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

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.

Automated summary

This study aimed to predict VFAs production from SDBS-pretreated WAS. A lab-scale continuous experiment was conducted at varying HRTs. The highest VFA yield was achieved at an HRT of 2 d, where hydrolysis and acidogenesis showed the highest microbial activities. The ANN model demonstrated high efficiency in learning the relationship between input variables and reactor performance, with satisfactory prediction outcomes.