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.
Highlights
It is predicted that natural gas will soon become the largest energy resource in the world, surpassing petroleum and coal [1,2,3]
We propose a mixed gas identification device based on a back-propagation (BP) algorithm, which can be used for the online detection of natural gas
The processing circuit consists of an Field-Programmable Gate Array (FPGA) chip, a liquid crystal display (LCD) and other circuit elements
Summary
It is predicted that natural gas will soon become the largest energy resource in the world, surpassing petroleum and coal [1,2,3]. In the process of mining natural gas, the crude gas contains about. Ethane and propane and other alkane gases are the main components of natural gas [4]. To ensure the quality of crude gas, we need to analyze the concentration of each component in it. Gas chromatography is the most commonly used method to determine total gas composition and each gas concentration range in natural gas [5,6,7,8]. The column chromatographic separation technique is based on the gas flow phase. The main basis for separation is that in the gas sample, different components have different adsorption degrees or solubility in the chromatographic column
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