General correlation for pressure drop through randomly-packed beds of spheres with negligible wall effects

Pressure drop in fixed beds has been studied for over a century and a large number of conflicting correlations exist in the literature. Contributing factors to these differences include the use of particles of different shapes, the presence of wall effects in beds of low tube-to-particle diameter ratio (N) and parameter fitting over limited ranges of modified Reynolds number ${\mathit{Re}}_m=\frac{\mathit{\rho v}{d}_p}{\mu \left(1-\epsilon \right)}$. The present work, in contrast, considers the entire Reynolds number range for perfect spheres in unbounded (high-N) random-packed beds. An asymptote-based correlation has been developed based on published data for extremely high ${\mathit{Re}}_m$ and on new particle-resolved computational fluid dynamics (PRCFD) results for extremely low ${\mathit{Re}}_m$. The resulting equation is given by: $\frac{∆P}{L}\frac{d_p}{\rho v^2}\frac{{\epsilon }^3}{\left(1-\epsilon \right)}=\frac{160.0}{{\mathit{Re}}_m}+\left(0.922+16{\mathit{Re}}_m^{-0.46}\right)\frac{{\mathit{Re}}_m}{\left({\mathit{Re}}_m+52\right)}$. This equation fits a literature data set of 541 points with average error 5.66%, and shows correct limits for both high and low ${\mathit{Re}}_m$.     

Relaxing the constant molar overflow assumption in distillation optimization

The constant molar overflow (CMO) framework, while useful for shortcut distillation models, assumes that all components have the same latent heats of vaporization. A simple transformation, from molar flows to latent-heat flows, allows shortcut models to retain the mathematical simplicity of the CMO framework while accounting for different latent heats, resulting in the constant heat transport (CHT) framework for adiabatic distillation columns. Although several past works have already proposed this transformation in the literature, it has not been well utilized in recent times. In this article, we show the utility of this transformation in upgrading various applications such as identifying energy-efficient multicomponent distillation configurations based on heat duty rather than surrogate vapor flow. The method transforms the $V^{\min }$ diagram to a $Q^{\min }$ diagram. Furthermore, we derive new and insightful analytical results in distillation, such as cumulative latent-heat stage fractions having monotonic profiles within a distillation column under the CHT framework.     

Encapsulation of phenylacetic acid in block copolymer nanoparticles during polymerization induced self-assembly

Polymerization induced self-assembly (PISA) is an in situ method for producing block copolymer nanoparticles. Performing PISA in the presence of a pharmaceutical drug causes the nanoparticles to encapsulate the drug. While this approach is straightforward, the effects of drug loading and block copolymer composition remain unclear. Here, we investigate encapsulation of the drug phenylacetic acid (PA) in poly(glycerol monomethacrylate)-block-poly(2-hydroxypropyl methacrylate) (PGMA-PHPMA) nanoparticles during PISA. Nanoparticle morphology is characterized by electron microscopy and light scattering, while encapsulation efficiency ($p$) is quantified using nuclear magnetic resonance diffusometry. Increasing the PA loading shifts the nanoparticle morphology from spherical micelles $\to$ cylindrical micelles $\to$ vesicles. At a 32 mg/ml PA loading, $p$ maximizes at ~80%. Increasing the PHPMA degree of polymerization minimally impacts $p$. The invariance of $p$ toward core block length suggests that PA binds to the nanoparticle corona, highlighting the importance of the hydrophilic block for drug encapsulation during PISA.     

Integration of machine learning and data analysis for the SAGD production performance with infill wells

There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data-driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio ($R_{\text{increase}}$), a total steam–oil ratio (SOR_total), and a stolen ratio ($R_{\text{Stolen}}$) for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network [ANN] algorithm, three gradient boosting decision tree [GBDT] algorithms, and a support vector machine [SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance.     

