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dc.contributor.author정재식-
dc.contributor.author서장원-
dc.contributor.author임승지-
dc.contributor.author서규원-
dc.contributor.author김경일-
dc.date.accessioned2021-08-31T15:30:02Z-
dc.date.available2021-08-31T15:30:02Z-
dc.date.issued2021-12-
dc.identifier.citationVOL 341, NO 125829-
dc.identifier.issn0960-8524-
dc.identifier.other57285-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/73712-
dc.description.abstractThe stability of dry anaerobic digestion (AD) of food waste (FW) as well as the resulting methane gas generation was investigated from the perspective of system dynamics. Various organic loading rates were applied to the system by modifying the water content in the FW feed and solid retention time (SRT). The excessive organic loading due to the accumulation of volatile fatty acids (VFAs) from the feed with 80% water content during the short SRT (15 and 20 d) caused system failure. In contrast, more intermediate materials, such as VFAs, was easily converted into methane at higher water contents. In addition, the biogas production rate of dry AD was effectively predicted based on SRT, soluble chemical oxygen demand, total VFA, total ammonia, and free ammonia using a recurrent neural network―the so-called “black-box” model. This implies the feasibility of applying this data-based black-box model for controlling and optimizing complex biological processes.-
dc.publisherBioresource technology-
dc.subjectBlack-box model-
dc.subjectDry anaerobic digestion-
dc.subjectFood waste-
dc.subjectLSTM-
dc.subjectRecurrent neural network-
dc.titlePrediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model-
dc.typeArticle-
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