Quantitative recognition of flammable and toxic gases with artificial neural network using metal oxide gas sensors in embedded platform

Artificial Neural Network (ANN) based pattern recognition technique is used for ensuring the reliable evaluation of responses from an array of Zinc Oxide (ZnO) based sensors comprising of pure ZnO nano-rods and composites of ZnO–SnO2. All the sensors were fabricated in the lab. The paper first reports the development of an artificial neural network based model for successfully recognizing different concentration of hydrogen, methane and carbon mono-oxide. Feed forward back propagation neural network was used for the classification of the gases at critical concentrations. The optimized ANN algorithm is then embedded in the microcontroller based circuit and finally verified under lab conditions.