Machine learning vs.ADM1:Reliable biogas prediction with minimal data requirements in full-scale plantsOA
Anaerobic digestion harnesses microbial processes to convert organic wastes into renewable biogas,offering a sustainable pathway for energy production.In agricultural settings,biogas plants often codigest livestock manure with crop residues,yet seasonal variations in feedstock quality introduce fluctuations that challenge process stability and yield optimization.Mechanistic models such as the Anaerobic Digestion Model No.1(ADM1)provide detailed biochemical simulations but require extensive substrate characterization,limiting their practicality for full-scale operations.Here we show that a simplified ADM1,alongside machine learning approaches-random forest and long short-term memory(LSTM)networks-achieves comparable accuracy in predicting daily biogas and methane production from a full-scale plant over 2023-2024.All models yielded Nash-Sutcliffe efficiencies above 0.78,with random forest excelling when incorporating feedstock quantities and maize silage volatile solids.While LSTM proved effective even with minimal inputs,it incurred a training time 141 times greater than ADM1,highlighting critical trade-offs in computational efficiency.These findings advance hybrid modelling strategies for real-time monitoring,enabling operators to balance predictive precision with data requirements to enhance renewable energy integration and agricultural sustainability.
Sofia Tisocco;Soren Weinrich;Henrik Bjarne Møller;Alastair James Ward;Liam Kilmartin;Xinmin Zhan;Paul Crosson
Civil Engineering,School of Engineering,University of Galway,Galway,H91 TK33,Ireland Teagasc Animal and Bioscience Research Department,Animal and Grassland Research and Innovation Centre,Dunsany,C15 PW93,IrelandFaculty of Energy⋅Building Services⋅Environmental Engineering,Münster University of Applied Sciences,Stegerwaldstraße 39,48565,Steinfurt,Germany Biochemical Conversion Department,Deutsches Biomasseforschungszentrum gemeinnützige GmbH,Torgauer Straße 116,Leipzig,04347,GermanyDepartment of Biological and Chemical Engineering,Aarhus University,Blichers All�e 20,Tjele 8830,DenmarkDepartment of Biological and Chemical Engineering,Aarhus University,Blichers All�e 20,Tjele 8830,DenmarkElectrical and Electronic Engineering,School of Engineering,University of Galway,Galway,H91 TK33,IrelandCivil Engineering,School of Engineering,University of Galway,Galway,H91 TK33,Ireland Ryan Institute,University of Galway,Galway,H91 TK33,Ireland MaREI Research Centre for Energy,Climate and Marine,Ryan Institute,University of Galway,Galway,H91 TK33,IrelandTeagasc Animal and Bioscience Research Department,Animal and Grassland Research and Innovation Centre,Dunsany,C15 PW93,Ireland
农业科技
ADM1Anaerobic digestionBiogas technologyFeature importanceMachine learningParameter estimation
《Environmental Science and Ecotechnology》 2026 (1)
P.108-118,11
financed by the Teagasc Walsh Scholarship Programme(Ref:2021010).
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