Tunable size and shape of conductive poly(N‐methylaniline) based on surfactant template and doping

Poly(N‐methylaniline) (PNMA) is one of the polyaniline derivatives with N‐substituted position. Polyaniline derivatives have attracted attention due to their higher solubility in common solvents than pristine polyaniline, but they still possess lower electrical conductivity. In this work, PNMA was synthesized via chemical oxidative polymerization in an ethanol–water system. The effect of surfactant type, namely anionic sodium dodecylbenzenesulfonate (SDBS), cationic cetyltrimethylammonium bromide and non‐ionic Tween20, on the electrical conductivity, doping level and morphology was investigated. PNMA prepared with the SDBS system possessed the highest electrical conductivity among the obtained PNMAs with and without surfactants. The effect of $N_{{\mathrm{\text{HClO}}}_4}$/N_NMA dopant mole ratios on the re‐doping, crystallinity, morphology and particle size was also examined. Using an $N_{{\mathrm{\text{HClO}}}_4}$/N_NMA mole ratio of 10:1 in the re‐doping process provided the highest electrical conductivity of 15.53 ± 2.5 S cm^?1, a doping level of 55.59%, along with hollow spherical particles with the thinnest membrane. Electron microscopy images revealed that the morphology of PNMA particles depended mainly on the surfactant type but not the $N_{{\mathrm{\text{HClO}}}_4}$/N_NMA mole ratio. © 2019 Society of Chemical Industry     

Recovery of valeric acid using green solvents

In this work, extraction of valeric acid (VA) using tri-n-butyl phosphate (TBP) as a reactive extractant was carried out. To reduce the toxic effects of the conventional diluents on microorganisms, non-toxic and green edible sunflower and soybean oils were tried as the diluents. The high values of the distribution coefficient and extraction efficiency advocated to use them in the bio-refinery industries. Moreover, it shows intensification of the recovery of VA using reactive extraction process. Sunflower oil appeared to be a better diluent than soybean oil. The complexation reaction stoichiometry (m and n) and equilibrium complexation reaction constant $K_{E\left(m:n\right)}$ were estimated by using the differential evolution technique. In spite of the loading ratio being less than 0.5, the estimated m/n was found to be more than 1.0. The higher values of $K_{E\left(m:n\right)}$ occurred due to the 9higher stability of the VA-TBP complex in sunflower oil than in soybean oil. The stoichiometry of VA decreased with increasing TBP concentration. The complex concentration, ${\left[{\left(\mathit{HA}\right)}_m{S}_n\right]}_{\mathrm{org}}$, was found to be higher for soybean oil. It increased with temperature and initial VA concentration but remained invariant with TBP concentration. Due to the decreasing trend of $K_{E\left(m:n\right)}$ with temperature, the complexation reaction became exothermic. The enthalpy changes due to mass transfer stipulated easier mixing of the phases in sunflower oil than in soybean oil.     

Modeling of the volatile organic compounds biodegradation process in the trickle-bed bioreactor—Analysis of the model parametric sensitivity

Mathematical models of the process of air purification from hydrophobic (styrene) or hydrophilic (vinyl acetate, VA) compounds carried out in a co-current trickle-bed bioreactor were presented. These models were marked as TSM (two-substrate model), OSM (one-substrate model) and AOSM (approximate one-substrate model). The experimental database was exploited to validate the TSM which approximated very well the experimental data; the mean percentage error of RE prediction did not exceed 3% for styrene and 4.1% for VA. For the tested systems, TSM was only sensitive to the changes in biomass dry density X_b and effective diffusion coefficient D_j,ef values. The percentage relative error of the state variable computed using OSM/AOSM in relation to the value obtained from TSM was the quantitative criterion for comparison of the results obtained using different mathematical models of the process. It was shown that the simplified models describe the process investigated with satisfactory accuracy.     

The axial dispersion of liquid solutions and solid suspensions in planar oscillatory flow crystallizers

The planar oscillatory flow crystallizer (planar-OFC) was designed with a rectangular cross-section to improve the flow and suspension of solids of conventional OFCs. Residence time distribution experiments with liquid and solid tracers were performed to assess the effect of the net flow rate, Q , the frequency, f , and the amplitude of oscillation, x₀ , on the axial dispersion of liquids, , and solids, , in three planar-OFCs with different geometries. It was found that Q and f have in general positive effects on and , and x₀ has negative effects. Furthermore, identical values of and were obtained in each crystallizer. It was also found that the interaction between Q and x₀ is the most significant one in all systems. These results show that the three crystallizers have similar axial dispersion performances with liquids and solids. This is of paramount importance for multiphase systems such as crystallization.     

