Purpose of reseach is to develop a method for generating training data to enable the use of artificial neural networks (ANN) method in gas analyzer systems. The problem of increasing the accuracy of separate determination of gas concentrations in multicomponent mixtures under conditions of environmental parameters changes is considered. It is proposed to increase the accuracy of determining target gas concentrations by using the ANN method for joint processing of sensor signals.Methods: Training data for the neural network were generated using numerical experiments and mathematical simulation methods. To assess the accuracy of training, the standard deviation (SD) was used and the relative error was calculated. ANN training and research were conducted in the MATLAB environment (the Neural Networks Toolbox application). When developing mathematical models of gas sensors, the theory of electrical circuits, electronic theory of chemisorption and the adsorption theory of heterogeneous catalysis were applied.Results: A method for generating training data sets using mathematical models of gas sensors is described. The proposed training method has been tested on a specific task, in particular, a decision-making device based on ANN for a four-component gas analyzer has been developed. The efficiency of using neural networks for tuning out from the mutual cross-sensitivity of sensors was evaluated.Conclusion: A method for generating training data using simulation models is proposed, which allows automazing the process of training, research, choosing the architecture and structure of ANN and their testing. The method was tested. Based on the analysis of the obtained errors, conclusions are made about the efficiency of using neural networks to reduce errors caused by cross sensitivity at different concentrations of the main and interfering gases.
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.
Thermocatalytic sensors are widely used in gas analysis systems and have high reliability and low cost. However, errors in measuring the concentration of combustible gases related to the non-linearity of the conversion characteristic and the influence of ambient temperature fluctuations significantly limit the scope of their application.Purpose of reseach. Development of a method for measuring gas concentration by thermocatalytic sensors, which allows reducing measurement errors by tuning out due to ambient temperature influence and linearization of the conversion characteristic.Problems. They are as follows: to develop a method for temperature stabilization of a thermocatalytic sensor; to make a structural and functional scheme for the sensor activation; to obtain a mathematical description of the method and substantitation for tuning out as a result of temperature influence; to experimentally confirm the possibility of linearization of the sensor conversion function in the thermal stabilization mode.Methods. The mathematical description of the method applies the theory of heat transfer and the theory of electrical circuits with discrete signals. When analyzing existing solutions and synthesizing the device, methods for calculating circuits with nonlinear elements and the theory of measurement systems have been used. The real conversion function has been obtained through an experimental method.Results. A method for measuring gas concentration by a thermocatalytic sensor with the use of a microcontroller and PWM has been developed. It allows reducing errors due to tuning out as a result of ambient temperature influence. A mathematical description of the method has been given. An experiment has been performed. It demonstrates the effectiveness of using temperature stabilization to linearize the conversion characteristic.Conclusion. The paper proposes a method for temperature stabilization of thermocatalytic gas sensors. The method makes it possible to increase the accuracy of measurements by tuning out due to the influence of temperature fluctuations and linearization of the conversion function. The possibility of linearization of the sensor function has been experimentally confirmed. It characterizes the dependence of the output signal on the concentration of combustible gas. Using this method allows you to reduce the cost of the sensor, improve the quality factors of the sensor, such as the reliability and stability of parameters.
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