GENOCOV Research Group, Department of Chemical, Biological and Environmental Engineering, Escola d'Enginyeria, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain.
GENOCOV Research Group, Department of Chemical, Biological and Environmental Engineering, Escola d'Enginyeria, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain.
Chemosphere. 2023 Oct;339:139605. doi: 10.1016/j.chemosphere.2023.139605. Epub 2023 Jul 22.
In the present study, the stoichiometry of the Sulphur Oxidizing-Nitrate Reducing (SO-NR) process, with a focus on Partial Autotrophic Denitrification (PAD), has been evaluated through a thermodynamic-based study whereas a model-based approach has been adopted to assess process kinetics. Experimental data on process performance and biomass yields were available from a previous work achieving efficient PAD, where a biomass yield of 0.113 gVSS/gS was estimated. First, the free Gibbs energy dissipation method has been implemented, in order to provide a theoretical framework exploring the boundaries for sulphur oxidizing biomass yields. Second, a screening of available mathematical models describing SO-NR process was conducted and five published models were selected, in order to assess the most suitable model structure for describing the observed PAD kinetics. To the best of our knowledge, none of reported biomass yields are estimated in systems operating PAD as the main process and, analogously, none of the proposed models have been applied to case studies aiming at partial denitrification only. The work showed that the very low biomass yield of 0.117 ± 0.007 gVSS/gS, observed in a PAD system in our previous work, suggests that the conditions applied to achieve partial denitrification resulted in a high energy-dissipating metabolism compared to complete denitrification applications. Models' analysis revealed that nitrite accumulation can be described by a classical Monod kinetics if different μ are adopted for each intermediate reaction, with Theil Inequality Coefficient values lower than 0.21 for both NO and NO. Nonetheless, adopting Haldane-type kinetics for nitrite uptake inferred higher identifiability to the model structure, resulting in confidence intervals below ±10% for all the parametric estimations. The thermodynamic and modelling outcomes support the experimental results obtained in the reference study and the critical comparison of model suitability to represent PAD process is believed pivotal to pave the way to its real-scale implementation.
在本研究中,通过基于热力学的研究评估了硫氧化-硝酸盐还原(SO-NR)过程的化学计量比,重点是部分自养反硝化(PAD),而采用基于模型的方法来评估过程动力学。先前的工作中提供了有关过程性能和生物量产率的实验数据,该工作实现了高效的 PAD,估计生物量产率为 0.113 gVSS/gS。首先,实施了自由 Gibbs 能耗散方法,以便为探索硫氧化生物量产率边界提供理论框架。其次,对描述 SO-NR 过程的可用数学模型进行了筛选,并选择了五个已发表的模型,以评估最适合描述观察到的 PAD 动力学的模型结构。据我们所知,在以 PAD 作为主要过程运行的系统中,没有报告估计的生物量产率,同样,没有提出的模型应用于仅部分反硝化的案例研究。研究表明,在我们之前的工作中,在 PAD 系统中观察到的非常低的生物量产率 0.117±0.007 gVSS/gS,表明为实现部分反硝化而施加的条件与完全反硝化应用相比导致了高能量耗散代谢。模型分析表明,如果对每个中间反应采用不同的 μ,则可以用经典的 Monod 动力学描述亚硝酸盐积累,对于 NO 和 NO,Theil 不相等系数值低于 0.21。然而,对于亚硝酸盐摄取采用 Haldane 型动力学推断对模型结构具有更高的可识别性,导致所有参数估计的置信区间低于±10%。热力学和建模结果支持参考研究中获得的实验结果,并且认为模型适合性的关键比较对于代表 PAD 过程铺平道路至关重要。