Abstract
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.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference26 articles.
1. Natural Gas Processing with Membranes: An Overview
2. Natural gas-hydrates — A potential energy source for the 21st Century
3. Potential and prospects of conventional and unconventional natural gas resource in China;Li;Acta Pet. Sin.,2012
4. Main Factors Affecting the Changes in Compositions of Natural Gas;Yan;Pet. Explor. Dev.,1991
5. Composition Analysis of Natural Gas by Multi-Dimensional Gas Chromatography;Liu;Xinjiang Oil Gas,2014
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献