Thermodynamic Modelling with Machine Learning for Industrial-Scale Biogas Production with CHP Recovery

2026-09-16T11:53:51+02:00

Most process models of anaerobic-digestion (AD) biogas plants either resolve the biochemistry in detail while treating the combined heat and power (CHP) unit with fixed heating values, or resolve the thermodynamics while neglecting how operating conditions affect biological conversion. This study presents an open-source, digital-twin-oriented simulation framework that couples a multi-factor steady-state kinetic model of a co-digestion reactor (cattle manure, food waste and maize silage) with an exergy model of the biogas and CHP combustion, evaluated directly with the PYroMat property library. After calibration against 19 steady-state operating periods from two independent laboratory CSTR studies, the kinetic model reproduced the measured specific methane yield (SMY) with a cross-validated R² of 0.89, whereas an uncalibrated ADM1 implementation under-predicted the same data. The framework was applied to a 365-day industrial run of a 0.6 MWel plant, a 4000-point machine-learning (ML) dataset and a 10,000-run Monte Carlo study. Gradient Boosting surrogates reproduced the mechanistic model with R² = 0.990 for SMY and 0.995 for CHP exergy efficiency, values that reflect surrogate fidelity rather than accuracy against plant data. Sobol' sensitivity analysis showed that digester temperature and organic loading rate govern SMY, whereas feedstock blend and organic loading rate govern exergy efficiency, a ranking confirmed by ML permutation importance. At the nominal point, the plant reaches 82.5% energy efficiency but only 40.7% exergy efficiency, mainly because recovered heat carries little exergy. All code, data and figures are released as a reproducible artefact.

Renewable Energy Integration in Power Systems: Optimal Dispatch, Battery Storage, and Operational Performance

2026-10-19T12:42:35+02:00

The increasing penetration of renewable energy sources, particularly solar and wind power, introduces significant operational challenges for modern power systems due to their variability and uncertainty. This study presents and evaluates a modelling and optimization framework to investigate the effects of renewable energy penetration on operating costs, carbon emissions, transmission losses, renewable energy curtailment, thermal generation requirements, and system flexibility. The framework is applied to a modified IEEE 39-bus test system incorporating conventional generating units, solar and wind generation, and a battery energy storage system (BESS). A multi-period optimal power flow model is used to evaluate four renewable energy penetration scenarios (0%, 20%, 35%, and 50%) over a 24-hour period. The optimization aims to minimize daily operating costs and carbon emissions while satisfying power balance, generator operating limits, ramp-rate constraints, transmission-line limits, and battery state-of-charge (SOC) constraints. The results indicate that daily operating costs decrease by 51.05%, from USD 1.898 million to USD 0.929 million, as renewable energy penetration increases from 0% to 50%. Daily carbon dioxide emissions decrease from 19.689 to 5.562 kilotonnes, while peak thermal generation decreases by approximately 38.81%. The 35% penetration scenario achieves the lowest transmission losses, with renewable energy curtailment limited to 2.65%. At 50% penetration, curtailment increases to 14.56%, primarily due to network congestion, minimum-generation constraints, and insufficient system flexibility. Optimized battery operation further reduces renewable energy curtailment, peak thermal generation, and the maximum thermal-generation ramp rate. These findings highlight the importance of coordinated energy storage, flexible generation, and transmission network enhancements in managing the operational challenges associated with high renewable energy penetration.

Journal of Transactions in Systems Engineering

Thermodynamic Modelling with Machine Learning for Industrial-Scale Biogas Production with CHP Recovery

Renewable Energy Integration in Power Systems: Optimal Dispatch, Battery Storage, and Operational Performance