Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower Using Metaheuristic Algorithms

Abstract

Metaheuristic algorithms are extensively utilized to find solutions and optimize complex industrial systems' performance. In this paper, metaheuristic algorithms are utilized to predict the optimum value of the operational availability of a cooling tower in a steam turbine power plant. These techniques have some flaws like poor convergence speed, being stuck in local optima, and premature convergence. For this purpose, a novel efficient stochastic model is proposed for a cooling tower that is configured with six subsystems. The Markovian birth-death process is utilized to develop the Chapman-Kolmogorov differential-difference equations. All the random variables are statically independent, and repairs are perfect. Failure rates are exponentially distributed, while repair rates follow the arbitrary distribution. Steady-state availability (SSA) of the system is derived concerning various failure and repair rates. The sensitivity analysis of SSA is also performed to identify the most critical component. Further, system availability is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) because they are found to be more suitable for such types of problems. It is revealed that the PSO outperforms GA in predicting the availability of cooling towers used in steam turbine power plants.