Purpose of research: search and analysis of existing models of gas-sensitive sensors. Development of mathematical models of gas-sensitive sensors of various types (semiconductor, thermocatalytic, optical, electrochemical) for their subsequent use in the training of artificial neural networks (INS). Investigation of main physicochemical patterns underlying the principles of sensor operation, consideration of the influence of environmental factors and cross-sensitivity on the sensor output signal. Comparison of simulation results with actual characteristics produced by the sensor industry. The concept of creating mathematical models is described. Their parameterization, research and assessment of adequacy are carried out.Methods. Numerical methods, computer modeling methods, electrical circuit theory, the theory of chemosorption and heterogeneous catalysis, the Freundlich and Langmuir equations, the Buger-Lambert-Behr law, the foundations of electrochemistry were used in creating mathematical models. Standard deviation (MSE) and relative error were calculated to assess the adequacy of the models.Results. The concept of creating mathematical models of sensors based on physicochemical patterns is described. This concept allows the process of data generation for training artificial neural networks used in multi-component gas analyzers for the purpose of joint information processing to be automated. Models of semiconductor, thermocatalytic, optical and electrochemical sensors were obtained and upgraded, considering the influence of additional factors on the sensor signal. Parameterization and assessment of adequacy and extrapolation properties of models by graphical dependencies presented in technical documentation of sensors were carried out. Errors (relative and RMS) of discrepancy of real data and results of simulation of gas-sensitive sensors by basic parameters are determined. The standard error of reproduction of the main characteristics of the sensors did not exceed 0.5%.Conclusion. Multivariable mathematical models of gas-sensitive sensors are synthesized, considering the influence of main gas and external factors (pressure, temperature, humidity, cross-sensitivity) on the output signal and allowing to generate training data for sensors of various types.
Purpose of research: Development of a neural model of a semiconductor gas sensor in order to generate data for training an information-processing device of gas analyzers based on artificial neural networks (ANN). Search and optimization of cleaning data composition and volume. The neural model of the sensor should take into account the influence of those factors on the signal, the fluctuations of which make the maximum contribution to the measurement errors. Testing of the model based on semiconductor carbon monoxide and hydrogen sensors.Methods. Methods of computer modeling, numerical methods, theory of neural networks. To compare the simulation results and the responses of real sensors, the relative error and standard deviation were determined.Results. Studies of various structures of the neural model of a semiconductor sensor have been carried out, the structure of a multilayer neural network of direct propagation for two semiconductor carbon monoxide and hydrogen sensors has been selected, modeling errors have been estimated, recommendations have been given for choosing the optimal structure and the amount of training data.Conclusion. Neural models of semiconductor carbon monoxide and hydrogen sensors have been obtained, conclusions have been drawn about the possibility of using this ANN structure in solving typical problems. Based on the analysis of the errors obtained, the effectiveness of using neural models of sensors to generate training data has been shown. The maximum relative error of modeling the TGS2442 semiconductor carbon monoxide sensor did not exceed 5% for the main characteristic and 2% for additional ones. The maximum relative error of modeling of the TGS2442 semiconductor hydrogen sensor did not exceed 3% for the main characteristic and 1% for additional ones.
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