Predicting gas concentrations of ternary gas mixtures for a predefined 3D sample space

Abstract

This paper presents a QCM sensor array and a data processing system to find gas concentration ratios of ternary gas mixtures. QCM sensors are very commonly used for gas sensing and detecting systems since they have linear responses to variable gas concentrations, especially for single gas samples. However, analyzing gas mixtures with a QCM sensor array creates a huge amount of data. Processing this data to obtain meaningful information becomes a complex and non-parametric problem. In this work, data processing is divided into pre- and post-processing sections to increase performance. The pre-processing section filters redundant data out, and extracts meaningful data. Then, a quadratic polynomial curve fitting is applied to the data. The post-processing section includes two feed-forward multi-layer artificial neural networks (ANNs): one for classification of species, and the other for quantification of the concentration ratios. Three industrial chemicals used in the experiments are acetone, chloroform and methanol. Variable volumes of these chemicals and their single, binary and ternary mixtures are applied to the sensor array, and the dynamic data is collected from the sensor responses. The ANNs are trained with 309 preprocessed data set (90% of whole data) using Levenberg–Marquardt training algorithm. Finally, the system is tested with 34 set of real data (10% of whole data). The average success rate of finding the concentration amounts in the testing phase is 93.87%, and identifying the species (classification) is 100%.