ArticleFoods (Basel, Switzerland)2026
Reactor-Aware Machine Learning Coupled with Differential Evolution for Predicting and Optimizing Cumulative Methane Production from Agro-Industrial Waste Co-Digestion.
Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Anaerobic digestion of agro-industrial residues supports waste valorization and renewable-energy production, but reliable prediction requires validation that accounts for repeated measurements within reactors. This study compared 16 regression models for predicting cumulative methane production from digestion time and banana peel-sugarcane molasses composition using 5007 observations from seven batch reactors. Models were evaluated by leave-one-reactor-out cross-validation (LORO-CV). Radial-basis-function support vector regression (SVR-RBF; C = 10, gamma = "scale", epsilon = 0.1) achieved the lowest pooled RMSE (118.09 NmL CH
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