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

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

doi:10.1016/j.biortech.2023.128629

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

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

Department of Civil and Environmental Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

This study aimed to predict volatile fatty acids (VFAs) production from SDBS-pretreated waste-activated sludge (WAS). A lab-scale continuous experiment was conducted at varying hydraulic retention times (HRTs) of 7 d to 1 d. The highest VFA yield considering the WAS biodegradability was 86.8 % based on COD at an HRT of 2 d, where the hydrolysis and acidogenesis showed the highest microbial activities. According to 16S rRNA gene analysis, the most abundant bacterial class and genus at an HRT of 2 d were Synergistia and Aminobacterium, respectively. Training regression (R) for TVFA and VFA yield was 0.9321 and 0.9679, respectively, verifying the efficiency of the ANN model in learning the relationship between the input variables and reactor performance. The prediction outcome was verified with R² values of 0.9416 and 0.8906 for TVFA and VFA yield, respectively. These results would be useful in designing, operating, and controlling WAS treatment processes.

Original language	English
Article number	128629
Journal	Bioresource technology
Volume	372
DOIs	https://doi.org/10.1016/j.biortech.2023.128629
Publication status	Published - 2023 Mar

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science & ICT) (No. NRF- 2022M3I3A1094215 ).

Publisher Copyright:
© 2023 Elsevier Ltd

All Science Journal Classification (ASJC) codes

Bioengineering
Environmental Engineering
Renewable Energy, Sustainability and the Environment
Waste Management and Disposal

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.biortech.2023.128629

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title = "Predicting the impact of hydraulic retention time and biodegradability on the performance of sludge acidogenesis using an artificial neural network",

abstract = "This study aimed to predict volatile fatty acids (VFAs) production from SDBS-pretreated waste-activated sludge (WAS). A lab-scale continuous experiment was conducted at varying hydraulic retention times (HRTs) of 7 d to 1 d. The highest VFA yield considering the WAS biodegradability was 86.8 % based on COD at an HRT of 2 d, where the hydrolysis and acidogenesis showed the highest microbial activities. According to 16S rRNA gene analysis, the most abundant bacterial class and genus at an HRT of 2 d were Synergistia and Aminobacterium, respectively. Training regression (R) for TVFA and VFA yield was 0.9321 and 0.9679, respectively, verifying the efficiency of the ANN model in learning the relationship between the input variables and reactor performance. The prediction outcome was verified with R2 values of 0.9416 and 0.8906 for TVFA and VFA yield, respectively. These results would be useful in designing, operating, and controlling WAS treatment processes.",

author = "{Kumar Pandey}, Ashutosh and Jungsu Park and Alice Muhorakeye and Raj Morya and Kim, {Sang Hyoun}",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science & ICT) (No. NRF- 2022M3I3A1094215 ). Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2023",

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doi = "10.1016/j.biortech.2023.128629",

language = "English",

volume = "372",

journal = "Agricultural Wastes",

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AU - Kumar Pandey, Ashutosh

AU - Park, Jungsu

AU - Muhorakeye, Alice

AU - Morya, Raj

AU - Kim, Sang Hyoun

N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science & ICT) (No. NRF- 2022M3I3A1094215 ). Publisher Copyright: © 2023 Elsevier Ltd

PY - 2023/3

Y1 - 2023/3

N2 - This study aimed to predict volatile fatty acids (VFAs) production from SDBS-pretreated waste-activated sludge (WAS). A lab-scale continuous experiment was conducted at varying hydraulic retention times (HRTs) of 7 d to 1 d. The highest VFA yield considering the WAS biodegradability was 86.8 % based on COD at an HRT of 2 d, where the hydrolysis and acidogenesis showed the highest microbial activities. According to 16S rRNA gene analysis, the most abundant bacterial class and genus at an HRT of 2 d were Synergistia and Aminobacterium, respectively. Training regression (R) for TVFA and VFA yield was 0.9321 and 0.9679, respectively, verifying the efficiency of the ANN model in learning the relationship between the input variables and reactor performance. The prediction outcome was verified with R2 values of 0.9416 and 0.8906 for TVFA and VFA yield, respectively. These results would be useful in designing, operating, and controlling WAS treatment processes.

AB - This study aimed to predict volatile fatty acids (VFAs) production from SDBS-pretreated waste-activated sludge (WAS). A lab-scale continuous experiment was conducted at varying hydraulic retention times (HRTs) of 7 d to 1 d. The highest VFA yield considering the WAS biodegradability was 86.8 % based on COD at an HRT of 2 d, where the hydrolysis and acidogenesis showed the highest microbial activities. According to 16S rRNA gene analysis, the most abundant bacterial class and genus at an HRT of 2 d were Synergistia and Aminobacterium, respectively. Training regression (R) for TVFA and VFA yield was 0.9321 and 0.9679, respectively, verifying the efficiency of the ANN model in learning the relationship between the input variables and reactor performance. The prediction outcome was verified with R2 values of 0.9416 and 0.8906 for TVFA and VFA yield, respectively. These results would be useful in designing, operating, and controlling WAS treatment processes.

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

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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