UNSPECIFIED (1993) A MODIFIED MULTILAYER PERCEPTRON MODEL FOR GAS-MIXTURE ANALYSIS. In: EUROSENSORS 6, SAN SEBASTIAN, SPAIN, OCT 05-07, 1992. Published in: SENSORS AND ACTUATORS B-CHEMICAL, 16 (1-3). pp. 344-348.

Abstract

An investigation was carried out on the ability of artificial neural networks (ANNs) to quantify the concentrations of individual gases and gas mixtures in air from patterns generated by an array of chemically modified sintered SnO2 sensors. The aim of this study was to design a neural paradigm that could compute the concentrations of four gases (H-2, CH4, CO and CO2) in simple gas mixtures. The experimental data were gathered by a gas test station with an array of three commercial Taguchi sensors (822, 813 and 815) and three catalytically modified sensors (812 with 1 mug of Pd, Au, Rh, respectively). The change in conductance of each of the six sensors was measured up to concentrations of 15 000 (H-2), 10 000 (CH4), 500 (CO) and 15 000 (CO2) PPM. Analysis of the raw data showed that the individual sensor responses were highly non-linear over the chosen concentration ranges and that the CO2 data fell in the noise. So the detection Of CO2, on its own or in gas mixtures, was problematic with sintered SnO2 sensors. Initially, three preprocessing algorithms were applied to the input data and fed into fully connected multilayer perceptron models with the backpropagation paradigm. The network error was minimised by changing the number and size of the hidden layers and the learning rate and momentum, yet its overall performance was still poor. Consequently, the model was modified by using three non-linear target functions (log, sigmoid and tanh). These models only gave slightly improved results. Finally, we adopted a partially connected network with the six input elements connected to all 9 elements in a single hidden layer. This corresponded to 3 for each gas (excluding the CO, data), but each group of three elements in the hidden layer was only connected up to one output. This helped to compensate for the relatively small signal for CO compared with H-2 and CH4, the idea being to separate the learning characteristics for each gas and thus obviate poor data for one gas affecting another with better data. The best results were obtained using log input and tanh output processing functions. In this case, the maximum prediction error was 10% for H-2, CH, and CO gases. It was also possible to quantify H-2:CH, gas mixtures to a similar accuracy with no interference effect observed from humidity changes. The CO concentration could also be detected in H-2:CH4:CO gas mixtures but to a much lower degree of accuracy.

Data sourced from Thomson Reuters' Web of Knowledge

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