The dynamics and breakup of bubbles in swirl-venturi bubble generator (SVBG) are explored in this work. The three-dimensional movement process and breakup phenomena of bubbles are captured by one high-speed camera system with two cameras while the distribution of swirling flow field are recorded through Particle Image Velocimetry technology. It is revealed that bubbles have two motion trajectories, which are deeply related to bubble breakup. One trajectory is that mother bubble moves upward in an axial direction of the SVBG to the diverging section, and the other trajectory is that mother bubble rotates obliquely upward to another side-wall along the radial direction. Meanwhile, binary breakup, shear-off-induced breakup, static erosive breakup and dynamic erosive breakup are observed. For relatively high liquid Reynolds number, vortex flow regions are extended and the bubble size is reduced. Furthermore, it is worth noting that the number of microbubbles increases significantly for intensive swirling flow.

A micro membrane adsorber with deep-permeation nano structure (DPNS) has been successfully fabricated by flowing synthesis. The nanoparticles are in situ assembled in membrane pores and immobilized in each membrane pores along the direction of membrane thickness. The nanoparticles with a lower size and thinner size distribution can be achieved owing to the confined space effect of the membrane pores. As a concept-of-proof, the nano ZIF-8 and ZIF-67 are fabricated in porous membrane pores for Methyl orange (MO) and Rhodamine B (RhB) adsorption. The adsorption rate is increased significantly owing to the enhanced contact and mass transfer in the confined space. The adsorption capacity for the RhB is also increased, since the size of the nanoparticles assembled in membrane pores is smaller with more active sites exposed. This micro membrane adsorber with DPNS has good reusability, and can provide a promising prospect for industrial application.

This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.

Modeling and analysis of the materials universe is an emerging area of research with many important applications in materials science. The main goal is to create a map of materials which allows not only to visualize and navigate the materials space, but also reveal complex relationships and “connections” among materials and potentially find clusters of materials with similar properties. In this paper, we consider the problem of mapping and exploring the materials universe using network science tools and concepts. The networks are based on the open-source materials data repository AFLOW.org where each material is represented as a node, and each pair of nodes is connected by a link if the respective materials exhibit a high level of similarity between their Density of States (DOS) functions. We discuss the importance of similarity measure selection, investigate basic structural properties of the resulting networks, and demonstrate advantages and limitations of the proposed approaches.

In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

Bubble breakup plays an important role in gas-liquid flows, but detailed studies are still scarce. In this work, the breakup behavior of a single bubble in a stirred tank was experimentally studied with a high-speed camera, focusing on the effect of gas density, liquid properties, agitation speed and mother bubble size. The bubble breakup time, breakup probability, breakup rate and daughter bubble size distribution were determined. The internal flow phenomenon inside a deformed bubble was studied in detail, which accounted for the effect of gas density or operating pressure. The results showed that with increasing gas density, agitation speed, mother bubble size and decreasing surface tension, the bubble breakup rate and probability of equal-size distribution significantly increased. With increasing liquid viscosity, the bubble breakup rate decreased especially in the high viscosity range. An M-shaped daughter bubble size distribution was observed, which was consistent with our previous bubble breakup model.

Having proper correlations for hydrodynamic forces is essential for successful CFD-DEM simulations of a fluidized bed. For spherical particles in a fluidized bed, efficient correlations for predicting the drag force, including the crowding effect caused by surrounding particles, are already available and well tested. However for elongated particles, next to the drag force, the lift force and hydrodynamic torque also gain importance. In this work we apply recently developed multi-particle correlations for drag, lift and torque in CFD-DEM simulations of a fluidized bed with spherocylindrical particles of aspect ratio 4 and compare them to simulations with widely used single-particle correlations for elongated particles. Simulation results are compared with previous magnetic particle tracking (MPT) experimental results. We show that multi-particle correlations improve the prediction of particle orientation and vertical velocity. We also show the importance of including hydrodynamic torque.

