基于模糊强化学习的空气压缩机群组协调预测控制

空气压缩机在提供动力的同时消耗着大量的电力能源,其节能增效备受关注.空气负荷需求受生产节奏、计划排程等影响呈现阶段性、间歇性的特点,导致空气压缩机组在大范围内变工况运行,人工调节速度慢,机组能耗高.针对此问题,以国内工业园区空气压缩机群组系统为背景,提出一种基于模糊强化学习的空气压缩机群组协调预测控制方法.首先利用工业现场的历史数据和设备运行机理建立基于模糊辨识的空气压缩机群组多工况模型;在此基础上,以最小化生产过程能耗为目标,结合生产工艺条件、设备安全等约束条件,建立基于模糊强化学习的空气压缩机组变负荷协调预测控制方法,保证系统在复杂工况下安全稳定运行;最后,将所提出方法应用于工业园区空气压缩仿真系统进行性能测试,取得较好的控制效果.

Coordinated predictive control of air compressor units based on fuzzy reinforcement learning

As the air compressor consumes a large amount of electric energy while providing driven-energy, its energy saving and efficiency enhancement have attracted much attention. The demand of compressed air is with the property of periodicity and intermittency due to the production rhythm, planned scheduling and other factors, which might cause the operational mode change of air compressor units in a wide range. The manual scheduling has the property of slow speed and energy consumption. Thus, aiming at the air compressor system of an industrial park, a coordinated predictive control method of air compressor units based on fuzzy reinforcement learning is proposed. First, a multiple operational mode model of compressed air group based on fuzzy identification model is structured by using the historical data of industrial site and equipment operation mechanism. On this basis, a variable load predictive control method of the air compressor group based on fuzzy reinforcement learning is established to ensure the safe and stable operation of the system under complex operational modes, which aims to minimize the energy consumption in the production process and combines the production process conditions, equipment safety and other constraints. Finally, the proposed method is verified by the air compression simulation system in an industrial park and a good control result is achieved.