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

Abstract

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.

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

“…In this case the Mate ´rn kernel is used, as it was tested successfully prior [8]. The Mate ´rn covariace function is given by…”

“…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.…”

“…In this figure, the dataflow for the packed liquid‐liquid extraction column is shown. The dataflow is based on Brockkötter et al 8.…”

“…However, this lack of accuracy or validity range shall be overcome by using a data-driven model. In this study, an already existing dataflow is applied for predicting flooding in packed liquid-liquid and high-pressure extraction columns [8]. In general, data-driven models have been applied successfully to predict flooding in distillation columns [9,10] and pulsed sieve-plate extraction columns [8].…”

“…In this study, an already existing dataflow is applied for predicting flooding in packed liquid-liquid and high-pressure extraction columns [8]. 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].…”