EnBiChem Research Group, Department of Industrial Engineering and Technology, University College West Flanders, Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium.
Environ Technol. 2009 Dec 14;30(14):1575-84. doi: 10.1080/09593330903358278.
Modelling is increasingly used for optimizing environmental treatment processes such as anaerobic digestion. It allows problems such as instability of the process to be solved by predicting various scenarios. The anaerobic digestion model No. 1 (ADM1) is accepted worldwide as the standard model for the description of anaerobic digestion. However, it is sophisticated and complex, so it is not user friendly. Therefore, a mathematical method was developed that allows the calculation of the reactor pH, as well as the biogas flow rate (Q) and composition (expressed as the CO2 partial pressure, pCO2), based on a small number of widely available analyses such as chemical oxygen demand and total organic carbon. Furthermore, the ADM1 model was originally designed for anaerobic digestion of wastewater. In this work, the ADM1 model is evaluated for the first time for application in the modelling of solid waste digestion. This evaluation was performed in two steps. First, a list of experimentally available lab-scale data (pH and Q) was grouped according to the composition and origin of the treated solid waste (e.g. manure or vegetable waste). For each group the developed model for the calculation of pH, Q and pCO2 was calibrated with this lab-scale data. After calibration, the model was validated with additional experimental results. It could be demonstrated statistically that the model was able to predict the experimental results, although the confidence region was rather large.
建模越来越多地用于优化环境处理过程，例如厌氧消化。它可以通过预测各种情况来解决过程不稳定等问题。厌氧消化模型 1（ADM1）被全世界公认为描述厌氧消化的标准模型。然而，它很复杂，因此不便于使用。因此，开发了一种数学方法，该方法可以根据少数几种广泛可用的分析（例如化学需氧量和总有机碳）来计算反应器 pH 值以及沼气流量（Q）和组成（表示为二氧化碳分压，pCO2）。此外，ADM1 模型最初是为废水的厌氧消化而设计的。在这项工作中，首次对 ADM1 模型进行了评估，以应用于固体废物消化的建模。该评估分两步进行。首先，根据处理的固体废物的组成和来源（例如粪便或蔬菜废物）将实验上可获得的实验室规模数据（pH 和 Q）列表进行分组。对于每个组，使用此实验室规模数据对用于计算 pH、Q 和 pCO2 的开发模型进行校准。校准后，使用其他实验结果对模型进行验证。可以从统计学上证明该模型能够预测实验结果，尽管置信区间相当大。
