Department of Microelectronics, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2021 Jan 7;21(2):351. doi: 10.3390/s21020351.
Natural gas component analysis is one of the significant technologies in the exploitation and utilization of natural gas. A stable and accurate online natural gas monitoring system is necessary for the gas extracting industry. We have developed an online monitoring system of natural gas with a novel hardware architecture. It improves the dependability and maintainability of the system. A specific instruction set is designed to facilitate the coordination of software and hardware. To reduce the sample noise, the exponentially weighted moving average (EWMA) method is used to preprocess the real-time raw data of the sensor array. A tailored neural network is designed for calibration. And the relationship between the performance and the structure of the gas neural network is demonstrated to find the optimal solution for accuracy and hardware scale. The design not only focuses on the optimization of individual components but also focuses on system-level improvement. The system has been running stably for several months in the gas fields. It meets the requirements of stability, ease of use, maintainability, and online monitoring in industrial applications.
天然气成分分析是天然气开发利用中的关键技术之一。对于采气行业来说，需要建立稳定、精确的天然气在线监测系统。我们设计了一种基于新型硬件架构的在线天然气监测系统，提高了系统的可靠性和可维护性。专门设计的指令集用于协调软硬件，以降低样本噪声。我们采用指数加权移动平均（EWMA）方法对传感器阵列的实时原始数据进行预处理。针对标定设计了定制化的神经网络，并展示了气敏网络的性能与结构之间的关系，以找到精度和硬件规模的最优解决方案。该设计不仅注重各个组件的优化，还注重系统级别的改进。该系统已经在气田稳定运行了几个月，满足工业应用中稳定性、易用性、可维护性和在线监测的要求。
