外文摘要：Sugarcane crushing is a complex process with multiple factors, multiple objectives, strong coupling, large nonlinearity and uncertainty. Due to the ambiguity of the pressing mechanism and the unexplicability of datadriven, the process index of sugarcane pressing process is difficult to predict. In order to solve this problem, this paper combines deep learning with pressing mechanism, and establishes a process index prediction model of sugarcane pressing process based on physics-informed neural network (PINN). Firstly, the constitutive model of sugarcane was established based on the pressing mechanism. Combined with the porous medium control equation and numerical simulation, the sugarcane pressing mechanism model was established and verified, which provided high-quality simulation data for subsequent research. Secondly, the porous medium control equation is embedded into the PINN model as a physical law to establish a unique loss function of the sugarcane pressing process. Combining the historical data and simulation data of the workshop, a large sample data is made to train the model, and the model is compared with the common data-driven model to further illustrate the accuracy and stability of the established model.
外文关键词：deep learning;Big data;Pressing mechanism;Porous media control equation;Physics -informed neural network
作者：Li, Chengfeng;Liu, Yetong;Meng, Yanmei;Duan, Qingshan
作者单位：Guangxi Univ;Guanagxi Yuchai Machinery Co Ltd
期刊名称：JOURNAL OF FOOD ENGINEERING
期刊影响因子：0.0
出版年份：2024
出版刊次：364
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台