ZHU Yonghong, DUAN Mingming, YANG Rongjie  
(School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, Jiangxi, China)

Abstract: Shuttle kiln is a main equipment in ceramic production. As a key process parameter in the production process of ceramic shuttle kiln, temperature plays a key role in determining the quality of ceramic products, the efficient and stable operation of kiln, reduction of energy consumption and so on. In order to effectively control the temperature in ceramic shuttle kiln, firstly, the predictive modeling method of ceramic shuttle kiln based on gated recurrent neural network was proposed in view of the characteristics of nonlinearity, large inertia, large lag and difficulty in establishing accurate mathematical model. Secondly, based on the established prediction model, the intelligent optimization control method of ceramic shuttle kiln temperature based on DDPG algorithm is proposed and the optimization control system scheme of ceramic shuttle kiln temperature based on DDPG is also presented. Finally, simulation experiments are carried out for the proposed method. Compared with PID control, fuzzy control and fuzzy PID control, the proposed method can make the error between the shuttle kiln sintering temperature and the ideal temperature to be reduced by 18.6–28.5%, showing high effectiveness and feasibility.
Key words: ceramic shuttle kiln; deep learning; GRU neural network; DDPG; intelligent optimization control