Experimental study on condensate heat transfer coefficient of multi-channel cylinder dryer integrated with Bayesian-optimized machine learning prediction

Abstract

Abstract The condensation heat transfer coefficient of the multi-channel dryer, one of the key component of paper-making machine, directly determines the efficiency of heat energy utilization. However, the prediction of condensation heat transfer coefficient remains a challenge because the heat transfer characteristics in multi-channel dryer is a complex fundamental issue involving the thermal behavior of two-phase fluid systems. Herein, we successfully developed the four supervised machine learning models to predict the heat transfer coefficient of a multi-channel cylinder dryer under different working conditions. The multi-channel cylinder dryer experiments under different steam mass flux and cooling water mass flow rates were performed and the measured data is used as the input data for training. Interestingly, the four trained Bayesian-optimized machine models present the excellent capability of prediction for condensation heat transfer coefficient of multi-channel cylinder dryer, where the values of R 2 for tested Bayesian-optimized-based SVR, ANN, linear SVR, and RF are 0.983, 0.997, 0.996, and 0.953, respectively. In addition, the feature importance of descriptors is quantified based on a random forest algorithm. Our study suggests that machine learning models can effectively predict the condensate heat transfer coefficient of two-phase fluid systems, which not only would be beneficial to optimizing the structures and operation parameters of multi-channel cylinder dryer in the industry but to develop a reasonable correlation of heat transfer coefficient in fundamental research.