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Experimental power of laboratory-scale results and transferability to full-scale anaerobic digestion
Author(s) -
Maximilian Lüdtke,
Åke Nordberg,
Christian Baresel
Publication year - 2017
Publication title -
water science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.406
H-Index - 137
eISSN - 1996-9732
pISSN - 0273-1223
DOI - 10.2166/wst.2017.290
Subject(s) - anaerobic digestion , transferability , scale (ratio) , environmental science , process engineering , full scale , scale up , yield (engineering) , methane , waste management , biochemical engineering , environmental engineering , pulp and paper industry , engineering , statistics , chemistry , mathematics , materials science , physics , logit , structural engineering , quantum mechanics , classical mechanics , metallurgy , organic chemistry
Anaerobic digestion is today internationally acknowledged as an environmentally sound process for energy and nutrient recovery from organic wastes, and it is the dominant sludge treatment technology in most countries' wastewater treatment plants. Laboratory- or pilot-scale experiments are commonly used as a first step to investigate the potential of new ideas or to confirm research hypothesis before confirmation in full-scale. The objectives of this study were to investigate transferability of methane yield assessments between laboratory- and full-scale, and to compare the influence of experimental uncertainties on experimental power in parallel continuous digester experiments for the two scales. Both batch experiment data (used in a simple equation), as well as continuous laboratory experiments, could be used to predict full-scale methane yield with a high accuracy (<5% difference). Full-scale digesters significantly outperformed hand-fed laboratory digesters in terms of experimental power regarding relative differences in methane yield between two digesters operated in parallel. However, to justify costly long-term continuous laboratory-scale experiments with sufficient experimental power and potentially high transferability, resources also have to be allocated to measures that ensure a high data quality from full-scale reference facilities.

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