It is well known that the quality of biogas directly affects its energy value and efficiency of use in power plants, as well as determines the profitability of production. However, it can be difficult to identify deviations in the quality of biogas at early stages, especially in industrial conditions, as this requires in-depth knowledge of the physico-chemical characteristics of the gas. As part of this project, an intelligent biogas quality management system was developed using Internet of Things technologies designed to monitor key parameters such as temperature, methane levels and gas humidity. The ESP32 microcontroller, programmed in C, was used as the control module, which collected data from sensors and transferred them to cloud storage. A web application has also been created that provides the user with the ability to remotely monitor and control the system in real time from any device connected to the Internet. Notifications and alarms could be transmitted via WhatsApp messenger. The system has demonstrated high efficiency in increasing the stability and productivity of biogas production.

This article discusses the intelligent analysis of multisensory biogas data obtained from an experimental dataset generated by a Lab-on-Chip platform. The relevance of this work stems from the need for real-time monitoring of biogas quality and biomass condition under anaerobic digestion conditions, where changes in the concentrations of methane, carbon dioxide, hydrogen sulfide, oxygen, and temperature directly affect the stability of the technological process and the energy efficiency of the plant. This study utilizes a multisensor Lab-on-Chip/biosensor platform designed for rapid analysis of small samples of biogas, biomass, and biomix. The platform integrates gas, liquid, and optical sensor channels, as well as a module for transmitting data to the cloud. The experimental data obtained are processed using intelligent data analysis methods, including statistical analysis, correlation analysis, anomaly detection, and assessment of the relationships between monitored parameters. The scientific significance of this work lies in the application of an integrated approach to the analysis of multichannel experimental data obtained from the ESP32 microcontroller, which enables a more accurate and timely assessment of the state of the biogas process. The practical significance lies in the ability to use the proposed approach for remote monitoring, early detection of anomalies, and improving the efficiency of biogas plant management.

In modern conditions of sustainable development and energy efficiency improvement, optimization of biogas transportation processes is becoming particularly relevant. This paper is devoted to the study of automation and artificial intelligence methods used to improve the efficiency of biogas transportation at all stages-from production to the end user. The article discusses modern intelligent control algorithms, including machine learning and neural network models that allow predicting losses, monitoring pressure, and optimizing transportation routes. The article analyzes the possibilities of integrating the Internet of Things (IoT) and digital twins to monitor the system state in real time. A comparative study of traditional and automated transportation management methods is conducted, and their advantages and limitations are revealed. А также This paper also presents a structural diagram of a container for transporting compressed biogas and a logical diagram of an automated control system operating on the basis of a microcontroller. The container includes 16 high-pressure cylinders, piping, pressure sensors, temperature sensors, gas leaks, as well as shut-off valve elements and solenoid valves. This ensures the physical safety of storage and transportation of biogas. The control system shown in the first diagram is implemented using a microcontroller that collects data from sensors, controls actuators (ventilation, valves), displays parameters on the display, and transmits information to remote monitoring devices. This architecture allows you not only to quickly respond to changes in environmental parameters, but also to prevent emergencies through data mining.

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.

This article discusses a study on the intelligent analysis of multisensory biogas data obtained from an experimental dataset generated by a Lab-on-Chip platform. The relevance of this work stems from the need for real-time monitoring of biogas quality and biomass condition under anaerobic digestion conditions, where changes in the concentrations of methane, carbon dioxide, hydrogen sulfide, oxygen, and temperature directly affect the stability of the technological process and the energy efficiency of the plant. This study utilizes a multisensor Lab-on-Chip/biosensor platform designed for rapid analysis of small samples of biogas, biomass, and biomix. The platform integrates gas, liquid, and optical sensor channels, as well as a module for transmitting data to the cloud. The experimental data obtained are processed using intelligent data analysis methods, including statistical analysis, correlation analysis, anomaly detection, and assessment of the relationships between monitored parameters. The scientific significance of this work lies in the application of an integrated approach to the analysis of multichannel experimental data obtained from the Lab-on-Chip platform, which enables a more accurate and timely assessment of the state of the biogas process. The practical significance lies in the ability to use the proposed approach for remote monitoring, early detection of anomalies, and improving the efficiency of biogas plant management.

This article examines the automation of monitoring and control for biogas plant processes based on the intelligent processing of multi-channel sensor data. A hybrid approach is proposed that integrates digital filtering, missing data recovery, adaptive sensor calibration, forecasting, and anomaly diagnosis into a unified automated control system. The monitored parameters include temperature, pressure, pH, humidity, biogas flow rate, and the concentrations of CH₄, CO₂, and H₂S, all of which characterize the operational status and safety of the biogas plant. Automated data processing involves temporal synchronization of measurements, detection of outliers and sensor malfunctions, noise suppression, and the recovery of missing values. A NeuralODE model is employed to forecast the plant's state, accounting for the continuous dynamics of the process and the irregularity of the time series. The diagnostic module analyzes discrepancies between actual and predicted values, interdependencies between sensor channels, and signs of sensor degradation, thereby enabling the detection of process irregularities and potentially hazardous operating conditions.