Dittmer Celina, Krümpel Johannes, Lemmer Andreas
State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim, Garbenstrasse 9, 70599 Stuttgart, Germany.
Microorganisms. 2021 Feb 5;9(2):324. doi: 10.3390/microorganisms9020324.
Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14-18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate's composition.
未来的沼气厂必须能够按需生产沼气，这需要积极的进料管理。因此，假定要对取决于底物供应的沼气产量进行模拟。大多数模拟模型基于复杂的1号厌氧消化模型（ADM1）。ADM1针对所有生化和物理化学过程步骤包含大量参数，必须仔细调整这些参数以反映各个全规模沼气厂的条件。由于缺乏可靠的测量技术和过程监测，几乎没有这些参数可用于全规模工厂。目前的研究调查展示了一个模拟模型，该模型基于时间序列分析原理，仅使用沼气生成和固体底物供应的历史数据，而不区分单个底物。对该模型进行的366次模拟、时长为48小时的广泛评估结果显示，平均绝对百分比误差（MAPE）为14%至18%。该评估基于两种不同的消化器，并证明该模型具有自学习能力，能够自动适应各自的应用，与底物组成无关。
