In practice, the volatile organic compounds (VOCs) pollution can exist when refueling due to the properties of the gasoline, low viscosity and high saturated-vapor pressure. A new gasoline vapor recovery system involving frequency conversion technology and machine learning is developed to cope with this problem. In the proposed system, firstly, the pumping capacity of the vacuum pump is evaluated, and test shows an almost linear relationship between suction volume and frequency. Then, the Multi-Layer Perception (MLP) neural network and the support vector regression (SVR) are employed to predict the gas-liquid ratio, and the numerical examples are presented to prove the high prediction accuracy of the MLP and SVR, respectively, where the MLP neural network has better generalization ability. Finally, compared with the two gasoline vapor recovery systems based on the 1: 1 fixed control model and the PID control model, respectively, the gasoline vapor recovery efficiency is improved significantly by the new gasoline vapor recovery system.

在实践中，由于汽油的特性、低粘度和高饱和蒸气压，加油时会存在挥发性有机化合物（VOCs）污染。针对这一问题，开发了一种涉及变频技术和机器学习的新型汽油蒸汽回收系统。在所提出的系统中，首先对真空泵的抽气能力进行了评估，测试表明抽吸量和频率之间几乎呈线性关系。然后，采用多层感知（MLP）神经网络和支持向量回归（SVR）对气液比进行预测，并通过数值算例分别证明了MLP和SVR的高预测精度，其中 MLP 神经网络具有更好的泛化能力。最后，