Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review. (December 2017) - Jani, D.B.; Mishra, Manish; Sahoo, P.K.
Abstract: In present study, an attempt has been made to review the applications of artificial neural network (ANN) for predicting the performance of solid desiccant cooling systems. Different types of neural networks are applied to model the solid desiccant cooling systems. With use of experimental data, an ANN model was developed which is based on different algorithms. Available experimental data were divided into two categories for training and testing of the ANN model. Later on, trained ANN model was tested for predicting the performance of system based on various input and output parameters such as air stream flow rates, temperatures and humidity ratios, pressure drop, dehumidifier effectiveness, cooling capacity, regeneration temperature, power input, coefficient of performance etc. So, present review proposes the use of ANN based model to simulate the relationship between inlet and outlet parameters of the system. The ANN predictions for these parameters usually agreed with the experimental values with higher correlation co-efficient. The previous studies show that ANNs can be used with a higher precision in guessing the performance of solid desiccant cooling systems. This review is useful for making opportunities to further research of ANNs and its feasibility which is becoming common in the coming days. Highlights: A noval approach of ANN modeling for desiccant cooling was studied. The ANN models were found to agree with the experimental studies. Predictive accuracy of ANN differs with the network architechture. ANN has an excellent performance with a small amount of training data.