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

Abstract

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.

Hybrid Genetic Algorithm and Tabu Search for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants

“…The second part is the opposite: stabilizing the demand for electricity and gradually increasing the demand for water by 5% until it reached the highest possible increase rate. First, all results were optimal compared to the results of IP and NLIP optimal methods [25], [26]. Additionally, the computational time to reach the optimal solution is less in most experiments, as illustrated in Table 5.a.…”

“…Many experiments were applied to find the optimal or near optimal solution through the following factors: number of chromosomes in each population CR, general iteration 𝛿 , internal iteration 𝛼, method of genes selection, and the value of the tenure 𝛾 of Tabu list. It is important to mention before starting with the details that the results of the proposal approach have been compared with the two published articles, [25] and [26] that used optimal method, IP and NLIP, for the same case.…”

“…All constraints were considered, and the results of the proposal were optimal compared to the schedule of power station experts, and in reasonable time. Also, Al-Hamad et al [26] used nonlinear integer programming (NLIP) method to address the previous problem. They applied the comparison with the results obtained with the previous study in terms of the time taken to reach the optimal solution, as the time taken was reasonably higher.…”

“…A hybrid Genetic Algorithm and Tabu Search would be the methodology to tackle this problem, as the study provides the PMS for each piece of equipment in the power plant, for time period of 12 months. The results will be compared with the results of the previous optimization approaches [25,26] in order to measure the quality of this methodology, in terms of solution quality and implementation time. Certain constraints will be taken into account, such as maintenance window restrictions, maintenance duration for each equipment, the level of reservoir water limits, etc.…”

Hybrid Genetic Algorithm and Tabu Search for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants

“…All constraints were considered, and the results of the proposal were optimal compared to the schedule of power plant experts and in reasonable time. Also, Al-Hamad et al [30] used the nonlinear integer programming (NLIP) method to address the previous problem. They applied the comparison with the results obtained with the previous study in terms of the time taken to reach the optimal solution, as the time taken was reasonably higher.…”