Abstract：
Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d-1·L -1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L-1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.
摘要：
厌氧消化（AD）已成为有机废物管理的流行技术，同时具有经济和环境优势。随着AD在世界范围内越来越普遍，研究工作主要集中在优化其流程上。在AD系统运行期间，不稳定事件的发生是不可避免的。到目前为止,关于启动扰动的驱动因素，从全面和实验室规模的研究中得出了许多结论。然而,初创企业研究报告中缺乏标准化的做法，这引起了人们对所获得数据的可比性和可靠性的担忧。本研究旨在开发一个知识数据库，并研究将机器学习技术应用于实验提取的数据以协助启动计划和监控的可能性。因此，一个标准化的数据库，引用了75个一级湿式连续搅拌釜反应器（CSTR）加工农业启动案例，工业,构建了31项研究的单消化或市政有机废水。该数据库中包含的总观察结果的10％与启动实验失败有关。然后，使用多变量分析和基于模型的排名方法研究了参数之间的相关性及其对启动持续时间的影响。通过数据库的相关性分析，突出了对选择趋势的见解。因此，发现有利于短启动持续时间的方案涉及相对较低的保留时间(平均初始和最终水力保留时间，（HRTi）和（HRTf）分别为26.25天和20.6天，分别)，高平均有机负载率（平均OLR平均值为5.24gVS·d-1·L-1）和高度可发酵底物的处理（平均进料挥发性固体（VSfeed）为81.35gL-1）。基于模型的AD参数排序表明，HRTf，VSceed,目标温度(Tf)对启动持续时间的影响最强，在评估的AD参数中获得最高的相对分数。该数据库可以作为未来启动研究的比较目的的参考，从而可以确定应严格控制的因素。
