An Intelligent Gas Concentration Estimation System Using Neural Network Implemented Microcontroller

2004 ◽  
pp. 1206-1212
Author(s):  
Ali Gulbag ◽  
Fevzullah Temurtas
Keyword(s):  
Neural Network ◽  
Gas Concentration ◽  
Concentration Estimation ◽  
Estimation System

A gas migration law study of a large-scale 3D physical similarity simulation with an adaptive Kalman filter algorithm

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyu Hao ◽  
Shugang Li ◽  
Tianjun Zhang
Keyword(s):  
Kalman Filter ◽  
Large Scale ◽  
Gas Migration ◽  
Adaptive Kalman Filter ◽  
Physical Similarity ◽  
Content Type ◽  
Gas Concentration ◽  
Rock Formation ◽  
Concentration Estimation ◽  
Migration Law

Purpose In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law. Design/methodology/approach First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend. Findings This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach. Originality/value For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

Multi-Dimensional Encoding Image Based Time Series Feature Extraction for Improved Colorimetric Gas Concentration Estimation

2021 ◽  
Vol MA2021-02 (57) ◽  
pp. 1939-1939
Author(s):  
Changhyun KIM ◽  
Junyeop Lee ◽  
Junkyu Park ◽  
Daewoong Jung ◽  
Chang-Woo Nam ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Gas Concentration ◽  
Concentration Estimation

A New Approach to Analyzing Gas Mixtures Based on Improved BP Neural Network Algorithm

2012 ◽  
Vol 260-261 ◽  
pp. 548-553
Author(s):  
Teng Li ◽  
Xiao Mei Yuan ◽  
Shi Liang Yang ◽  
Xin Hui Zhang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Bp Neural Network ◽  
Array Signal Processing ◽  
Sensor Arrays ◽  
Gas Mixtures ◽  
New Approach ◽  
Gas Concentration ◽  
Network Algorithm ◽  
Neural Network Algorithm

A new approach is presented for analyzing gas mixtures by transforming the problem into a pattern classification one to reduce the effect of the poor repeatability of sensor response on the prediction of gas concentration. The aim of numerical simulation is to determine how successfully the approach using the combination of artificial neural networks with multi-sensor arrays can analyze multi-component gas mixtures. The results indicate that the new approach is realistic for gas mixture analysis, and numerical simulation is a powerful tool to determine the architecture of a network. By constructing improved BP neural network algorithm and basic BP neural network into sensor array signal processing and extracting 6 component as the input of neural network, Our investigation results indicated that recognition results obtained from improved BP neural network algorithm more accuracy than the results obtained from basic BP neural network.

scholarly journals WATER TREATMENT BY OZONATION: CONTAMINANTS CONCENTRATION ESTIMATION BY DYNAMICAL NEURAL NETWORK

2005 ◽  
Vol 38 (1) ◽  
pp. 191-196
Author(s):  
Isaac Chairez ◽  
Alexander Poznyak ◽  
Tatyana Poznyak
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Concentration Estimation ◽  
Dynamical Neural Network
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