Numerical Modelling for the Energy Transition: A 15-Year Industrial Validation of ADM1 and AM2 for Real-Time Digital Twins

• 15-year validation using 5,479 unique daily records from a full-scale multi-substrate digester. • AM2 achieves R² = 0.804 with 97% lower computational cost than ADM1 (0.16 s vs 6.18 s). • ADM1 reproduces mean biogas production with BIAS = −0.20 m³·d⁻¹ but exhibits limited temporal correlation (R² = −0.340). • Quantitative engineering criteria for model selection in real-time digital twin applications. • Seasonal robustness confirmed across all four seasons (AM2 R² = 0.769–0.824). Numerical modelling is a vital pillar for the energy transition, enabling the optimization of anaerobic digestion (AD) through real-time digital twins. Selecting appropriate computational models for AD monitoring requires validated guidance on the trade-off between mechanistic accuracy and computational efficiency. While the Anaerobic Digestion Model No. 1 (ADM1) provides comprehensive mechanistic representation, its computational complexity limits real-time implementation in resource-constrained operational systems. Reduced-order models such as Anaerobic Model No. 2 (AM2) offer computational efficiency, but their long-term reliability remains insufficiently validated under realistic operating variability. This study establishes a systematic validation framework using 15 years of daily biogas production data (5,479 unique daily records) from a multi-substrate full-scale digester, providing quantitative engineering criteria for model selection in waste-to-energy facilities. Both models were calibrated and assessed using multiple performance indicators (R², RMSE, NSE, and KGE) under seasonal variations and operational disturbances. The results demonstrate a fundamental trade-off between mechanistic completeness and temporal tracking capability: ADM1 accurately reproduces mean biogas production (BIAS = −0.20 m³·d⁻¹, 0.25% relative error) but exhibits limited temporal correlation (R² = −0.340) under the near-steady-state operating conditions of this industrial digester (CV = 8.6%, HRT = 40 d). This behaviour reflects the inherent low-pass filter structure of ADM1, whose dynamic response time constant exceeds the dominant daily variability of the signal. AM2, by contrast, achieves R² = 0.804 (KGE = 0.858, MAPE = 2.99%) with 97% lower computational cost (0.16 s vs 6.18 s for a 15-year simulation), supporting its potential suitability for real-time digital twin applications. These findings provide quantitative deployment criteria for practitioners selecting between mechanistic fidelity and real-time operability in waste-to-energy monitoring systems.