Development of a Model Using a Multilayer Perceptron for Methane Concentration Measurement System Based on a Wireless Opto-Electronic Sensor
Keywords:
methane, opto-electronic sensor, concentration, neural network, multilayer perceptronAbstract
The work proposes a model using a multilayer perceptron (MLP) for methane concentration measurement system based on a wireless optoelectronic sensor. In the controlled environment that may be dangerous to humans, a sensor is located, it remotely transmits information regarding the methane concentration to a computer, where a neural network processes the received data. As a sensor, it is proposed to use optoelectronic methane concentration sensor due to its high sensitivity, accuracy, reliability, and stability, as well as fast response, non-contact measurements, resistance to interference, the possibility of remote monitoring, and minimal maintenance. As a neural network, it is proposed to use a multilayer perceptron due to its simplicity in implementation and high efficiency for gas concentration estimation tasks in conditions of a small or medium training sample size, when the sum of basic nonlinearities well approximates the relationship between the sensor characteristics and the gas concentration without complex dynamic effects, and in case of low noise and interference, if the data has a moderate level of background fluctuations. Such systems allow measuring methane concentration with high accuracy by collecting data by means of optoelectronic sensor in real time, which, in combination with processing of the collected data using a neural network, demonstrates great potential in measuring methane concentration, and, consequently, early warning of its leakage. The work also proposes to use a multilayer perceptron model for processing data from a wireless optoelectronic sensor of methane concentration. The main indicators of the effectiveness of the proposed multilayer perceptron model, which were determined during the research, were selected such characteristics as the mean square error (MSE) and the mean absolute error (MAE). To analyze these indicators, the change in MSE and accuracy during model training and validation, the comparison of data from the optical sensor and the model, as well as the assessment of the change in MSE and MAE during the measurement process, were carried out.
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