New multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions

Abstract

In this study, five Multiple Linear Regression, three Multilayer Perceptron Regressor, seven Decision Tree and four Support Vector Machine models were constructed to predict outlet temperature and humidity ratio of silica gel desiccant wheels using eight input parameters for unbalanced flow condition. The effect of different kernel functions of Support Vector Machine algorithms, on the modeling of desiccant wheel was investigated for the first time in the open literature. Detailed validation of the developed models showed that the Response Surface model outperformed other Multiple Linear Regression models, and the Support Vector Machine model with Pearson VII Universal kernel was the best among all models. The determination coefficient and root mean square error for temperature were found to be 0.9791 and 1.2832 °C for the Response Surface model and, 0.9984 and 0.3511 °C for the Support Vector Machine model with Pearson VII Universal kernel, respectively. In the case of humidity ratio, the corresponding statistical parameters were 0.9763 and 0.5672 g/kg for the former and, 0.9976 and 0.1810 g/kg for the latter. The proposed models can be used reliably in the analysis of solid desiccant-based air conditioning systems for design and energy analysis.