煤-电双目标下基于有模型强化学习的回转窑工艺参数优化

基于煤-电双目标下回转窑工艺参数优化问题,提出了有模型强化学习的解决方法. 首先,以固定时间间隔为单位对历史工艺参数和运行目标进行数据处理与聚合. 其次,搭建概率神经网络建立回转窑控制参数与影响参数、运行目标值的关系模型,该模型被用作为后期强化学习框架中的奖励模型. 然后,利用基于模型的离线策略优化的强化学习算法构建控制参数推荐智能体,同时优化回转窑生产过程的煤电消耗. 最后,给出一个案例证明所提方法对回转窑工艺参数优化的适应性、高效性.

Optimization of Process Parameters of Rotary Kiln Based on Model-Based Reinforcement Learning Under the Dual Objectives of Coal and Electricity

Aiming at the optimization problem of rotary kiln process parameters under the dual objectives of coal and electricity, this paper proposes a model-based reinforcement learning solution. Firstly, data processing and aggregation were performed on historical process parameters and operating targets in units of fixed time intervals. Secondly, a probabilistic neural network is built to establish the relationship model between the control parameters of the rotary kiln, the influencing parameters, and the operating target value, which was used as the reward model in the later reinforcement learning framework. Then, a reinforcement learning algorithm based on model-based offline strategy optimization was used to construct a control parameter recommendation agent, and at the same time, the coal and electricity consumption of the rotary kiln production process was optimized. Finally, a case analysis was given to prove the adaptability and high efficiency of the proposed method for optimizing the process parameters of rotary kiln.