Mihi Miriam, Ouhammou Badr, Aggour Mohammed, Daouchi Brahim, Naaim Soufyane, El Mers El Mahdi, Kousksou Tarik
Faculty of Science of Kenitra, Ibn Tofail University, Morocco.
National School of Applied Sciences, Chouaib Doukkali University, Morocco.
Heliyon. 2024 Sep 28;10(19):e38472. doi: 10.1016/j.heliyon.2024.e38472. eCollection 2024 Oct 15.
Anaerobic digestion (AD) is one of the most extensively accepted processes for organic waste cleanup, and production of both bioenergy and organic fertilizer. Numerous mathematical models have been conceived for modeling the anaerobic process. In this study, a new modified dynamic mathematical model for the simulation of the biochemical and physicochemical processes involved in the AD process for biogas production was proposed. The model was validated, and a sensitivity analysis based on the OAT approach (one-at-a-time) was carried out as a screening technique to identify the most sensitive parameters. The model was developed by updating the bio-chemical framework and including more details concerning the physico-chemical process. The fraction X was incorporated into the model as a particulate inert product arising from biomass decay (inoculum). New components were included to distinguish between the substrate and inoculum, and a surface-based kinetics was used to model the substrate disintegration. Additionally, the sulfate reduction process and hydrogen sulfide production have been included. The model was validated using data extracted from the literature. The model's ability to generate accurate predictions was testified using statistical metrics. The model exhibited excellent performance in forecasting the parameters related to the biogas process, with measurements falling within a reasonable error margin. The relative absolute error (rAE) and root mean square error (RMSE) were both less than 5 %, indicating a high ability of the current model in comparison with the literature. Additionally, the scatter index (SI) was below 10 %, and the Nash-Sutcliffe efficiency (NES) approached one, which affirms the model's accuracy and reliability. Finally, the model was applied to investigate the performances of the AD of food waste (FW). The findings of this study support the robustness of the developed model and its applicability as a virtual platform to evaluate the efficiency of the AD treatment and to forecast biogas production and its quality, CO emission, and energy potential across various organic solid waste types.
厌氧消化（AD）是有机废物净化以及生物能源和有机肥料生产中应用最广泛的工艺之一。人们已经构思了许多数学模型来模拟厌氧过程。在本研究中，提出了一种新的改进动态数学模型，用于模拟沼气生产厌氧消化过程中涉及的生化和物理化学过程。对该模型进行了验证，并采用基于一次改变一个变量（OAT）方法的敏感性分析作为筛选技术，以识别最敏感的参数。该模型是通过更新生化框架并纳入更多关于物理化学过程的细节而开发的。分数X作为生物质衰减（接种物）产生的颗粒状惰性产物被纳入模型。纳入了新的成分以区分底物和接种物，并使用基于表面的动力学对底物分解进行建模。此外，还纳入了硫酸盐还原过程和硫化氢生成过程。使用从文献中提取的数据对该模型进行了验证。通过统计指标证明了该模型生成准确预测的能力。该模型在预测与沼气过程相关的参数方面表现出色，测量值落在合理的误差范围内。相对绝对误差（rAE）和均方根误差（RMSE）均小于5%，表明当前模型与文献相比具有较高的能力。此外，离散指数（SI）低于10%，纳什-萨特克利夫效率（NES）接近1，这证实了该模型的准确性和可靠性。最后，应用该模型研究了食物垃圾（FW）厌氧消化的性能。本研究结果支持了所开发模型的稳健性及其作为虚拟平台的适用性，该平台可用于评估厌氧消化处理的效率，并预测各种有机固体废物类型的沼气产量及其质量、CO排放和能源潜力。