Zeolite belongs to one of the most important families of solid acid catalysts in chemical industries. It is however severely constrained by the diffusion limitation for bulky molecules, the lack of multi-functionality for sequential reactions and pore adaptability towards specific adsorbates, due to the small micropore size and simple aluminosilicate framework. Introducing mesopores into the zeolitic framework towards hierarchical zeolites is prevailing, but usually suffers from compromised crystallinity as well as insufficient interconnectivity and openness of mesopores. Herein, a novel of acid-redox co-functionalized single-crystalline zeolite with highly open and interconnected mesopores is designed and fabricated. As a proof-of-concept study, we integrate the solid acid and Fe-oxy redox sites in a hierarchical MEL zeolite with well characterized microporosity and mesoporosity. It exhibits superior activity and stability towards the alkylation between mesitylene with benzyl alcohol, arising from greatly facilitated intracrystalline molecular diffusion, mitigated metal leaching and optimized adsorbate-pore wall interactions.

In this study, a Schulze Ring Shear Tester and the Discrete Element Method (DEM) are employed to investigate the effect of polydispersity on the binary shear flows. Both experimental results and DEM simulations show that the pre-shear stresses are greater for binary blends than for monodispersed particles. The flowability of these mixtures is strongly affected by the solid fraction, with minimal flow function values correlating to maximum packing fraction. However, minimum flow function values are not observed at the same packing fractions where the maximum pre-shear stress occurs. The powder friction has a slightly higher contribution to powder strength than powder adhesion, and the frictional component follows the same trend as shear stress with mixture composition. Using DEM, it is demonstrated that the addition of small adhesive particles reduces the averaged angular velocity of the larger particles, which makes a contribution to the larger shear stress for binary blends.

The complexity of lipid feedstocks and the lack of data on physical properties hinder the simulation of oleochemical processing units. In this work, an iterative lumping approach is proposed to define an adequate number of key components such that diversification between lipid feedstocks becomes possible, while keeping the determination of physical properties as required for process modelling manageable. As a case study, the iterative lumping approach is used for simulation and optimization of a fatty acid distillation plant. For predicting vapour-liquid equilibria of fatty acids, the best results were acquired using the property method UNIQ-HOC. Using the iterative lumping approach, 11 key components were selected to represent the feedstock. The process model properly predicts the product composition, yield, purity and heat duty. The most important process parameters are found to be side-reflux-ratio, reboiler-outlet-temperature and heat-duty of the pitch-distiller. For optimization, an increase of the side-reflux-ratio and reboiler-outlet-temperature, is recommended.

Interfacial tension is an essential physical property in two phase flow and it changes due to the mass transfer. The measurement of dynamic interfacial tension (DIFT) in such condition is a difficult problem. In previous study (Zhou at al., Chem Eng Sci. 2019; 197:172-183), we presented the quantitative relation between the droplet breakup frequency function (DBFF) and interfacial tension. It is found that the DBFF is highly depends on interfacial tension. Therefore the DBFF is a suitable parameter to quantitatively characterize the interfacial tension. Based on this concept, the DIFT in the column is determined by regression method after the DBFF under mass transfer condition is measured. It is found that the DIFT is smaller than the static interfacial tension. This result indicates that interphase mass transfer leads to decreasing of the interfacial tension. The decreasing extent of the DIFT has a positive correlation with the mass transfer flux.

A recently published approach for modeling the cross flow in an extruder channel using a new solution to the biharmonic equation is utilized in a study of chaotic mixing in a free helix single screw extruder. This novel extruder was designed and constructed with the screw flight, also referred to as the helix, detached from the screw core. Each of the screw elements could be rotated independently to obtain chaotic motion in the screw channel. Using the new extruder, experimental evidence for the increased mixing of a dye, for both a Dirac and droplet input, with a chaotic flow field relative to the traditional residence time distribution is presented. These experimental results are compared using the new biharmonic equation-based model. Because of the ability to periodically rotate only the flight/helix, the chaotic mixing results are minimally confounded by the existence of Moffat eddies.

Community detection decomposes large-scale, complex networks ‘optimally’ into sets of smaller sub-networks. It finds sub-networks that have the least inter-connections and the most intra-connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi-objective optimization problem. The whale optimization algorithm is extended to enabe it to handle multi-objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population’s positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non-dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process to show its application and performance.