Convective heat transfer coefficient for the side-wall in a fixed bed

Fixed beds are widely used in the chemical and process industry due to their relatively simple yet effective performance. Determining the radial heat transfer at the wall in a fixed bed is crucial to predict the performance of columns. Heat transfer parameters often need to be obtained experimentally. Various Nusselt ${\mathrm{Nu}}_w$ versus Reynolds ${Re}_p$ correlations in literature show considerable scatter and discrepancies. The tube-to-particle diameter ratio $\frac{D_{\mathrm{t}}}{D_{\mathrm{p}}}$ and boundary conditions on the particle surface have been understood to affect heat transfer near the wall by virtue of influence on the near-wall porosity and mixing. In this work, a fixed bed consisting of mono-disperse particles is generated via gravity-forced sedimentation modelling utilizing the discrete element method for a $\frac{D_{\mathrm{t}}}{D_{\mathrm{p}}}$ ratio of 3.3. The system is meshed and imported in a computational fluid dynamics (CFD) solver. Fluid inlet velocity is varied to get ${Re}_p\in \left[1,1500\right]$ corresponding to the laminar and turbulent flow regimes. The particles are treated as boundaries with Dirichlet, Neumann, and Robin boundary conditions applied for the closure of energy balance. Another set of simulations is run with particles modelled as solids with varying thermal conductivities ($k_s/k_f$). The heat flux and volume-averaged fluid temperature calculated during post-processing are used to determine the wall heat transfer coefficient and, subsequently, the wall Nu number. Fifteen ${\mathrm{Nu}}_w$ versus ${Re}_p$ correlations are compiled and analyzed. A new semi-empirical correlation for the wall Nusselt number has been developed for a fixed bed packed with monodisperse spheres for $\frac{D_t}{D_p}=3.3$ and results compared with data published in literature. Additionally, the impact of buoyancy effect on the wall Nusselt number has been studied.     

Indirect urea electrooxidation by nickel(III) in alkaline medium: From kinetic and mechanism to reactor modeling

This study consists in the determination of two kinetic laws of urea oxidation (UO) and electrooxidation (UEO) in alkaline media on nickel(III). Two kinds of active sites were examined, the first one derived from a Ni(OH)₂ powder and the second from a massive nickel electrode. Partial orders of nickel(III) (two for UO and five UEO) enable to conclude about (i) the urea adsorption on two nickel(III) sites and (ii) that a multistep oxidation of urea occurs involving five nickel(III) site electroregenerations. A multipathway mechanism is also proposed to explain UEO facilitated by the nickel(III)/nickel(II) mediation system, and to predict the by-products' formation previously identified ($\mathrm{N}{\mathrm{O}}_2^{-},\mathrm{N}{\mathrm{H}}_3,\mathrm{OC}{\mathrm{N}}^{-},\mathrm{C}{\mathrm{O}}_3^{2-}$). At last, a model combining the UEO kinetic law previously established, with diffusive and convective transport phenomena was developed. A consistent correlation (maximum deviation of 6%) between laboratory electrolysis results with the model' predictions was obtained under different operating conditions, enabling the validation of this model.     

Double Smoothed Volatility Estimation of Potentially Non-stationary Jump-diffusion Model of Shibor

The occurrence-50 of economic policies and other sudden and large shocks often bring out jumps in financial data, which can be characterized through continuous-time jump-diffusion model. In this article, we present the double smoothed non-parametric approach for infinitesimal conditional volatility of jump-diffusion model based on high frequency data. Under certain minimal conditions, we obtain the strong consistency and asymptotic normality for the estimator as the time span T → ∞ and the sample interval ${\mathrm{\Delta }}_n\to 0$. The procedure and asymptotic behavior can be applied for both Harris recurrent and positive Harris recurrent processes. The finite sample properties of the underlying double smoothed volatility estimator are verified through Monte Carlo simulation and Shanghai Interbank Offered Rate in China for application.     

Phase transition of hafnon,HfSiO4, at high pressure

The high-pressure behavior of hafnon has been systematically investigated by combining in situ synchrotron X-ray diffraction, Raman, high-resolution transmission electron microscopy (HRTEM) techniques, and theoretical simulations. Hafnon starts phase transition at 26.6 GPa and completes the transition to an irreversible scheelite phase ($I{4}_1/a$, Z = 4, a₀ = 4.712 Å, and c₀ = 10.378 Å) at ∼45 GPa. The HRTEM observation of an interface between hafnon and scheelite phases allows atomic scale understanding of the transition process with a relationship of (200)_h‖(112)_s, ${(00\overline{2})}_{\mathrm{h}}\parallel {(\overline{1}10)}_{\mathrm{s}}$//, and ${[010]}_{\mathrm{h}}\parallel {[\overline{1}\overline{1}1]}_{\mathrm{s}}$. Hafnon shows a significantly lower transition pressure (∼12.6 GPa), as calculated from the relative enthalpies, than the measured pressure (∼26 GPa), indicating a kinetically hindered process involved in the transition. A high pressure low symmetry phase in hafnon ($I{\overline{4}}_{2}d$) is identified by the simultaneous appearance of two Raman modes (∼75 and 450 cm⁻¹) at 26.6 GPa and their subsequent simultaneous disappearance at 36.7 GPa. These results are important to understanding the mechanism of the zircon-scheelite transition for both zircon and hafnon.     

