Experimental and modeling evaluation of droplet size in immiscible liquid-liquid stirred vessel using various impeller designs

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.

Computational prediction of blend time in a large-scale viral inactivation process for monoclonal antibodies biomanufacturing

Viral inactivation (VI) is a process widely used across the pharmaceutical industry to eliminate the cytotoxicity resulting from trace levels of viruses introduced by adventitious agents. This process requires adding Triton X-100, a non-ionic detergent solution, to the protein solution and allowing sufficient time for this agent to inactivate the viruses. Differences in process parameters associated with vessel designs, aeration rate, and many other physical attributes can introduce variability in the process, thus making predicting the required blending time to achieve the desired homogeneity of Triton X-100 more critical and complex. In this study we utilized a CFD model based on the lattice Boltzmann method (LBM) to predict the blend time to homogenize a Triton X-100 solution added during a typical full-scale commercial VI process in a vessel equipped with an HE-3-impeller for different modalities of the Triton X-100 addition (batch vs. continuous). Although direct experimental progress of the blending process was not possible because of GMP restrictions, the degree of homogeneity measured at the end of the process confirmed that Triton X-100 was appropriately dispersed, as required, and as computationally predicted here. The results obtained in this study were used to support actual production at the biomanufacturing site.

Scaling up extractive deacidification of waste cooking oil

Biodiesel produced from waste feedstocks can play a significant role in fighting climate change, improperly disposed waste and growing energy demand. Waste feedstocks such as used cooking oil have a great potential for energy production. However, they often have to be purified from free fatty acids prior to biodiesel production. Extractive deacidification with deep eutectic solvents is a promising alternative to conventional purification methods. To evaluate the process of extractive deacidification of waste cooking oil, a full set of physical, hydrodynamic and kinetic data were experimentally determined on a laboratory scale. Hydrodynamic and kinetic experiments were performed in three geometrically similar jacketed agitated vessels. Vessels were equipped with axial flow impeller (four pitched blade impeller). Physical properties (density, viscosity and surface tension) were experimentally determined. Preliminary hydrodynamic experiments involved several model systems without mass transfer. As a result, correlation between power number and Reynolds number as well as scale-up criterion was developed. Obtained dependencies were correlated with the physical properties. Mixing intensity for achieving complete dispersion was determined. Second stage of investigation involved two sets of experiments, hydrodynamic and kinetic, with interphase mass transfer (the extraction of free fatty acids from waste cooking oil with deep eutectic solvent, potassium carbonate:ethylene glycol, 1:10). Obtained results enabled understanding interphase mass transfer and prediction of mass transfer coefficient from the derived dimensionless correlations. The values of volumetric mass transfer coefficients were smaller for the dispersed phase, indicating that the prevailing mass transfer resistance was within the droplets. The working hypothesis was that the same process result should be achieved at the same dispersion rate, and that hypothesis was confirmed - at all scales extraction efficiency was 97.9 ± 0.1%.

Analysis of flow pattern characteristics and strengthening mechanism of co-rotating and counter-rotating mixing with double impellers on different string shafts

Based on two cases of the double impellers on different shafts in the co-rotating and counter-rotating, the distribution of the velocity, streamline, turbulent kinetic energy, and turbulent energy dissipation rate are obtained through three-dimensional unsteady numerical simulation. Very good agreements between experimental and numerical results have been obtained. The hydrometallurgy purification experimental platform was built with the size of one third of the simulated. The results show that the mechanical string mixing system with double impellers on different shafts can form a more obvious convection effect in the central area of the double impellers, which can effectively break the mixing isolation region and improve the mixing effect. In the co-rotating case, the two impellers can generate strong convection in the central area and form an interactive vortex and a high-speed flow channel between the two impellers. while the convection formed by counter-rotating case is weaker and the vortex structures are independent of each other. The counter-rotating system performs better in the macro momentum transfer and the co-rotating system performs better in the micro-mixing level. In the experiments of hydrometallurgy purification, 7.93% more energy is used in the co-rotating system than that of the counter-rotating system. The average energy consumed by co-rotating in the process of purifying every one percent of Cd2+ ions are 8.65% lower than that of counter-rotating. The co-rotating system can improve microscopic mass transfer effect and finally save energy and time compared to the counter-rotating system.

The Liquid–Liquid Dispersion Homogeneity in a Vessel Agitated by a High-Shear Sawtooth Impeller

The agitation of immiscible liquids or solid suspensions is a frequent operation in chemical and metallurgical industries. The product quality yield and economy of the processes are significantly affected by mixing conditions. Prediction of mean drop size distribution (DSD) during agitation is fundamental for processes in many branches of industry where the mass transfer is crucial. This contribution aims to analyze the homogeneity of a dispersed system in a vessel agitated by a high-shear sawtooth impeller. The homogeneity of liquid–liquid dispersion is determined by comparison of Sauter mean diameters and drop size distribution (DSD) from different measured regions and for various dispersion times. The experiments were carried out in a baffled vessel for various impeller speeds. The sizes of droplets were obtained by the in-situ measurement technique and by the image analysis (IA) method.