Availability optimization of biological and chemical processing unit using genetic algorithm and particle swarm optimization

Abstract

Purpose

The demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability.

Design/methodology/approach

This paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman–Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).

Findings

Nature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA.

Research limitations/implications

This paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process.

Originality/value

Availability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.

Citation

Saini, M., Goyal, D., Kumar, A. and Patil, R.B. (2022), "Availability optimization of biological and chemical processing unit using genetic algorithm and particle swarm optimization", International Journal of Quality & Reliability Management, Vol. 39 No. 7, pp. 1704-1724. https://doi.org/10.1108/IJQRM-08-2021-0283