Jia Tanghao, Guo Tianle, Wang Xuming, Zhao Dan, Wang Chang, Zhang Zhicheng, Lei Shaochong, Liu Weihua, Liu Hongzhong, Li Xin
Department of Microelectronics, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2019 May 5;19(9):2090. doi: 10.3390/s19092090.
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.
测量天然气中各成分的浓度是一项艰巨的挑战，因为混合气体中的不同成分对单个传感器具有交叉敏感性。我们基于神经网络算法开发了一种混合气体识别装置，可用于天然气的在线检测。利用神经网络技术消除混合气体对每个传感器的交叉敏感性，以便分别准确识别甲烷、乙烷和丙烷的浓度。该装置中的神经网络算法由现场可编程门阵列（FPGA）实现，具有体积小、响应速度快的优点。FPGA利用并行计算，大大加快了神经网络的计算过程。在甲烷含量为0 - 100%的范围内，甲烷以及乙烷和丙烷等重质烷烃的测试误差小于0.5%，响应速度为几秒。
