CALAGUA - Unidad Mixta UV-UPV, Departament d'Enginyeria Química, Universitat de València, Avinguda de la Universitat s/n, 46100 Burjassot, València, Spain.
CALAGUA - Unidad Mixta UV-UPV, Institut Universitari d'Investigació d'Enginyeria de l'Aigua i Medi Ambient - IIAMA, Universitat Politècnica de València, Camí de Vera s/n, 46022, València, Spain.
Sci Total Environ. 2023 Aug 1;884:163669. doi: 10.1016/j.scitotenv.2023.163669. Epub 2023 May 4.
A mechanistic model describing the key interactions occurring in microalgae-bacteria consortia systems was developed and validated. The proposed model includes the most relevant features of microalgae, such as light dependence, endogenous respiration, growth, and nutrient consumption for different nutrient sources. The model is coupled to the plant-wide model BNRM2, including heterotrophic and nitrifying bacteria, and chemical precipitation processes, among others. A major novelty of the model is microalgae growth inhibition by nitrite. Validation was conducted using experimental data from a pilot-scale membrane photobioreactor (MPBR) fed with permeate from an anaerobic membrane bioreactor (AnMBR). Three experimental periods dealing with different interactions between nitrifying bacteria and microalgae were validated. The model was able to accurately represent the dynamics occurring in the MPBR, predicting the relative abundance of microalgae and bacteria over time. Specifically, >500 pairs of experimental and modeled data were evaluated, giving an average R coefficient of 0.9902. The validated model was also used to evaluate different offline control strategies for enhancing process performance. For example, partial-nitrification resulting in NO-N accumulation (i.e., microalgae growth inhibition) could be avoided by increasing biomass retention time from 2.0 to 4.5 days. It has been also concluded that microalgae biomass growth rate could be also enhanced by punctually increasing the dilution rate, allowing to outcompete nitrifying bacteria.
开发并验证了一个描述微藻-细菌共生系统中关键相互作用的机理模型。所提出的模型包括微藻的最相关特征,如光依赖性、内呼吸、生长和不同营养源的营养消耗。该模型与包括异养细菌和硝化细菌以及化学沉淀过程等在内的全流程模型 BNRM2 耦合。模型的一个主要新颖之处是亚硝酸盐对微藻生长的抑制。使用来自厌氧膜生物反应器 (AnMBR) 渗透物进料的中试规模膜光生物反应器 (MPBR) 的实验数据进行了验证。验证了涉及硝化细菌和微藻之间不同相互作用的三个实验阶段。该模型能够准确地表示 MPBR 中的动态变化,预测微藻和细菌随时间的相对丰度。具体来说,评估了超过 500 对实验和模型数据,平均 R 系数为 0.9902。经过验证的模型还用于评估不同的离线控制策略以提高工艺性能。例如,通过将生物质停留时间从 2.0 天增加到 4.5 天,可以避免部分硝化导致的 NO-N 积累(即微藻生长抑制)。还得出结论,通过定期增加稀释率也可以提高微藻生物质生长速率,从而能够与硝化细菌竞争。