Wet Cooling Tower Heat Transfer and Function Prediction using MLP Neural Network

Calculating and predicting the performance of
cooling towers has posed a significant challenge for
researchers in this field. Over time, various methods,
including the utilization of artificial intelligence and
algorithms, have been proposed to address this issue. In
this study, experimental data pertaining to cooling tower
performance has been employed to develop a novel model
based on neural networks. The objective is to predict the
performance of cooling towers and analyze performance
trends in this particular type of structure. To achieve this,
a multi-layer perceptron neural network is utilized due to
its high capacity, with real data serving as input.
Subsequently, the efficiency of the neural network model
is assessed by comparing the results with real-world
samples. The validation process involves predicting
cooling tower performance, examining performance
trends, and analyzing tower behavior under windy
conditions.