Optimal design of forced-draft counter-flow evaporative-cooling towers through single and multi-objective optimizations using oppositional chaotic artificial hummingbird algorithm

In this study, single-objective (SOP) and multi-objective (MOP) design optimization problems have been solved for mechanical forced-draft counter-flow wet-cooling towers using an enhanced-search variant of a recently proposed swarm-intelligence based metaheuristic, artificial hummingbird algorithm (AHA). Incorporating the oppositional rule to ensure a faster convergence by avoiding unnecessary search of the space, and with chaos-embedded sequences to obtain more diversifying search-population towards more accuracy in the obtained solutions, the proposed oppositional chaotic artificial hummingbird algorithm (OCAHA) has been implemented for effective design optimizations of cooling towers. In SOP, six number cases of literature have been algorithmically experimented for minimizing the total annual cost TAC as the single-objective function with mass flow rate of cooling air and cross-sectional area of tower fill as the two decision variables and with fourteen number of design inequality constraints based on the process temperatures and enthalpies. Merkel's method has been used for deriving the tower geometrical dimensions from empirical correlations of overall mass transfer-coefficient and loss-coefficient for the specified type of tower fill-packing. In MOP, range, tower characteristic ratio, effectiveness are the three objective functions, which have been maximized simultaneously with minimizing the water evaporation rate as the fourth objective for the problem. Mass flow rates of cooling air and circulating water are the two decision variables with the required input parameters of recent literature have been considered for multi-objective problem. The obtained designs through SOP and MOP have been analyzed with the competing designs, and a superior performance of OCAHA in both the optimizations have been validated.