Reducing the destructive effect of ambient humidity variations on gas detection capability of a temperature modulated gas sensor by calcium chloride

Despite the successful detection performance of electronic nose in laboratories, they face challenges for use in the industry due to their accuracy reduction resulted from variable ambient properties especially relative humidity (RH) variation which is studied here. Responses of a single temperature modulated metal oxide gas sensor have been analyzed by SVM and k-NN methods to achieve a detector for low concentration levels of acetone, ethanol, 1-propanol and 1-butanol in air. The classifier models were designed and tested under different train-test conditions which showed that studied gases can be detected by the classifier if only they were measured in the same train and test environmental conditions and deviation of humidity level from train condition, reduces the detection accuracy to less than 60%. The accuracy increases by expanding the training dataset and training the system with responses carried out for gas with various RH contents. It was also shown that by using CaCl2 at the rout of gas flow, the destructive effect of RH variation is reduced and the detection accuracy increases to above 90%, while to achieve this accuracy, it is not necessary to train the system in all humidity conditions. By this method, the number of required test for system training reduces drastically. The method can be generalized to other electronic nose and gas detectors which suffer from humidity variations.

Automated summary

Despite the successful detection performance of electronic nose in laboratories, challenges exist for their industrial use due to accuracy reduction caused by variable ambient properties, especially relative humidity (RH) variation. Classifier models designed to detect studied gases showed that when humidity level deviates from training conditions, detection accuracy decreases to less than 60%.