CFD Simulation of Liquid-Liquid Extraction Columns and Visualization of Eulerian Datasets

In this joint work, a complete framework for modeling, simulating and visualizing multiphase fluid flow within an extraction column is presented. We first present a volume-of-fluid simulation, which is able to predict the surface of the droplets during coalescence. However, a fast and efficient model is needed for the simulation of a liquid-liquid extraction column due to the high number of occurring droplets. To simulate the velocity and droplet size in a DN32 extraction column, a coupled computational fluid dynamic-population balance model solver is used. The simulation is analyzed using path-line based visualization techniques. A novel semi-automatic re-seeding technique for droplet path-line integration is proposed. With our technique, path-lines of fluid droplets can be re-initialized after contact with the stirring devices. The droplet breakage is captured, allowing the engineer to improve the design of liquid-liquid columns layout

CFD Simulation of Liquid-Liquid Extraction Columns and Visualization of Eulerian Datasets

In this joint work, a complete framework for modeling, simulating and visualizing multiphase fluid flow within an extraction column is presented. We first present a volume-of-fluid simulation, which is able to predict the surface of the droplets during coalescence. However, a fast and efficient model is needed for the simulation of a liquid-liquid extraction column due to the high number of occurring droplets. To simulate the velocity and droplet size in a DN32 extraction column, a coupled computational fluid dynamic-population balance model solver is used. The simulation is analyzed using path-line based visualization techniques. A novel semi-automatic re-seeding technique for droplet path-line integration is proposed. With our technique, path-lines of fluid droplets can be re-initialized after contact with the stirring devices. The droplet breakage is captured, allowing the engineer to improve the design of liquid-liquid columns layout

An open environment CT-US fusion for tissue segmentation during interventional guidance.

Therapeutic ultrasound (US) can be noninvasively focused to activate drugs, ablate tumors and deliver drugs beyond the blood brain barrier. However, well-controlled guidance of US therapy requires fusion with a navigational modality, such as magnetic resonance imaging (MRI) or X-ray computed tomography (CT). Here, we developed and validated tissue characterization using a fusion between US and CT. The performance of the CT/US fusion was quantified by the calibration error, target registration error and fiducial registration error. Met-1 tumors in the fat pads of 12 female FVB mice provided a model of developing breast cancer with which to evaluate CT-based tissue segmentation. Hounsfield units (HU) within the tumor and surrounding fat pad were quantified, validated with histology and segmented for parametric analysis (fat: -300 to 0 HU, protein-rich: 1 to 300 HU, and bone: HU>300). Our open source CT/US fusion system differentiated soft tissue, bone and fat with a spatial accuracy of ∼1 mm. Region of interest (ROI) analysis of the tumor and surrounding fat pad using a 1 mm(2) ROI resulted in mean HU of 68±44 within the tumor and -97±52 within the fat pad adjacent to the tumor (p<0.005). The tumor area measured by CT and histology was correlated (r(2) = 0.92), while the area designated as fat decreased with increasing tumor size (r(2) = 0.51). Analysis of CT and histology images of the tumor and surrounding fat pad revealed an average percentage of fat of 65.3% vs. 75.2%, 36.5% vs. 48.4%, and 31.6% vs. 38.5% for tumors <75 mm(3), 75-150 mm(3) and >150 mm(3), respectively. Further, CT mapped bone-soft tissue interfaces near the acoustic beam during real-time imaging. Combined CT/US is a feasible method for guiding interventions by tracking the acoustic focus within a pre-acquired CT image volume and characterizing tissues proximal to and surrounding the acoustic focus

Image analysis for design and operation of gravity separators with coalescing aids

In gravity separators, also known as settlers, two immiscible liquid phases separate due to differences in density. In extraction mixer-settler units, a dispersion needs to be separated within the separator unit. In order to overcome the hitherto purely experimental design, a knitted mesh adapted model as well as an automated test facility were developed in this work, which easily enable a scale-up to industrial units. An automation allows for a controlled investigation of knitted meshes as coalescing aids in settlers, and this was achieved via photo-optical probes with an optimized image analysis technique. It overcomes the limitations of neuronal network training based on manually annotating images using computer-generated image data. Therefore, the new methodology and setup are explained in detail, and the derivation and application of a new model to design separators with knitted meshes as coalescing aid is presented and compared to experimental results using meshes of different structures and materials. Finally, case studies and scale-up are discussed

Modeling of solid-particle effects on bubble breakage and coalescence in slurry bubble columns

Solid particles heavily affect the hydrodynamics in slurry bubble columns. The effects arise through varying breakup and coalescence behavior of the bubbles with the presence of solid particles where particles in the micrometer range lead to a promotion of coalescence in particular. To simulate the gas-liquid-solid flow in a slurry bubble column, the Eulerian multifluid approach can be employed to couple computational fluid dynamics (CFD) with the population balance equation (PBE) and thus to account for breakup and coalescence of bubbles. In this work, three approaches are presented to modify the breakup and coalescence models to account for enhanced coalescence in the coupled CFD-PBE framework. The approaches are applied to a reference simulation case with available experimental data. In addition, the impacts of the modifications on the simulated bubble size distribution (BSD) and the applicability of the approaches are evaluated. The capabilities as well as the differences and limits of the approaches are demonstrated and explained

Autonomous Liquid–Liquid Extraction Operation in Biologics Manufacturing with Aid of a Digital Twin including Process Analytical Technology

Liquid–liquid extraction has proven to be an aid in biologics manufacturing for cell and component separation. Because distribution coefficients and separation factors can be appropriately adjusted via phase screening, especially in aqueous two-phase systems, one stage is frequently feasible. For biologics separation, aqueous two-phase systems have proven to be feasible and efficient. The simple mixer–settler equipment type is still not standard in biologics manufacturing operations. Therefore, a scalable digital twin would be of aid for operator training, process design under the regulatory demanded quality by design approach for risk analysis, design and control space definition, and predictive maintenance. Autonomous operation is achieved with the aid of process analytical technology to update the digital twin to real time events and to allow process control near any optimal operation point. Autonomous operation is first demonstrated with an experimental feasibility study based on an industrial type example of pDNA manufacturing via lysis from E. coli with and without cell separation performance

Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network

The efficiency of many chemical engineering applications depends on the surface/volume ratio of the dispersed phase. Knowledge of this particle size distribution is a key factor for better process control. The challenge of measurements acquired by optical imaging techniques is the segmentation of overlapping particles, especially in high phase fraction flows. In this work, a convolutional neural network is trained to segment droplets in images acquired by a shadowgraphic approach. The network is trained on artificial images and implemented into a droplet size algorithm. The results are compared to an OpenSource segmentation approach