Optimal configuration and operating condition of counter flow cooling towers using particle swarm optimization algorithm

Cooling towers are broadly utilized in industrial activities and consume substantial energy, resource and expenditure. Optimization of cooling tower configurations and operating conditions is imperative for reducing consumptions. In this paper, the counter flow cooling tower optimization problems are solved utilizing the particle swarm optimization (PSO) algorithm, which is able to handle both single-objective and deeper multi-objective optimization problems. Based on heat and mass transfer balance equations, the mathematical model of the counter flow cooling tower is established. Validation of the mathematical model shows satisfactory agreement with the experimental data. The single-objective particle swarm optimization (SOPSO) is explored through the proposed mathematical model. With appropriate population size and iteration step number, six cases pursuing minimum total annual cost are investigated by deciding multiple optimal decision variables. Since SOPSO only considers one objective, which may be restricted under conditions with diverse requirements, the multi-objective particle swarm optimization (MOPSO) is also introduced. The case for MOPSO involves four objectives, namely the range, tower characteristic ratio, effectiveness and water evaporation rate, while flow rates of air and water are the decision variables. The results of SOPSO and MOPSO are both compared with previous literatures and satisfactory performance of PSO algorithm is revealed.