Comparative study of multiple linear regression (MLR) and artificial neural network (ANN) techniques to model a solid desiccant wheel

In recent years, the use of solid desiccant wheels has become attractive not only for air-conditioning applications, but also for food drying processes and storage due to their capacity to use waste heat in order to meet dry and hot air demand. It is very important that solid desiccant wheels be modelled for different purposes in such a way that the equipment can be well analysed in various systems. Modelling the solid desiccant wheel is a difficult and complex process because of the coexisting heat and mass transfer. In this study, six Artificial Neural Network (ANN) models with various activation functions and Multiple Linear Regression (MLR) models with six different structures have been formed to observe the process air outlet conditions of the solid desiccant wheel, and compared with each other to identify the suitability of the use of these models. In comparison, R-2, RMSE and MAE values were taken into consideration as performance criteria. At the end of the study, ANN models were observed to provide better convergence than MLR models. The best convergence for the process air outlet conditions was provided by the ANN-V model. Of all the MLR models, the best convergence was provided by MLR-VI model.