AUTOMATIC DATA ACQUISITION SYSTEM FOR BIOGAS MONITORING AND MATHEMATICAL MODELING
Anaerobic Biodigester; Internet of Things; Kinetic Modeling; Spent Mushroom Substrate; Circular Economy
Efficient biogas production in anaerobic biodigesters depends on the rigorous control of physical parameters, such as temperature (T), relative humidity (RH), and operating pressure (OP), which directly regulate microbial activity and, consequently, process performance. In this context, the objective is to develop, implement, and validate a low-cost Automatic Data Acquisition System (ADAS), based on the ESP32-C6 microcontroller and digital sensors (DHT11, DHT22, BMP280, and BME280), capable of monitoring the physical parameters of biogas at short time intervals. The ADAS will be installed inside the Biogas Monitoring Module (BMM), operating under two experimental conditions (open and closed) to evaluate the effect of gas circulation on sensor performance. Validation will be carried out through simultaneous comparison with a Conventional Data Acquisition System (CDAS), consisting of a datalogger, U-tube manometer, and thermo-hygro-anemometer barometer, using metrological indicators of error, agreement, and calibration. The T, RH, and OP data obtained by the ADAS will support the mathematical modeling of biogas production kinetics based on the Accumulated Production Potential (APP, L kg⁻¹), determined exclusively after confirmation of methane presence through a flammability test. Experimental data will be fitted to nonlinear regression models (Boltzmann Sigmoid, Gompertz, Modified Gompertz, Logistic, Linear Response Plateau, and Exponential), with the best model selected according to adjusted R², Akaike Information Criterion (AIC), and normality and homoscedasticity tests. The apparent hydrolysis kinetic constant (k, week⁻¹) will be estimated using first- and second-order models, and the operational temporal indicators T₉₀ and Teff will be determined. Finally, the energy conversion potential of biogas and the associated economic savings resulting from the replacement of fossil fuels will be estimated, considering a herd of 200 dairy cows and current market prices. It is expected that the ADAS will present metrological performance equivalent to the CDAS, enabling its adoption as an accessible monitoring tool, and that kinetic