用于生物除磷的序批式反应器(SBR)的动态分区模型

S. Daneshgar, Sina Borzooei, Lukas Debliek, Elias Van Den Broeck, Riet Cornelissen, Piet de Langhe, Cesare Piacezzi, Miguel Daza, S. Duchi, U. Rehman, I. Nopens, E. Torfs
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引用次数: 0

摘要

生物反应器通常被模拟为连续搅拌罐反应器(CSTR)或串联的连续搅拌罐反应器(串联罐配置)。在具有非理想混合的大型系统中,这种方法无法充分捕捉复杂的流体力学,导致模型因空间梯度的叠加而不准确。高度详细的计算流体动力学(CFD)模型可以深入了解复杂的流体动力学,但对于流表模型和数字孪生应用来说,计算成本过于昂贵。隔室模型(CM)可以提供更逼真的流体动力学表现,同时计算成本也不高,是一种中间方案。然而,在不同的流动条件下,工厂的流体力学可能会有很大不同。动态 CM 可以优雅地捕捉这些变化。迄今为止,CMs 的应用主要局限于连续流系统。在本研究中,为生物除磷工艺开发了一个序批式反应器(SBR)的动态 CM。由于 SBR 的运行阶段各不相同,这给 CM 开发带来了挑战。在溶解氧和磷酸盐预测方面,动态 CM 比 CSTR 模型(使用相同的生物动力学参数)有明显改善,减少了对模型重新校准的需求,而重新校准可能导致模型过度拟合和有限的外推能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dynamic compartmental model of a sequencing batch reactor (SBR) for biological phosphorus removal
Bioreactors are usually modelled as continuous stirred tank reactors (CSTRs) or CSTRs connected in series (Tanks-In-Series configuration). In large systems with non-ideal mixing, such approaches do not sufficiently capture the complex hydrodynamics, leading to model inaccuracies due to the lumping of spatial gradients. Highly detailed computational fluid dynamics (CFD) models provide insight into complex hydrodynamics but are computationally too expensive for flow-sheet models and digital twin applications. A compartmental model (CM) can be a middle-ground by providing a more realistic representation of the hydrodynamics and still being computationally affordable. However, the hydrodynamics of a plant can be very different under varying flow conditions. Dynamic CMs can capture these changes in an elegant way. So far, the application of CMs has been limited mostly to continuous flow systems. In this study, a dynamic CM of a sequencing batch reactor (SBR) is developed for a bio-P removal process. The SBR comes with challenges for CM development due to its distinct operational stages. The dynamic CM shows significant improvements over the CSTR model (using the same biokinetic parameters) for dissolved oxygen and phosphate predictions reducing the need for model recalibration that can lead to over-fitting and limited extrapolation capability of the model.
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