Identification of Air-Fuel Ratio for a High-Temperature and High-Speed Heat-Airflow Test System Based on Support Vector Machine

• Main Authors: Chaozhi Cai, Lubin Guo, Yumin Yang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: High-temperature; high-speed; combustion system; air-fuel ratio; support vector machine; system identification
• Online Access: https://ieeexplore.ieee.org/document/9091516/

Air-fuel ratio is an important parameter in high-temperature and high-speed heat-airflow test system. If air-fuel ratio of the system is too low, the fuel cannot be fully burned, which will not only reduce the control performance of the gas temperature, but also increase the pollutant emissions of the combustor. In order to solve this problem, it is necessary to identify the air-fuel ratio of the system, get the prediction model of the air-fuel ratio, and adjust the fuel input according to the prediction value of the air-fuel ratio. In order to realize the accurate identification of the air-fuel ratio of the system, this paper briefly analyses the mathematical model of the air-fuel ratio in high-temperature and high-speed heat-airflow test system, and proposes an identification method of the air-fuel ratio based on support vector machine. On the basis of the experimental data, the air-fuel ratio of the system is identified by using different kernels, i.e. firstly, the experimental scheme is designed, and the fuel mass flow rate, air mass flow rate, gas temperature and actual air-fuel ratio of the system are collected under different experimental conditions; then, the collected data are divided into training datasets and test datasets, and the training datasets are trained by support vector machine to obtain identification model of the air-fuel ratio; finally, the identification model is validated with test datasets under different conditions, and the accuracy of the model is obtained. The identification results show that the support vector machine has good identification performance and can accurately approximate the actual dynamic process of the air-fuel ratio. The average absolute error of the identification model is less than 0.05, and the average relative error is less than 0.5% when the test datasets are smaller than the training datasets.