How to model the floculation process?
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Flocculation can be modeled using a combination of computational fluid dynamics (CFD) and population balance models (PBM) . Machine learning algorithms can also be used to model flocculation processes, especially in cases where there is a lack of fundamental understanding and real-time measurement methods . Logistic growth theory can be employed to model the time evolution of particle size or concentration in flocculation experiments . A hybrid systematic model-based framework that integrates computational methods and stochastic modeling approaches can be used for monitoring and control of the flocculation process . Additionally, a new flocculation model has been proposed that considers the influence of retention time, which is particularly important for low retention time flocculators .
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| Papers (5) | Insight |
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| The paper proposes a new flocculation model that considers the influence of retention time in flocculation processes, specifically for low retention time hydraulic flocculators. The model is presented in Equation 4 and includes terms related to particle aggregation and oc breakup. | |
01 Jan 2020 6 Citations | The paper proposes a hybrid systematic model-based framework that integrates computational methods in chemistry and stochastic modeling approaches for monitoring and controlling the flocculation process above microscale. |
| The paper describes a model based on logistic growth theory that can be used to model the time evolution of floc size or particle concentration in flocculation processes. | |
01 Jan 2021 3 Citations | The paper demonstrates the use of a hybrid modelling framework that combines first-principles models with machine learning algorithms to model the flocculation process. |
9 Citations | The paper proposes a combined model of computational fluid dynamics (CFD) and the population balance model (PBM) to simulate the flow characteristics and floc behavior in a full-scale flocculation process. |
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