The isosteric heat of adsorption is an important thermodynamic property used to characterise and optimise adsorption processes. In this work, analytic expressions for isosteric heats of adsorption are derived for a collection of commonly used isotherm models and a two-dimensional molecular equation of state based on the SAFT-VR approach. The use of these expressions is presented with an example of adsorption of nitrous oxide, N₂O, on biochar, which is a waste biomass charcoal that exhibits high adsorption potential. The results show that accurate fitting of the adsorption isotherms leads to consistent results obtained with different approaches, however, the predicted isosteric heat of adsorption exhibits strong variations in the regions where experimental data is insufficient such in the region of low pressure/low coverage. Convergence on the prediction of the isosteric heat of adsorption by the different models is only observed in the region where no extrapolation of experimental data is needed.

The droplet generation mechanism in the step T-junction remains unknown, especially for the transition stage from dripping to jetting . In this work, the droplet generation mechanism was systematically investigated in a novel modified step T-junction. We found that under different fluid regimes, different factors take action. In dripping regime, the interfacial tension dominated the formation mechanism when the surfactant concentration was controlled below micelle concentration (CMC). In jetting regime, our experimental results showed that the influence of the surfactant concentration on the size of generated droplets was rather negligible while the phase ratio indeed determined such a parameter. In the dripping-jetting transition stage, an abnormal increase of droplet size was observed despite the increase of continuous phase flow. To the best for our knowledge, it is the first study to report generation mechanism in modified step T-junction from dripping to jetting regimes.

As an attractive alternative technology for the separation of long chain olefin and paraffin, a novel silver-based deep eutectic solvent (Ag-DES) was prepared and utilized for 1-octene/n-octane separation. Comprehensive reactive extraction separation experiments were performed to highlight the Ag-DES concentration and operating temperature discriminations using compounds with different ratio of 1-octene/n-octane. The novel Ag-DES showed optimal separation performance regarding 1-octene/n-octane and possessed the highest levels separation selectivity in the range 3.75-16.74 with excellent circulation stability in our best knowledge. Furthermore, FT-Raman measurements and quantum chemistry calculation were conducted to elucidate the interaction mechanism of Ag-DES in the separation of 1-octene and n-octane, which revealed that both chemical complexation and strong physical attraction existed in the complex of Ag-DES with 1-octene rather than n-octane. This study lends important insight for the development of Ag-DES reactive extraction separation process for the energy-efficient long chain α-olefin purification from F-T synthesis products.

The present study depicts the hydrodynamics along with the mixing characteristics inside a millichannel-based serpentine fixed-bed device to attain the particular demands of the fabrication of the miniature adsorption devices. Residence Time Distribution (RTD) analyses were accomplished to analyze the velocity distribution inside the packed bed geometry. The operating variables effect the hydrodynamics, mixing, and the lead adsorption characteristics, which were pronounced clearly in the present context. Depending on the results obtained in the experiment, the new correlations were proposed. The parametric effects on the lead ions adsorption were studied in the same millichannel geometry packed with the graphene oxide (GO) coated glass beads. Thomas model was utilized to investigate the kinetics of the adsorptive removal process. The regeneration study of the said millichannel-based fixed-bed device was also executed.

Aspen Plus® simulations using the Peng-Robinson (PR-EOS) and the COSMO-SAC models were performed to study absorption power cycles (APCs) using mixtures of R-134a with two ionic liquids, [C2C1im][Tf2N] or [C6C1im][Tf2N], and compared against an R-134a organic Rankine cycle (ORC) operating under similar conditions. The PR-EOS results were in agreement with calculations from a PR model fitted to the R-134a+IL experimental phase equilibrium data. The APCs have similar efficiencies and outperform the ORC by 3-46%, with the largest differences observed when operating with lower grade (lower TH) heating sources, lower quality cooling (higher TL) and lower subcooling in the pump inlet stream. The PR-EOS and the COSMO-SAC results follow similar trends, but numerical discrepancies are observed in the cycle efficiencies and stream flow rates and compositions due to differences in solubilities and enthalpy changes between both models, suggesting that improvements are needed to increase the accuracy of COSMO-SAC for these systems.