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

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 te...

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Veröffentlicht in:	IEEE access Jg. 10; S. 24659 - 24677
Hauptverfasser:	Kumar, Ashish, Saini, Monika, Gupta, Nivedita, Sinwar, Deepak, Singh, Dilbag, Kaur, Manjit, Lee, Heung-No
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Availability Convergence Cooling cooling tower Cooling towers Critical components Difference equations Differential equations Failure analysis Failure rates genetic algorithm Genetic algorithms Heuristic methods Independent variables Markov modeling Markov processes Mathematical models Particle swarm optimization Poles and towers Power generation Power plants Power system reliability Random variables Reliability Repair Sensitivity analysis Steam electric power generation Steam turbines Stochastic models Subsystems Turbines
ISSN:	2169-3536, 2169-3536
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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.
AbstractList	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.
Author	Sinwar, Deepak Saini, Monika Kaur, Manjit Singh, Dilbag Kumar, Ashish Gupta, Nivedita Lee, Heung-No
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SubjectTerms	Algorithms Availability Convergence Cooling cooling tower Cooling towers Critical components Difference equations Differential equations Failure analysis Failure rates genetic algorithm Genetic algorithms Heuristic methods Independent variables Markov modeling Markov processes Mathematical models Particle swarm optimization Poles and towers Power generation Power plants Power system reliability Random variables Reliability Repair Sensitivity analysis Steam electric power generation Steam turbines Stochastic models Subsystems Turbines
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Title	Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower Using Metaheuristic Algorithms
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