Explosive gas recognition system using thick film sensor array and neural network

A sensor array with nine discrete sensors integrated on a substrate was developed for recognizing the species and quantity of explosive gases such as methane, propane, and butane. The sensor array consisted of nine oxide semiconductor gas-sensing materials with SnO₂ as the base material plus a heating element based on a meandered platinum layer all deposited on the sensor. The sensors on the sensor array were designed to produce a uniform thermal distribution and show a high and broad sensitivity and reproductivity to low concentrations through the use of nano-sized sensing materials with high surface areas and different additives. Using the sensitivity signals of the array along with an artificial neural network, a gas recognition system was then implemented for the classification and identification of explosive gases. The characteristics of the multi-dimensional sensor signals obtained from the nine sensors were analyzed using the principal component analysis (PCA) technique, and a gas pattern recognizer was implemented using a multi-layer neural network with an error back propagation learning algorithm. The simulation and experimental results demonstrate that the proposed gas recognition system is effective in identifying explosive gases. For real time processing, a DSP board (TMS320C31) was then used to implement the proposed gas recognition system in conjunction with a neural network.