designed to reduce the likelihood of failure or deterioration of item performance. This aims to improve overall reliability and system availability. In this research, a preventive maintenance schedule (PMS) was designed for electricity and desalination of water in power plants, subject to meeting relevant constraints. The proposed methodology is used to generate a PMS for the boilers, turbines, and distillers. A nonlinear integer programming (NLIP) model was employed to address this problem. The results of the proposed method were compared with the PMS for a power station in Kuwait. The results were better in terms of the volume of production and in terms of the gap between the available production and demand in order to continue providing consumers with electricity and water without a shortage in the event of a breakdown in equipment. It produces an improvement of 12.12% and 16.58% respectively, for water and electricity. Furthermore, the sensitivity and robustness of the proposed method were analysed by increasing the maintenance duration for some equipment, increasing the demand, and adding various additional conditions. In addition, a comparison of additional conditions with a binary problem method in terms of computer time for the search for an optimal solution was carried out, where the model provided an optimal solution in a reasonable time. Among the most important benefits that the user can obtain for this technique are extending the life of the equipment, increasing efficiency, and reducing expenses.

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“…proposed a non‐dominated sorting GA to optimise the maintenance schedule of multiple reactors in a nuclear power plant with minimisation of maintenance cost, risk and unavailability of the reactors in a nuclear power plants. Alhamad et al 19 . used GA to set up preventive maintenance schedule tasks of the generation and desalination units in separate and linked cogeneration plants.…”

Section: Introductionmentioning

confidence: 99%

A reliability‐cost optimisation model for maintenance scheduling of wastewater treatment's power generation engines

Jolfaei

Jin

Linden

et al. 2021

Quality & Reliability Eng

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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.

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Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

Cited by 8 publications

References 37 publications

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

Nonlinear Integer Programming for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants with Production

A reliability‐cost optimisation model for maintenance scheduling of wastewater treatment's power generation engines

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