基于深度确定性策略梯度的陶瓷梭式窑温度智能优化控制

Intelligent Optimization Control of Temperature in Ceramic Shuttle Kiln Based on Depth Deterministic Policy Gradient

梭式窑是陶瓷生产中的一种主要设备。温度作为陶瓷梭式窑生产过程中的关键工艺参数,对陶瓷产品的质量、维持窑炉高效平稳运行以及降低能耗方面起着关键性作用。为了实现对陶瓷梭式窑温度的有效控制,首先,针对陶瓷梭式窑非线性、大惯性、大滞后以及难以建立精确的数学模型的特点,基于门控递归神经网络建立了陶瓷梭式窑预测模型并进行了优化。其次,基于所建立的预测模型提出了基于DDPG算法的陶瓷梭式窑温度智能优化控制方法,并给出了基于DDPG陶瓷梭式窑温度优化控制系统方案。最后,针对所提出的方法开展了仿真实验研究。仿真结果表明,与PID控制、模糊控制和模糊PID控制方法的控制效果比较,所提出的方法使得梭式窑烧结温度与理想温度误差缩小18.6%~28.5%不等,因而提出的方法是有效可行的。

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 fiizzy PID control,the proposed method can make the error between the shuttle kiln sintering temperature and the ideal temperature to be reduced by 1&6-28.5%,showing high effectiveness and feasibility.