Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 32, Piazza L. da Vinci, 20133 Milan, Italy.
Institut National de Recherche en Informatique et en Automatique (INRIA), Biocore, Université Cote d'Azur, 2004, Route des Lucioles - BP 93, 06902 Sophia-Antipolis, France.
Environ Sci Technol. 2021 Mar 16;55(6):3940-3955. doi: 10.1021/acs.est.0c05264. Epub 2021 Mar 3.
The first objective of this study is to assess the predictive capability of the ALBA (ALgae-BActeria) model for a pilot-scale (3.8 m) high-rate algae-bacteria pond treating agricultural digestate. The model, previously calibrated and validated on a one-year data set from a demonstrative-scale raceway (56 m), successfully predicted data from a six-month monitoring campaign with a different wastewater (urban wastewater) under different climatic conditions. Without changing any parameter value from the previous calibration, the model accurately predicted both online monitored variables (dissolved oxygen, pH, temperature) and off-line measurements (nitrogen compounds, algal biomass, total and volatile suspended solids, chemical oxygen demand). Supported by the universal character of the model, different scenarios under variable weather conditions were tested, to investigate the effect of key operating parameters (hydraulic retention time, pH regulation, ka) on algae biomass productivity and nutrient removal efficiency. Surprisingly, despite pH regulation, a strong limitation for inorganic carbon was found to hinder the process efficiency and to generate conditions that are favorable for NO emission. The standard operating parameters have a limited effect on this limitation, and alkalinity turns out to be the main driver of inorganic carbon availability. This investigation offers new insights in algae-bacteria processes and paves the way for the identification of optimal operational strategies.
本研究的首要目标是评估 ALBA（藻类-细菌）模型在一个中试规模（3.8 米）高负荷藻类-细菌塘中处理农业消化物的预测能力。该模型之前已经在一个示范规模的（56 米）养殖槽的一年数据集上进行了校准和验证，成功预测了在不同气候条件下使用不同废水（城市废水）进行的六个月监测期间的数据。在不改变以前校准中任何参数值的情况下，模型准确地预测了在线监测变量（溶解氧、pH 值、温度）和离线测量（氮化合物、藻类生物量、总悬浮固体和挥发性悬浮固体、化学需氧量）。模型的通用性得到了支持，测试了不同天气条件下的不同情景，以研究关键操作参数（水力停留时间、pH 值调节、ka）对藻类生物量生产力和养分去除效率的影响。令人惊讶的是，尽管进行了 pH 值调节，但发现无机碳的强烈限制严重阻碍了过程效率，并产生了有利于 NO 排放的条件。标准操作参数对这种限制的影响有限，而碱度是无机碳可用性的主要驱动因素。这项研究为藻类-细菌过程提供了新的见解，并为确定最佳操作策略铺平了道路。
