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, (HRT) and (HRT) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLR of 5.24 g VS·d·L ) and the processing of highly fermentable substrates (average feed volatile solids (VS) of 81.35 g L). The model-based ranking of AD parameters demonstrated that the HRT, the VS, and the target temperature (T) 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.

中文翻译：

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厌氧消化操作参数对启动持续时间的相关性和影响：稳健启动指南的数据库构建

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在全球范围内变得越来越普遍，研究工作主要集中在优化其流程上。 AD系统运行过程中，不稳定事件的发生是不可避免的。到目前为止，关于初创企业扰动的驱动因素的全面和实验室规模的研究已经得出了许多结论。然而，初创研究中报告的标准化实践的缺乏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数据以协助启动规划和监控的可能性。因此，构建了一个标准化数据库，参考 31 项研究中的 75 个一级湿式连续搅拌釜反应器 (CSTR) 处理农业、工业或城市有机废水的单一消化启动案例。该数据库中包含的总观测值的 10% 涉及失败的启动实验。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洞察。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（HRT）和（HRT）分别为 26.25 天和 20.6 天）、高平均有机负荷率（平均OLR 为 5.24 g VS·d·L ）和高度可发酵底物的加工（平均饲料挥发性固体 (VS) 为 81.35 g L）。基于模型的 AD 参数排序表明，HRT、VS 和目标温度 (T) 对启动持续时间的影响最大，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比较的参考，从而确定应严格控制的因素。