脉冲筛板萃取塔中液滴破碎的混合建模

IF 2.5 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Andreas Palmtag, Johannes Rousselli, Henning Gröschl, Andreas Jupke
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引用次数: 0

摘要

准确的脉冲筛板萃取柱(pse)模型依赖于正确的液滴直径预测,以估计萃取物在相边界上的传质。在现象学上,液滴直径是由液滴破碎和凝聚的平衡决定的。而对于大多数工业溶剂体系,聚结作用较小;断裂是决定液滴直径的主要现象。然而,大多数的预测方法的特点是在较宽的有效范围和较好的预测精度之间进行权衡。为了克服这一限制,我们开发了一种混合破碎模型,其中物理经验模型基础通过数据驱动参数估计模型(PEMs)得到增强。混合模型是基于Garthe破损模型的修正形式,为此我们为模型参数开发了一个线性PEM,并分别为dstab和d100开发了两个数据驱动的PEM。在743个试验数据集上验证了混合断裂模型,并基于拉力度量对模型进行了评价。在灵敏度分析中,该模型正确地预测了各种溶剂性质、操作条件和筛板几何形状的破碎概率。在未来的研究中,混合破碎模型可以在不初始参数化的情况下纳入提取柱模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid modeling of drop breakage in pulsed sieve tray extraction columns
Accurate models for pulsed sieve tray extraction columns (PSEs) depend on the correct prediction of the drop diameter to estimate extractive mass transfer across the phase boundary. Phenomenologically, the drop diameter is determined by a balance of drop breakage and coalescence. While for most industrial solvent systems, coalescence plays a minor role; breakage is mostly the dominant phenomenon determining the drop diameter. However, most modeling approaches for drop breakage in PSEs are characterized by a trade-off between a broad validity range and good prediction accuracy. To overcome this limitation, we developed a hybrid breakage model for drop breakage in PSEs in which a physical-empirical model basis is enhanced by data-driven parameter estimator models (PEMs). The hybrid model is based on a revised form of Garthe’s breakage model, for which we developed a linear PEM for the model parameters and two data-driven PEMs for dstab and d100 , respectively. The hybrid breakage model was validated on 743 experimental data sets and evaluated based on the pull metric. In a sensitivity analysis, the model correctly predicted the breakage probability over a wide range of solvent properties, operating conditions, and sieve tray geometries. In future studies, the hybrid breakage model can be incorporated into extraction column models without an initial parametrization.
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来源期刊
CiteScore
3.50
自引率
0.00%
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审稿时长
13 weeks
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