Predicting the impact of hydraulic retention time and biodegradability on the performance of sludge acidogenesis using an artificial neural network.

Kumar Pandey, Ashutosh; Park, Jungsu; Muhorakeye, Alice; Morya, Raj; Kim, Sang-Hyoun

Kumar Pandey, Ashutosh; Park, Jungsu; Muhorakeye, Alice; Morya, Raj; Kim, Sang-Hyoun.

Afiliação

Kumar Pandey A; Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Park J; Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Muhorakeye A; Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Morya R; Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Kim SH; Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea. Electronic address: sanghkim@yonsei.ac.kr.

Bioresour Technol ; 372: 128629, 2023 Mar.

Article em En | MEDLINE | ID: mdl-36646359

Assuntos

Ácidos Graxos Voláteis; Esgotos; Esgotos/microbiologia; Fermentação; RNA Ribossômico 16S/genética; Bactérias/genética; Concentração de Íons de Hidrogênio; Reatores Biológicos

Palavras-chave

Acidogenic fermentation; Artificial neural network; Microbial analysis; Volatile fatty acids; Waste-activated sludge

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esgotos / Ácidos Graxos Voláteis Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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