Soft computing for availability optimization of a crushing system of sugar plant using PSO

Abstract

Purpose

In this paper, the objective is to perform mathematical modeling to optimize the steady-state availability of a multi-state repairable crushing system of a sugar plant using the evolutionary algorithm of Particle Swarm Optimization (PSO). The system availability is optimized by evaluating the optimal values of failure and repair rate parameters concerned with the subsystem of the system.

Design/methodology/approach

Mathematical modeling of the multi-state repairable system is performed to develop the first-order differential equations based on the exponential distribution of the failure and repair rates. These differential equations are recursively solved to obtain the availability under normalizing conditions. The availability of the system is optimized by using the PSO algorithm. The results obtained by PSO are validated by using the Genetic Algorithm (GA).

Findings

The availability analysis of the system concludes that the cane preparation (F1) is critical of the crushing system and the optimized availability of the system using PSO is achieved as high as 87.12%.

Originality/value

A crushing system of the sugar plant is evaluated as it is the main system of the sugar plant. The maintenance data associated with failure and repair rate parameters were analyzed with the help of maintenance records/logbook and by conducting personal meetings with maintenance executives of the plant. The results obtained in the paper helped them to plan maintenance strategies accordingly to get optimal system availability.

Citation

Aggarwal, A.K. and Kumar, A. (2021), "Soft computing for availability optimization of a crushing system of sugar plant using PSO", International Journal of Quality & Reliability Management, Vol. 38 No. 9, pp. 1947-1963. https://doi.org/10.1108/IJQRM-10-2020-0347