锂电池悬浮式烘箱数值模拟分析与结构优化

目的 为提高盐业包装线封口温度控制精度，融合果蝇优化算法和径向基神经网络设计一种温度控制系统。方法 介绍控制系统结构，利用RBF神经网络的自学习、自适应能力实现PID控制器参数的在线调节，可确保封口温度的自适应控制。通过果蝇优化算法实现神经网络初始值优化，提高神经网络的全局搜索能力。最后，进行仿真和实验分析。结果 结果表明，温度偏差可以控制在1%以下，该控制算法具有较好的稳定性，达到稳定状态耗时较少，系统超调量明显变小，在一定程度上提升了封口温度控制的精确性和稳定性。结论 所述控制系统控制性能比较理想，可满足食用盐包装封口温度控制需求。

Numerical Simulation Analysis and Structure Optimization of Lithium Battery Suspension Oven

The paper aims to design a temperature control system by integrating drosophila optimization algorithm and radial basis neural network to improve the temperature control accuracy of salt packaging line. The structure of the control system was introduced. The self-learning and adaptive ability of RBF neural network were used to realize the online adjustment of PID controller parameters, which can ensure the adaptive control of sealing temperature. The initial value of neural network was optimized by drosophila optimization algorithm, and the global searching ability of neural network was improved. Finally, the simulation and experimental analysis were carried out. The results showed that the temperature deviation can be controlled below 1%, and the control algorithm had good stability. It took less time to reach the stable state, and the overshoot of the system was significantly reduced, which improved the accuracy and stability of sealing temperature control to a certain extent. The control system has ideal control performance and can meet the requirements of controlling the sealing temperature of edible salt packaging.