Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier

Cooling of the buildings is very much mandatory in summer and to meet this, considerable energy will be spent for cooling purpose across the world. Present work focuses on the multistage evaporative cooling pads where four different packing are used to analyze the different humidification output parameters. Cam shaft which is powered by the motor gives reciprocating motion to the cooling pads which is made to dip inside the stagnant water. Input operating parameters such as air velocity, cam shaft speed and the number of cooling pads are varied and the output parameters like pressure drop, cooling effect, coefficient of performance, relative humidity drop and energy consumption rate are determined. Results indicated that, there is an increase in COP, pressure drop and the energy consumption rate with the rise in the air velocity. Artificial neural network has been used for predicting the performance parameters of the experimental results. 3-15-4 structured MLP based network is considered and is trained by using trainscg, trainlm and using trainbr networks. Results indicated a good prediction capability of ANN techniques with MRE of test data lying below 12%. Trainbr outperformed the other two networks as the correlation coefficient was much higher and MRE was lower for both training as well as test data.

Automated summary

This study focuses on analyzing the humidification output parameters of multistage evaporative cooling pads, showing that an increase in air velocity leads to an increase in coefficient of performance, pressure drop, and energy consumption rate. Artificial neural network demonstrates good prediction capability for the performance parameters of experimental results, with Trainbr outperforming other networks.