Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Abstract

Highlights Mixed potential sensor data was used to train ML algorithms for natural gas identification. Random Forest, ANN, and NN methods achieved >98% natural gas identification accuracy. Random Forest is fast enough for real time inference on portable hardware.