使用人工神经网络预测水力停留时间和可生物降解性对污泥产酸性能的影响。
Predicting the impact of hydraulic retention time and biodegradability on the performance of sludge acidogenesis using an artificial neural network.
机构信息
Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
出版信息
Bioresour Technol. 2023 Mar;372:128629. doi: 10.1016/j.biortech.2023.128629. Epub 2023 Jan 13.
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 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.
本研究旨在预测 SDBS 预处理废活性污泥（WAS）中挥发性脂肪酸（VFAs）的生成。在不同水力停留时间（HRT）为 7 d 至 1 d 的条件下进行了实验室规模的连续实验。在 HRT 为 2 d 时，基于 COD，考虑到 WAS 的可生物降解性，VFAs 的最高产率为 86.8%，其中水解和产酸显示出最高的微生物活性。根据 16S rRNA 基因分析，在 HRT 为 2 d 时，最丰富的细菌纲和属分别为协同菌和氨菌。总挥发性脂肪酸（TVFA）和 VFA 产率的训练回归（R）分别为 0.9321 和 0.9679，验证了 ANN 模型在学习输入变量与反应器性能之间关系方面的效率。TVFA 和 VFA 产率的预测结果的 R 值分别为 0.9416 和 0.8906，验证了结果的准确性。这些结果将有助于设计、操作和控制 WAS 处理过程。
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