Predicting the impact of hydraulic retention time and biodegradability on the performance of sludge acidogenesis using an artificial neural network. (March 2023)

• Record Type: Journal Article
• Authors: Kumar Pandey, Ashutosh; Park, Jungsu; Muhorakeye, Alice; Morya, Raj; Kim, Sang-Hyoun
• Is Part Of: Bioresource technology. Volume 372(2023)
• Publication Date: 2023-03
• Subjects: Acidogenic fermentation -- Volatile fatty acids -- Microbial analysis -- Waste-activated sludge -- Artificial neural network; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2023.128629
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: VFA production from waste activated sludge was conducted at HRT ranging 7 to 1 d. The highest VFA yield of 0.868 g COD/g bCOD was achieved at HRT 2 d. A back-propagation ANN model predicted the VFA production yield with R 2 of 0.9416. The optimized condition showed the highest hydrolytic and acidogenic activities. Bacterial population was dominated by class Synergistia and genus Aminobacterium. 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 R 2 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.