Abstract
The paper states that biogas plants are of particular importance in the development of renewable energy sources, and their efficiency is largely determined by the accuracy and reliability of parameter measurements during the production process. Sensors that determine temperature, pressure, pH, humidity, methane (CH₄) and hydrogen sulfide (H₂S) concentrations, gas flow, and oxidation-reduction potential (ORP) form the basis of the monitoring system. However, during operation, they are affected by nonlinear dependence, noise, drift, and errors that reduce the reliability of measurements. To solve this problem, mathematical modeling and sensor optimization methods are proposed. The study proposes a mathematical model that describes the correlations between the physicochemical characteristics of the environment and the output signals of the sensors. Based on this model, an analysis of the sensitivity of the measurement channels was carried out, critical areas where accuracy is significantly reduced were identified, and methods for compensating for errors were proposed. To improve the reliability of the results, intelligent data processing was used, including artificial neural networks, which allow adaptive adjustment of output data and calibration in real-time monitoring mode. The proposed approach improves measurement accuracy and the stability of the sensor system to external influences, which is also of practical importance for monitoring and controlling biogas plants. A mathematical model was proposed that takes into account the physicochemical dependence on environmental parameters (temperature, pressure, pH, Ch₄ and H₂S concentrations, humidity, gas flow, and redox potential) and sensor response. Based on this, a sensitivity analysis of the measurements was performed to identify areas of maximum error. Intelligent data processing using artificial neural networks was used to compensate for systematic errors and sensor drifts, which allowed for real-time calibration and correction of sensor readings.