Neural Network-Based Classification of Toxic Gases for a Sensor Array

Abstract

Electronic noses or array sensors are very popular in the last decades because of their ability to avoid the cross-sensitivity issue in semiconductor metal oxide (SMO) gas sensors. The identification and discrimination of toxic gases have a significant role in industrial applications. This work encompasses the classification of carbon monoxide (CO) and methane (CH4) toxic gases using a gas sensor array. Classification algorithm based on artificial neural network (ANN) with one hidden layer is used for identifying the gas type from the gas mixture. This metal oxide gas sensor array is built with six SMO gas sensors, which are sensitive to several types of gases. The ANN model ensures a training accuracy of 94.57% and a validation accuracy of 93.33%. For practical applications, the gas concentration is randomly assigned in the training stage. Neural network-based classification algorithm provides better performance in identifying the type of gas.