Prediction of Flooding in Packed Liquid‐Liquid and High‐Pressure Extraction Columns Using a Gaussian Process

Brockkötter, Johannes; Ahndorf, Johannes; Jupke, Andreas

doi:10.1002/cite.202100073

Cited by 5 publications

(4 citation statements)

References 29 publications

(40 reference statements)

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“…The operational window of an extraction column is limited by flooding. Flooding depends on a complex interplay of physicochemical properties of both phases, the column geometry, column material, pulsation intensity, phase ratio, mass transfer rate and its direction, as well as the overall throughput. ,, Optical observable flooding of an extraction column is often defined via two characteristics: Accumulation of dispersed phase below the first packing element leading to a prop. Accumulation and stagnancy of dispersed phase within the packing. …”

Section: Modeling the Fluid Dynamics Phenomenamentioning

confidence: 99%

Dynamic Modeling of a Liquid–Liquid Extraction Column─An Industrial Approach

Brockkötter,

Hammacher,

Becker

et al. 2024

Ind. Eng. Chem. Res.

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Model-based evaluation and optimization of technical extraction columns are necessary when the product specifications of a technical system (TS) are not reached. However, state-of-the-art models are parameterized and validated using standard test systems (STSs) of highest purity. In contrast to that, TSs are characterized by an incomplete list of components, impurities, and batch-depending physicochemical properties. This divergence between STSs and TSs results in a lack of trust regarding developed model-based approaches for extraction columns. In this paper, an experimental approach for parameterization of a one-dimensional rate-based model is presented. The model is parameterized for a STS leading to a weighted absolute percentage error, WAPE w Acetone,out , of 8.53%. The parameter set is then used for a TS leading to a WAPE w TC,out of 17.43%. The distribution coefficient is the most sensitive model parameter as a change in the distribution coefficient of 3% yields a relative change of WAPE w TC,out by 19.39%. Varying the fluid dynamics parameters Sauter diameter and hold-up by 20%, the relative change in WAPE w TC,out is 44.03% and only 5.48%, respectively. A recommendation for minimal instrumentation for lab-scale extraction columns and technical large-scale columns is given.

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Section: Modeling the Fluid Dynamics Phenomenamentioning

confidence: 99%

Dynamic Modeling of a Liquid–Liquid Extraction Column─An Industrial Approach

Brockkötter,

Hammacher,

Becker

et al. 2024

Ind. Eng. Chem. Res.

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“…While flooding limits the operation conditions of the extraction column, and thus must be prevented, the separation efficiency is higher close to the flooding point. 51 Hence, the study of this parameter is of the most importance.…”

Section: Impact Of Interfacial Tension and Particle Diameter On The C...mentioning

confidence: 99%

“…Flooding conditions are reached when the disperse phase can no longer sediment, the continuous phase can no longer flow through the packing, and the disperse phase might start appearing in the continuous phase outlet or the reverse. While flooding limits the operation conditions of the extraction column, and thus must be prevented, the separation efficiency is higher close to the flooding point . Hence, the study of this parameter is of the most importance.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Rigorous Dynamic Modeling and Simulation of an Industrial Liquid–Liquid Extraction Packed Column for Biodiesel Washing

Pinho,

Braz,

Granjo

et al. 2023

Ind. Eng. Chem. Res.

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This work focuses on developing a dynamic model of a packed bed column for biodiesel washing. Its development was motivated by increasing problems in these columns when higher amounts of waste cooking oil and other low-quality fatty materials are incorporated as raw materials. This incorporation causes the formulations of raw oil to change significantly, requiring process flexibility and adequate control systems to maintain the product within strict quality standards and prevent operational problems. A detailed rate-based model of a packed column is implemented in gPROMS ModelBuilder, accounting for mass transfer and hydrodynamics phenomena such as holdup, flooding, and phase inversion. The model is compared to data from an industrial unit. The simulation results are in good agreement with the typical concentration values measured in the industrial unit. Using the developed model, the influence of operating parameters and the presence of contamination on the column’s behavior are studied.

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“…The development of the data-driven models follows the data flow introduced by (Brockkötter et al, 2020;Brockkötter et al, 2021). The data flow consists of five steps, including (i) data transformation, (ii) data split, (iii) machine learning (ML), (iv) selection of the data-driven algorithm, and (v) wrapper feature selection.…”

Section: Data Flow For Machine Learningmentioning

confidence: 99%

Hybrid modeling of drop breakage in pulsed sieve tray extraction columns

Palmtag,

Rousselli,

Gröschl

et al. 2023

Front. Chem. Eng.

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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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Prediction of Flooding in Packed Liquid‐Liquid and High‐Pressure Extraction Columns Using a Gaussian Process

Cited by 5 publications

References 29 publications

Dynamic Modeling of a Liquid–Liquid Extraction Column─An Industrial Approach

Dynamic Modeling of a Liquid–Liquid Extraction Column─An Industrial Approach

Rigorous Dynamic Modeling and Simulation of an Industrial Liquid–Liquid Extraction Packed Column for Biodiesel Washing

Hybrid modeling of drop breakage in pulsed sieve tray extraction columns

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