This study conducts a comprehensive sustainability assessment of Dry Reforming of Methane (DRM), focusing on carbon intensity and syngas energy recovery (%) as primary performance indicators. By combining thermodynamic analysis with physics-informed machine learning (ML) models, DRM performance is evaluated across a range of operating conditions. Incorporating reaction enthalpy, carbon intensity, and syngas energy recovery as engineered features substantially improves model accuracy over baseline and kinetic models. Monte Carlo simulations are used to quantify uncertainty and identify robust operating windows, while techno-economic analysis benchmarks DRM against Steam Methane Reforming (SMR) and electrolysis. The results demonstrate that DRM can achieve syngas energy recovery values up to 190% and carbon intensity as low as 0.17, underscoring its promise as a competitive, low-carbon pathway for hydrogen and syngas production.
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Devasahayam et al. (2025) studied this question.
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