大气压下同轴圆柱反应器DBD等效电路模型参数预测

大气压下同轴圆柱反应器介质阻挡放电在气体处理领域应用广泛。反应器等效电路模型是高性能介质阻挡放电电源设计的关键。然而,反应器等效电路模型参数随工作状况而非线性变化,这增加了复杂工况下系统精准设计的难度。针对这一问题,在非线性钳位等效模型的基础上采用遗传算法优化的神经网络对反应器等效模型参数进行预测。以系统的电压幅值、工作频率、气体流速和气体温度作为模型输入,以非线性钳位模型的钳位电压、介质等效电容和气隙等效电容作为模型输出。实验结果表明,在较宽的预测范围内,该方法保持较高的预测精度。利用该方法在全局范围内对反应器介质阻挡放电等效模型参数进行预测,可以为复杂工况下的电源设计提供更准确的反应器等效模型。

Parameter Prediction for Equivalent Circuit Model of Dielectric Barrier Discharge in Coaxial Cylinder Reactor at Atmospheric Pressure

Dielectric barrier discharge(DBD) in a coaxial cylinder reactor at atmospheric pressure is widely used in the field of gas treatment. The equivalent circuit model of a reactor is the key to the design of high-performance DBD power supply. However, the parameters of this model vary nonlinearly with the change of working conditions, which com-plicates the accurate design of the system under complex operating conditions. To solve this problem, based on a nonlinear clamping equivalent model, a neural network optimized by genetic algorithm is used to predict the parameters of the equivalent model. The input of this model are the system''s voltage amplitude, working frequency, gas flow rate, and gas temperature, while its output are the clamping voltage of the nonlinear clamping model, equivalent capacitance of dielectric, and equivalent capacitance of gas. Experimental results showed that this method maintained a relatively higher prediction accuracy in a wider range. Therefore, it can be used to predict the parameters of the equivalent model of DBD in a reactor in a global range, which provides an accurate equivalent model of the reactor for power supply design under complex operating conditions.