Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by an FPGA - Jia, Tanghao; Guo, Tianle; Wang, Xuming; Zhao, Dan; Wang, Chang; Zhang, Zhicheng; Lei, Shaochong; Liu, Weihua; Liu, Hongzhong; Li, Xin (Sensors (Basel, Switzerland), 2019)
It is a daunting challenge to measure the concentration of each component in natural gas, because different components in mixed gas have cross-sensitivity for a single sensor. We have developed a mixed gas identification device based on a neural network algorithm, which can be used for the online detection of natural gas. The neural network technology is used to eliminate the cross-sensitivity of mixed gases to each sensor, in order to accurately recognize the concentrations of methane, ethane and propane, respectively. The neural network algorithm is implemented by a Field-Programmable Gate Array (FPGA) in the device, which has the advantages of small size and fast response. FPGAs take advantage of parallel computing and greatly speed up the computational process of neural networks. Within the range of 0–100% of methane, the test error for methane and heavy alkanes such as ethane and propane is less than 0.5%, and the response speed is several seconds.