Application of a simplified ADM1 for full-scale anaerobic co-digestion of cattle slurry and grass silage: assessment of input variability

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作者
Sofia Tisocco
Sören Weinrich
Gary Lyons
Michael Wills
Xinmin Zhan
Paul Crosson
机构
[1] University of Galway,Civil Engineering, College of Science and Engineering
[2] Animal and Grassland Research and Innovation Centre,Teagasc Animal and Bioscience Research Department
[3] Deutsches Biomasseforschungszentrum gemeinnützige GmbH,Biochemical Conversion Department
[4] Münster University of Applied Sciences,Faculty of Energy · Building Services · Environmental Engineering
[5] Agri-Food and Biosciences Institute,Ryan Institute
[6] University of Galway,MaREI Research Centre for Energy, Climate and Marine, Ryan Institute
[7] University of Galway,undefined
关键词
ADM1; Agricultural feedstocks; Biogas technology; Input variability; Parameter estimation;
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摘要
Mathematical modeling of anaerobic digestion is a powerful tool to predict gas yields and optimize the process. The Anaerobic Digestion Model No. 1 (ADM1) is a widely implemented model for this purpose. However, modeling full-scale biogas plants is challenging due to the extensive substrate and parameter characterization required. This study describes the modification of the ADM1 through a simplification of individual process phases, characteristic components and required parameters. Consequently, the ability of the simplified model to simulate the co-digestion of grass silage and cattle slurry was evaluated using data from a full-scale biogas plant. The impacts of substrate composition (crude carbohydrate, protein and lipid concentration) and variability of carbohydrate degradability on simulation results were assessed to identify the most influential parameters. Results indicated that the simplified version was able to depict biogas and biomethane production with average model efficiencies, according to the Nash-Sutcliffe efficiency (NSE) coefficient, of 0.70 and 0.67, respectively, and was comparable to the original ADM1 (average model efficiencies of 0.71 and 0.63, respectively). The variability of crude carbohydrate, protein and lipid concentration did not significantly impact biogas and biomethane output for the data sets explored. In contrast, carbohydrate degradability seemed to explain much more of the variability in the biogas and methane production. Thus, the application of simplified models provides a reliable basis for the process simulation and optimization of full-scale agricultural biogas plants.
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