Digital Extraction Column: Measurement and Modeling Techniques

Hlawitschka, Mario; Schulz, J.; Wirz, Dominic; Schäfer, Jan; Keller, Alexander; Bart, Hans‐Jörg

doi:10.1002/cite.202000043

Cited by 9 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the last few decades, image postprocessing techniques to extract the features of drops have been successfully developed. , For a single drop, the subduction method was used for drop identification, that is, to distinguish the profile of the drop by subtracting the sequenced image from a reference image. Then, the drop was presented as a white dot by the application of threshold and binarization.…”

Section: Measuring Techniquesmentioning

confidence: 99%

Experimental Study of Unsteady Drag Coefficient of Droplets in a Liquid–Liquid System

Tan

Wang

Zhang

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

In liquid−liquid contact process, the motion of droplets relative to the surrounding fluid always involves accelerating and decelerating, which affects the mass, heat, and momentum transfer. The lack of experimental data of the unsteady drag coefficient has been one of the limitations on the prediction of the unsteady flow field. In this study, the accelerated and decelerated water droplets in an organic phase were measured by a high-speed camera. The results show that with a decrease in droplet diameter, the acceleration becomes more significant, while the absolute relative velocity decreases, causing a lower Reynolds number. The Basset force and add mass force were solved numerically and compared with drag force. The unsteady drag coefficient is always smaller than the corresponding steady drag coefficient in the case of accelerating relative flow and larger than that in the case of decelerating relative flow. A new unsteady drag coefficient model has been established, which has an acceptable agreement with the experimental data.

show abstract

Section: Measuring Techniquesmentioning

confidence: 99%

Experimental Study of Unsteady Drag Coefficient of Droplets in a Liquid–Liquid System

Tan

Wang

Zhang

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…The development of this type of ''smart equipment'' by using new sensor or ML solutions to determine hard-to-measure variables and offer more process flexibility has attracted considerable attention in academic and industrial research [1,4,26]. Some examples include the digitalization of extraction columns by means of novel measurement techniques complemented by modeling and simulation methods to create tools for predictive online monitoring, which is reviewed in Hlawitschka et al [27]. Other approaches work on the integration of novel sensors and actuators to obtain valuable process information and create more responsive equipment [28].…”

Section: Kernel Density Estimation Decision Treementioning

confidence: 99%

“…Some examples include the digitalization of extraction columns by means of novel measurement techniques complemented by modeling and simulation methods to create tools for predictive online monitoring, which is reviewed in Hlawitschka et al 27. Other approaches work on the integration of novel sensors and actuators to obtain valuable process information and create more responsive equipment 28.…”

Section: Introductionmentioning

confidence: 99%

Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches

Oeing

Neuendorf

Bittorf

et al. 2021

Chemie Ingenieur Technik

View full text Add to dashboard Cite

Flooding of separation columns is a severe limitation in the operation of distillation and liquid‐liquid extraction columns. To observe operation conditions, machine learning algorithms are implemented to recognize the flooding behavior of separation columns on laboratory scale. Besides this, the investigated columns already provided the modular automation interface Module Type Package (MTP), which is used for data access of necessary sensor data. Hence, artificial intelligence (AI) tools with deep learning offer high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice due to complex combination of operational sensing, data analysis, and active control of the equipment. This article provides an overview on how AI‐based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns for stable and robust operational conditions.

show abstract

“…In these studies, terms of drop behaviors usually come from two ways: from the previous literature or from the single drop study 25 . PBM based on the single drop study was found promising in scale‐up design of the column 26 and has a potential to reduce pilot test requirements 27 . However, one of the challenges is to obtain a set of robust parameters, 28 as they are always limited to a certain column geometry and specific test systems.…”

Section: Introductionmentioning

confidence: 99%

Drop sizes and population balance model for a Karr column

Zhang

Mumford

et al. 2021

AIChE Journal

View full text Add to dashboard Cite

Drop sizes were investigated and measured in a Karr column. A stagewise population balance model was presented considering drop breakage and coalescence. The same breakage mechanisms observed in the single drop study were also found in column. The breakage probability model was selected by comparing the model from single drop study and the widely used models. Model parameters were calculated based on a two‐step optimization method. The results showed that the drop sizes decreased with an increase in reciprocating intensity, but changed slightly with phase velocities. The predicted drop sizes were compared to the experimental data in previous studies and good agreement was achieved. Finally, the population balance model along with some existing correlations were compared with the experimental data. It was found that the population balance model and correlations with the low agitation term provided better predictions of drop sizes in the Karr column. Transport Phenomena and Fluid Mechanics: Karr column, drop size distribution, population balance model, parameter optimization.

show abstract

Digital Extraction Column: Measurement and Modeling Techniques

Cited by 9 publications

References 60 publications

Experimental Study of Unsteady Drag Coefficient of Droplets in a Liquid–Liquid System

Experimental Study of Unsteady Drag Coefficient of Droplets in a Liquid–Liquid System

Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches

Drop sizes and population balance model for a Karr column

Contact Info

Product

Resources

About