Performance improvement of a crystallization system through optimization and sensitivity analysis

Abstract

Purpose

This paper deals with the performance optimization and sensitivity analysis for crystallization system of a sugar plant.

Design/methodology/approach

Crystallization system comprises of five subsystems, namely crystallizer, centrifugal pump and sugar grader. The Chapman–Kolmogorov differential equations are derived from the transition diagram of the crystallization system using mnemonic rule. These equations are solved to compute reliability and steady state availability by putting the appropriate combinations of failure and repair rates using normalizing and initial boundary conditions. The performance optimization is carried out by varying number of generations, population size, crossover and mutation probabilities. Finally, sensitivity analysis is performed to analyze the effect of change in failure rates of each subsystem on availability, mean time to failure (MTBF) and mean time to repair (MTTR).

Findings

The highest performance observed is 96.95% at crossover probability of 0.3 and sugar grader subsystem comes out to be the most critical and sensitive subsystem.

Originality/value

The findings of the paper highlights the optimum value of performance level at failure and repair rates for subsystems and also helps identify the most sensitive subsystem. These findings are highly beneficial for the maintenance personnel of the plant to plan the maintenance strategies accordingly.

Citation

Aggarwal, A.K. (2021), "Performance improvement of a crystallization system through optimization and sensitivity analysis", International Journal of Quality & Reliability Management, Vol. 38 No. 7, pp. 1466-1486. https://doi.org/10.1108/IJQRM-06-2020-0184