Dynamic Processing of Gas Sensor Data Using Machine Learning Methods

This study addresses the problem of environmental monitoring of air in cities and industrial areas, which consists in detecting gases and volatile organic compounds using semiconductor gas sensors. To provide selectivity in the detection of certain gases, several semiconductor sensors with different doping components were tested. In addition, to ensure selectivity of gas determination, as well as high temporal resolution of the sensors, six types of nonlinear operating temperature conditions were used - the so-called heating dynamics. Due to the high complexity of the model describing the processes of interaction between gases and sensors, machine learning methods (linear regression with no regularization, lasso, ridge, random forest, gradient boosting and multilayer perceptron) based on the use of physical experiment data were used to process the sensor response. Optimal heating dynamics and optimal machine learning methods have been determined.