Optimal design and analysis of mechanical draft cooling tower using improved Jaya algorithm

• Published in: International journal of refrigeration Vol. 82; pp. 312 - 324
• Main Authors: Rao, R.V., More, K.C.
• Format: Journal Article
• Language: English
• Published: Paris Elsevier Ltd 01.10.2017; Elsevier Science Ltd
• Subjects: Adaptive algorithms; Algorithme de colonie d'abeille artificielle; Algorithme Jaya; Algorithme Jaya auto-adaptif; Artificial bee colony algorithm; Computational mathematics; Cooling; Cooling tower; Cooling towers; Cost optimization; Costs; Design optimization; Energy conservation; Industrial engineering; Industrial plants; Jaya algorithm; Manufacturing engineering; Mathematical models; Merkel method; Méthode Merkel; Méthode Poppe; Optimisation du coût; Optimization algorithms; Poppe method; Population density; Resource conservation; Self-adaptive Jaya algorithm; Swarm intelligence; Tour de refroidissement; Méthode Merkel; Algorithme de colonie d'abeille artificielle; Algorithme Jaya auto-adaptif; Jaya algorithm; Optimisation du coût; Algorithme Jaya; Poppe method; Self-adaptive Jaya algorithm; Tour de refroidissement; Artificial bee colony algorithm; Méthode Poppe; Merkel method; Cost optimization; Cooling tower
• ISSN: 0140-7007; 1879-2081
• DOI: 10.1016/j.ijrefrig.2017.06.024

•Improved Jaya algorithm is proposed for design of mechanical draft cooling tower.•Results of the proposed algorithm are comparatively much better.•The algorithm's performance is superior in terms of time and convergence. A cooling tower is an imperative component of industrial plants. The minimization of energy related expenses is critical for conservation of resources and energy savings. Hence, the present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of cooling tower from economic facets. In this work, six different examples are considered in the design optimization of mechanical draft cooling tower. Various researchers have attempted the same mathematical models by using different methods like Merkel method, Poppe method and artificial bee colony (ABC) algorithm. The results achieved by using the proposed self-adaptive Jaya algorithm are compared with the results achieved by using the Merkel method, Poppe method, ABC algorithm and basic Jaya algorithm. The proposed self-adaptive Jaya algorithm determines the population size automatically and the user need not tune the population size. The proposed self-adaptive Jaya algorithm is proved better as compared to the other optimization methods with respect to achieving the optimal value of the objective function at less computational effort.