GEMMA - Group of Environmental Engineering and Microbiology, Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya - BarcelonaTech, c/Jordi Girona, 1-3, Building D1, E-08034 Barcelona, Spain.
Civil and Environmental Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93407, United States.
Sci Total Environ. 2017 Dec 1;601-602:646-657. doi: 10.1016/j.scitotenv.2017.05.215. Epub 2017 May 31.
An integral mechanistic model describing the complex interactions in mixed algal-bacterial systems was developed. The model includes crucial physical, chemical and biokinetic processes of microalgae as well as bacteria in wastewater. Carbon-limited microalgae and autotrophic bacteria growth, light attenuation, photorespiration, temperature and pH dependency are some of the new features included. The model named BIO_ALGAE was built using the general formulation and structure of activated sludge models (ASM), and it was implemented in COMSOL Multiphysics™ platform. Calibration and validation were conducted with experimental data from two identical pilot HRAPs receiving real wastewater. The model was able to simulate the dynamics of different components in the ponds, and to predict the relative proportion of microalgae (58-68% in average of total suspended solids (TSS) and bacteria (30-20% in average of TSS). Microalgae growth resulted strongly influenced by the light factor f(I), decreasing microalgae concentrations from 40 to 60%. Furthermore, reducing the influent organic matter concentration of 50% and 70%, model predictions indicated that microalgae production increased from (8.7gTSSmd to 13.5gTSSmd) due to the new distribution of particulate components. The proposed model could be an efficient tool for industry to predict the production of microalgae, as well as to design and optimize HRAPs.
开发了一种综合的机制模型,用于描述混合藻-细菌系统中的复杂相互作用。该模型包括废水中微藻和细菌的关键物理、化学和生物动力学过程。其中包括一些新的特征,如碳限制的微藻和自养细菌生长、光衰减、光呼吸、温度和 pH 依赖性。该模型名为 BIO_ALGAE,使用活性污泥模型 (ASM) 的通用公式和结构构建,并在 COMSOL Multiphysics™ 平台上实现。使用来自接收实际废水的两个相同的 HRAP 实验数据进行了校准和验证。该模型能够模拟池塘中不同成分的动态,并预测微藻(总悬浮固体(TSS)的 58-68%)和细菌(TSS 的 30-20%)的相对比例。微藻的生长受到光因子 f(I)的强烈影响,使微藻浓度从 40%降低到 60%。此外,当将进水有机物浓度降低 50%和 70%时,模型预测表明,由于颗粒成分的新分布,微藻产量从(8.7gTSSmd 增加到 13.5gTSSmd)。该模型可以成为工业界预测微藻生产以及设计和优化 HRAP 的有效工具。