Department of Chemical Engineering, Universidad de Almería, E04120 Almería, Spain; CIESOL Solar Energy Research Centre, Joint Centre University of Almería-CIEMAT, 04120 Almería, Spain.
CIESOL Solar Energy Research Centre, Joint Centre University of Almería-CIEMAT, 04120 Almería, Spain; Department of Informatics, Universidad de Almería, E04120 Almería, Spain.
Water Res. 2024 Jan 1;248:120837. doi: 10.1016/j.watres.2023.120837. Epub 2023 Nov 4.
Modelling microalgae-bacteria in wastewater treatment systems has gained significant attention in the last few years. In this study, we present an enhanced version of the ABACO model, named ABACO-2, which demonstrates improved accuracy through validation in outdoor pilot-scale systems. ABACO-2 enables the comprehensive characterization of microalgae-bacteria consortia dynamics, allowing to predict the biomass concentration (microalgae, heterotrophic bacteria, and nitrifying bacteria) and nutrient evolution. The updated version of the model incorporates new equations for nutrient coefficient yields, oxygen mass balance, and microorganism cellular decay, while significantly reducing the number of calibrated parameters, simplifying the parameter identification. Calibration and validation were performed using data from a 80 m raceway reactor operated in a semicontinuous mode over an extensive period (May to November, total of 206 days) at a fixed dilution rate of 0.2 day (corresponding to 5 days of hydraulic retention time), where untreated urban wastewater was used as culture medium. ABACO-2 exhibited robustness, accurately forecasting biomass production, population dynamics, nutrient recovery, and prevailing culture conditions across a wide range of environmental and water composition conditions. Mathematical models are essential instruments for the industrial development and optimization of microalgae-related wastewater treatment processes, thereby contributing to the sustainability of the wastewater treatment industry.
在过去几年中,对污水处理系统中的微藻-细菌建模引起了极大的关注。在这项研究中,我们提出了 ABACO 模型的增强版本,称为 ABACO-2,该模型在户外中试规模系统中的验证表明其准确性得到了提高。ABACO-2 能够全面描述微藻-细菌共生体的动态,从而可以预测生物质浓度(微藻、异养细菌和硝化细菌)和营养物质的演变。该模型的更新版本包含了新的营养系数产率、氧质量平衡和微生物细胞衰减方程,同时大大减少了校准参数的数量,简化了参数识别。使用在固定稀释率为 0.2 天(相当于水力停留时间 5 天)下以未处理的城市废水为培养基的 80 m 跑道式反应器在广泛的时间段(5 月至 11 月,共 206 天)中以半连续模式运行的数据进行了模型的校准和验证。ABACO-2 表现出了稳健性,能够准确地预测生物质的产生、种群动态、营养物质的回收以及各种环境和水质条件下的流行培养条件。数学模型是微藻相关废水处理工艺的工业发展和优化的重要工具,从而有助于废水处理行业的可持续发展。