Datenbasierte Modellierung des Flutverhaltens von Extraktions‐ und Hochdruckextraktionskolonnen

Brockkötter, Johannes; Jupke, Andreas; Weber, Benedikt; Cielanga, Michael

doi:10.1002/cite.202055039

Cited by 1 publication

(2 citation statements)

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“…Brockko ¨tter et al [8,11] already highlighted that a Gaussian process is capable to predict flooding precisely and to calculate accurate flooding curves for pulsed sieve plate extraction columns. Therefore, the Gaussian process regression is chosen in this study and described briefly in the following.…”

Section: Algorithmmentioning

confidence: 99%

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

Brockkötter

Ahndorf

Jupke

2021

Chemie Ingenieur Technik

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Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.

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Section: Algorithmmentioning

confidence: 99%

“…In general, data-driven models have been applied successfully to predict flooding in distillation columns [9,10] and pulsed sieve-plate extraction columns [8]. Beside predicting flooding in distillation and extraction columns, these models were already successfully implemented for the online monitoring of flooding [11][12][13].…”

Section: Introductionmentioning

confidence: 99%