| Optimization of operating parameters and performance evaluation of forced draft cooling tower using response surface methodology (RSM) and artificial neural network (ANN)
Ramkumar Ramakrishnan and Ragupathy Arumugam
The Journal of Mechanical Science and Technology, vol. 26, no. 5, pp.1643-1650, 2012
Abstract : "Optimization of cold water temperature in forced draft cooling tower with various operating parameters has been considered in the present
work. In this study, response surface method (RSM) and an artificial neural network (ANN) were developed to predict cold water
temperature in forced draft cooling tower. In the development of predictive models, water flow, air flow, water temperature and packing
height were considered as model variables. For this propose, an experiment based on statistical five-level four factorial design of experiments
method was carried out in the forced draft cooling tower. Based on statistical analysis, packing height, air flow and water flow
were high significant effects on cold water temperature, with very low probability values (< 0.0001). The optimum operating parameters
were predicted using RSM, ANN model and confirmed through experiments. The result demonstrated that minimum cold water temperature
was optioned from the ANN model compared with RSM."
Keyword : Cooling tower; Optimization; Cold water temperature; Response surface methodology; Artificial neural network |
