Wu Yiran, Kovalovszki Adam, Pan Jiahao, Lin Cong, Liu Hongbin, Duan Na, Angelidaki Irini
1College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083 China.
2Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
Biotechnol Biofuels. 2019 May 3;12:106. doi: 10.1186/s13068-019-1442-7. eCollection 2019.
Monitoring and providing early warning are essential operations in the anaerobic digestion (AD) process. However, there are still several challenges for identifying the early warning indicators and their thresholds. One particular challenge is that proposed strategies are only valid under certain conditions. Another is the feasibility and universality of the detailed threshold values obtained from different AD systems. In this article, we report a novel strategy for identifying early warning indicators and defining threshold values via a combined experimental and simulation approach.
The AD of corn stalk (CS) was conducted using mesophilic, completely stirred anaerobic reactors. Two overload modes (organic and hydraulic) and overload types (sudden and gradual) were applied in order to identify early warning indicators of the process and determine their threshold values. To verify the selection of experimental indicators, a combined experimental and simulation approach was adopted, using a modified anaerobic bioconversion mathematical model (BioModel). Results revealed that the model simulations agreed well with the experimental data. Furthermore, the ratio of intermediate alkalinity to bicarbonate alkalinity (IA/BA) and volatile fatty acids (VFAs) were selected as the most potent early warning indicators, with warning times of 7 days and 5-8 days, respectively. In addition, IA, BA, and VFA/BA were identified as potential auxiliary indicators for diagnosing imbalances in the AD system. The relative variations for indicators based on that of steady state were observed instead of the absolute threshold values, which make the early warning more feasible and universal.
The strategy of a combined approach presented that the model is promising tool for selecting and monitoring early warning indicators in various corn stalk AD scenarios. This study may offer insight into industrial application of early warning in AD system with mathematical model.
监测和提供早期预警是厌氧消化（AD）过程中的重要操作。然而，识别早期预警指标及其阈值仍存在若干挑战。一个特别的挑战是，所提出的策略仅在特定条件下有效。另一个挑战是从不同AD系统获得的详细阈值的可行性和通用性。在本文中，我们报告了一种通过实验和模拟相结合的方法来识别早期预警指标并定义阈值的新策略。
使用中温、完全搅拌的厌氧反应器对玉米秸秆（CS）进行厌氧消化。应用了两种过载模式（有机和水力）和过载类型（突然和逐渐），以识别该过程的早期预警指标并确定其阈值。为了验证实验指标的选择，采用了实验和模拟相结合的方法，使用了改进的厌氧生物转化数学模型（BioModel）。结果表明，模型模拟与实验数据吻合良好。此外，选择中间碱度与碳酸氢盐碱度之比（IA/BA）和挥发性脂肪酸（VFA）作为最有效的早期预警指标，预警时间分别为7天和5 - 8天。此外，IA、BA和VFA/BA被确定为诊断AD系统失衡的潜在辅助指标。观察的是基于稳态的指标相对变化而非绝对阈值，这使得早期预警更具可行性和通用性。
所提出的组合方法策略表明，该模型是在各种玉米秸秆AD场景中选择和监测早期预警指标的有前途的工具。本研究可能为利用数学模型在AD系统中进行早期预警的工业应用提供见解。
