Design and Application of Mixed Natural Gas Monitoring System Using Artificial Neural Networks.

Abstract

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.