基于能耗最低的挤出机温度控制

Extruder Temperature Control Based on Minimum Energy Consumption

目的研究挤出机的温度对流涎薄膜生产质量的影响。方法针对流延机组挤出机料筒温度控制具有时变、非线性及大时滞等特性,以单螺杆挤出机为对象,利用PID神经元网络较强的解耦能力,并根据料筒内物料温度变化特点,提出一种分区比例控制方法,设计分区比例式PID神经元网络控制器,最后通过仿真和实验方法对其进行验证。结果分区比例式PID神经元网络控制器的阶跃信号超调量为5.9%,脉冲干扰稳定时间为162.8 s,正常生产时电耗为12.04 k Wh。传统PID控制器的阶跃信号超调量为16.4%,脉冲干扰稳定时间为192.4 s,正常生产时的电耗为13.42 k Wh。结论分区比例式PID神经元网络温度控制系统控制精度高,且优化了料桶内物料温度上升曲线,使系统单位产量的能耗得到了有效降低。

The aim of this work was to study the influence of extruder temperature on the quality of the cast film produced. According to the properties of the extruder temperature control system such as the time-varying, nonlinear, and the large delay characteristics, taking the single screw extruder as the object, we put forward a kind of partition ratio control method and designed the partition ratio type PID neuron network controller based on the strong decoupling ability of PID neural network and the characteristics of the temperature distribution of the material inside the extruder cylinder. Finally, the simulation and experiment were carried out to verify the method. The step signal overshoot of partition ratio type PID neural network controller was 5.9%, the pulse interference stability time was 162.8 s, and the power consumption during normal production was 12.04 kWh. In comparison, the step signal overshoot of traditional PID controller was 16.4%, the pulse interference stability time was 192.4 s, and the power consumption during normal production was 13.42 kWh. In conclusion, the robustness and overshoot of the partition ratio type PID neural network controller are well controlled, the control precision of the system has been greatly improved, and the energy consumption of per unit output has been effectively reduced.