A physics-constrained hybrid residual neural network for the prediction of moisture content in a closed-cycle drying system

Abstract

Closed-cycle drying technology has the advantages of safety, energy saving, and environmental protection, and has a wide application prospect. Due to the multi-physics and multi-scale nature of heat/mass transfer in the closed-cycle drying process, as well as the characteristics of drying medium circulation and energy integration, it is difficult to obtain the product moisture content prediction model in closed-cycle process based on rigorous closed-cycle drying process mechanism model. However, accurate monitoring of product moisture content in the closed-cycle drying process is the key to improving drying quality and process optimization. Aiming at the challenges in predicting moisture content, this paper proposes a physics-constrained hybrid residual neural network (PC-HRNN) for soft sensor modelling within a designed closed-cycle drying system. The characteristic of this proposed method lies in its effective fusion of physics, knowledge, and data. First, a hybrid residual neural network (HRNN) is constructed based on the integration of a physics model and a data-driven model. The HRNN takes the knowledge from the physics model as its auxiliary features and learns the prediction residuals of the physics model through a neural network model. Then, a new loss function that takes into account physical laws is introduced into HPNN to standardize model training and improve the generalization performance of the model. The experimental results show that the PC-HRNN model improves prediction accuracy and model robustness, reduces the demand for data, and demonstrates stronger extrapolation capabilities under limited data.