Casagli Francesca, Bernard Olivier
Biocore, Inria Centre at Université Côte d'Azur, INRAE, 2004 Route des Lucioles, 06902 Sophia-Antipolis, France.
LOV (Laboratoire d'Océanographie de Villefranche), Sorbonne Université, CNRS UMR 7093, 181 Chem. du Lazaret, 06230 Villefranche-sur-Mer, France.
Microorganisms. 2022 Jul 26;10(8):1515. doi: 10.3390/microorganisms10081515.
Oxygenation in wastewater treatment leads to a high energy demand. High-rate algal-bacterial ponds (HRABP) have often been considered an interesting solution to reduce this energy cost, as the oxygen is provided by microalgae during photosynthesis. These complex dynamic processes are subject to solar fluxes and consequently permanent fluctuations in light and temperature. The process efficiency therefore highly depends on the location and the period of the year. In addition, the temperature response can be strongly affected by the process configuration (set-up, water depth). Raised pilot-scale raceways are typically used in experimental campaigns, while raceways lying on the ground are the standard reactor configuration for industrial-scale applications. It is therefore important to assess what the consequences are for the temperature patterns of the different reactor configurations and the water levels. The long-term validated algae-bacteria (ALBA) model was used to represent algae-bacteria dynamics in HRABPs. The model was previously validated over 600 days of outdoor measurements, at two different locations and for the four seasons. However, the first version of the model, like all the existing algae-bacteria models, was not fully predictive, since, to be run, it required the measurement of water temperature. The ALBA model was therefore updated, coupling it with a physical model that predicts the temperature evolution in the HRABP. A heat transfer model was developed, and it was able to accurately predict the temperature during the year (with a standard error of 1.5 ∘C). The full predictive model, using the temperature predictions, degraded the model's predictive performances by less than 3%. N2O predictions were affected by ±7%, highlighting the sensitivity of nitrification to temperature The temperature response for two different process configurations were then compared. The biological process can be subjected to different temperature dynamics, with more extreme temperature events when the raceway does not lie on the ground and for thinner depths. Such a situation is more likely to lead to culture crashes.
废水处理中的氧合作用导致了高能量需求。高速率藻菌塘（HRABP）常被视为降低这种能源成本的一个有趣解决方案，因为在光合作用过程中微藻会提供氧气。这些复杂的动态过程受太阳辐射影响，因此光照和温度会持续波动。所以，过程效率高度依赖于地理位置和一年中的时间段。此外，温度响应会受到过程配置（设置、水深）的强烈影响。在实验活动中通常使用升高的中试规模跑道式池塘，而地面上的跑道式池塘是工业规模应用的标准反应器配置。因此，评估不同反应器配置和水位对温度模式的影响很重要。长期验证的藻菌（ALBA）模型被用于描述HRABP中的藻菌动态。该模型先前在两个不同地点经过600天的室外测量以及四季的验证。然而，该模型的第一版和所有现有的藻菌模型一样，并非完全具有预测性，因为运行它需要测量水温。因此，对ALBA模型进行了更新，将其与一个预测HRABP中温度变化的物理模型相结合。开发了一个传热模型，它能够准确预测全年的温度（标准误差为1.5摄氏度）。使用温度预测的完整预测模型使模型的预测性能下降不到3%。N2O预测受到±7%的影响，突出了硝化作用对温度的敏感性。然后比较了两种不同过程配置的温度响应。生物过程可能会经历不同的温度动态，当跑道式池塘不在地面上且深度较浅时，会出现更极端的温度事件。这种情况更有可能导致培养物崩溃。
