Study On Dynamic Reaction Of Metal Oxide Gas Sensors & Sensor Array Optimization

Electronic nose is an instrument for gas/odor analysis which simulates the biologic olfaction. Comparing with the traditional gas analyzing instruments, it has the virtues of high speed for analyzing, facility in operation, easiness to carry, low cost etc. Electronic nose could be used in many application fields for important national requirements, such as food quality assessment and control, environment, security, sanitation, avigation etc. Gas sensor array is the most important part in electronic noses. Metal oxide (MOX) gas sensor is the widely used sort of gas sensors. There are several problems in the applications of electronic nose. These problems mainly included three subjects, which were the feature extraction from the response curves of MOX gas sensors, reaction model analysis of MOX gas sensors, and the sensor array optimization. These problems were analyzed in this paper.In the research of feature extraction analysis, the characters of response curves in the time domain and in phase space were analyzed. A piecemeal signal feature extraction method based on information and relativity analysis and a entire feature extraction method were established. In the piecemeal signal feature extraction method, features from the 10th second of the integrals, differences curves, and the 5.7th second of the primary derivatives curves, and the 6.2th second of the secondary derivatives curves were extracted. In the entire feature extraction method, six features were extracted. With these features, the response curves could be reconstructed. The mean error of the reconstructed response curves from the original signal response curves was 5.4%. In order to reduce the response-recovery time of electronic nose, the characters of recovery curves were analyzed. And an electronic nose with nine metal oxide gas sensors and a method of feature extraction on sensor recovery curves were established to reduce response-recovery time. With the electronic nose and the feature extraction method, the mean response-recovery time in the measurements was 33.5 s, which was about 42.7% of the response-recovery time in typical traditional gas sample measurements. Finally, the feature stabilities were compared in the measurements of formaldehyde-containing detections in octopus. The minimum relative errors of static features R₀ (resistance in the air) , S (sensor response) , and one dynamic feature DR (desorption rate) were 2.1%, 9.9% and 7.2%. In the reaction model analysis of MOX gas sensors, a reaction model of MOX gas sensors is established to simulate the sensor response patterns, where the mean simulation error was 3.98%. A performance database of sensors and a pattern matching method were built for gas sort classification without any usual pattern recognition methods. The other applications of the reaction model were also analyzed, including the researches of the influence of temperature, O₂ concentration, humidity, film thickness, grain size, doping, catalyzer etc. on the sensor response properties. Finally, the reason of different response patterns with different gas sensors was analyze. The results showed that on the surfaces of sensing materials, different gases sorts reacted with different active dots, or with different reacting probabilities.In the sensor array optimization analysis, four common feature selection method were used in working temperature selection of MOX sensor array, where a 10 sensor array with working temperature of 300℃was optimized to a 4 sensor array with working temperature of 220℃. In the sensor array optimization analysis, a sensor array optimization method based on sub-array was also established to analyze the unique functions of each sensor in the optimized array. A measurement with a 6 TGS sensors array (TGS2600, TGS2602, TGS2610, TGS2611, TGS2620 and TGS813) to classify 11 gas sorts (benzene, toluene, xylene, acetone, butanone, methanol, ethanol, formaldehyde, acetaldehyde, pentane and cyclohexane) was used in the data validation. The sensor array was optimized to 3 sensors with the method. Each sensor in the optimized array had unique functions to solve different difficult tasks. TGS2600 had the unique functions to discriminate Butanone and Acetaldehyde. TGS2602 had the unique functions to discriminate Benzene and Cyclohexane, Methanol and Ethanol. TGS813 had the unique functions to discriminate Cyclohexane and Pentane. The combination of TGS2600 and TGS2602 had the unique functions to discriminate Acetone and Butanone, Acetone and Acetaldehyde. The proposed method might be a new generation of sensor array optimization methods.