Biomethane production modelling from third-generation biomass

Abstract

Third-generation biomass constitutes a renewable energy source that could substitute fossil fuels. This study evaluated the biomethane potential (BMP) of Ulva sp., Codium sp. and Undaria pinnatifida through anaerobic digestion. The daily production of biomethane was evaluated using different models, including Modified Gompertz, Chen and Hashimoto, First-order, Transfer and Cone models, as well as an Artificial Neural Network (ANN) model. The experimental BMP was 0.17, 0.26, and 0.32 Nm³/kg VS for Ulva sp., Codium sp., and U pinnatifida, respectively. Among the non-linear regression models, the Transfer model (R2 > 0.9915) and the First-order model (R2 > 0.9889) are the ones that best fit the experimental data. However, the ANN shows a better fit (R2 > 0.999 and RMSE<4.537) to the data compared to the non-linear regression models. Furthermore, ANN can capture the complexity of biological systems, allowing for more accurate and detailed modelling of the processes involved. The identification and optimization of the biomethane potential of macroalgae contribute to developing sustainable energy alternatives, offering a renewable energy source that could mitigate the environmental stress associated with traditional fossil fuels.