Zum Flutpunkt in pulsierten Siebboden‐Extraktionskolonnen unter Einbeziehung experimenteller Tropfengrößendaten

Aufderheide, Eberhard; Vogelpohl, Alfons

doi:10.1002/cite.330560921

Cited by 8 publications

(1 citation statement)

References 5 publications

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“…For the development of the data-driven model, 3048 datasets of flooding points for pulsed sieve plate extraction columns were retrieved from the literature. − The physical properties of pure components and operational and geometric parameters are the features of the model. The overall throughput at flooding is the target of the model.…”

Section: Database and Dataflowmentioning

confidence: 99%

Prediction and Characterization of Flooding in Pulsed Sieve Plate Extraction Columns Using Data-Driven Models

Brockkötter

Cielanga

Weber

et al. 2020

Ind. Eng. Chem. Res.

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Reliable prediction of flooding conditions is needed for sizing and operation of sieve plate extraction columns. Due to the complex interplay of chemical properties, the extraction column geometry and material and the pulsation intensity, the development of physical models and semiempirical correlations for a broad validity range is complicated. Available models and correlations may fail in predicting the flooding curve accurately. To overcome this problem, a data-driven model has been developed, which is capable of predicting flooding with a higher accuracy than conventional correlations from the literature. The optimized black-box approach, a purely data-driven approach, is a Gaussian process with a root mean squared error of 1.65 × 10–3 m/s and a coefficient of determination of 0.942. The combination of the data-driven model with additional physical models improves the accuracy not significantly. This gray-box approach results in a Gaussian process with a root mean squared error of 1.61 × 10–3 m/s and a coefficient of determination of 0.944. The data-driven model is able to calculate correct flooding curves for different representative chemical systems and extraction column geometries.

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