Dynamics adsorption of the enhanced CH4 recovery by CO2 injection

The dynamic adsorption isotherms of CO₂-EGR were measured by using an Intelligent Gravimetric Analysis system. In the initial CO₂ injecting stage, all the injected CO₂ enters into the adsorbent and the mole fraction of CH₄ in the gas phase () is maintained at 1.0. The CH₄ recovery factor () increases. The duration of this stage (t_CD) depends on the selectivity of CO₂ over CH₄ (). An adsorbent with large has long t_CD. In the second stage, the injected CO₂ competes with CH₄ for adsorption. The cumulative of the second stage is much larger than that of the initial stage. However, decreases sharply. in the whole CO₂ injection is always larger than that before CO₂ injection, suggesting that CH₄ desorption results from the displacement of CO₂ rather than from pressure depletion.     

Influence of weak electrostatic charges and secondary flows on pneumatic powder transport

The dynamics of the transported powder determines the functionality and safety of pneumatic conveying systems. The relation between the carrier gas flow, induced by the flown-through geometry, and the powder flow pattern is not clear yet for electrostatically charged particles. This paper highlights the influence of relatively minor cross-sectional secondary flows and electrostatic forces on the concentration and dynamics of the particles. To this end, direct numerical simulations (DNS) capture the interaction of the continuous and dispersed phases using a four-way coupled Eulerian–Lagrangian strategy. The transport of weakly charged particles in channel flows, where turbopheresis defines the particle concentration, is compared to duct flows, where additional cross-sectional vortices form. For both geometries, the Stokes number ($St=8,32$) and the electrical Stokes number (${\mathit{St}}_{\mathrm{el}}=\left[0,1,2,4\right]\times {10}^{-3}$) are varied, and the turbulent carrier flow was fixed to ${\mathit{Re}}_{\tau }=360$. The presented simulations demonstrate that secondary flows, for the same ${\mathit{Re}}_{\tau }$, $St$, and $S{t}_{\mathrm{el}}$, dampen the effect of particle charge. In a duct flow, vortical secondary flows enhance the cross-sectional particle mobility against the direction of electrostatic forces. Compared to a duct flow, in a channel, the wall-normal aerodynamic forces are weaker. Thus, electrical forces dominate their transport; the local particle concentration at the walls increases. Further, electrostatic charges cause a stronger correlation between the gas and particle velocities. In conclusion, despite being weak compared to the primary flow forces, secondary flow and electrostatic forces drive particle dynamics during pneumatic transport.     

Modelling and parameter estimation of trans-β-farnesene coordination polymerization

trans-β-Farnesene is a bio-derived terpene monomer that can polymerize, generating polymers with properties that can be similar to the properties of conventional petroleum-derived polymers. For this reason, in the present study, several coordination polymerizations of trans-β-farnesene are carried out using the Ziegler–Natta catalyst system composed by neodymium versatate (${\mathrm{NdV}}_3$), diisobutylaluminum hidride (DIBAH), and dimethyldichlorosilane (DMDCS) in order to evaluate the influence of key operation variables on the control of average molar masses and monomer conversion. A phenomenological model is proposed to describe the coordination polymerization of trans-β-farnesene, and the kinetic parameters required to simulate the reactions are estimated. The initial concentration of DIBAH used as a chain transfer agent (CTA) is calculated by a data reconciliation procedure since this very active compound can participate in undesired side reactions. It is shown that the initial monomer, DIBAH, and ${\mathrm{NdV}}_3$ concentrations exert strong influences on the monomer conversion and average molar masses of (poly)farnese while the temperature effect is not so pronounced. The proposed kinetic mechanism was able to predict well the experimental data collected during the reactions, with the successful reconciliation of CTA concentrations and estimation of model parameters.     

Mixing rules for high density polyethylene-polypropylene blends

High-density polyethylene (HDPE) and polypropylene (PP) blends of varying composition have been evaluated in an effort to establish a mixing rule for melt flow index (MFI). In addition, a previously established relationship between MFI and $\overline{M_w}$ for linear polymers was also evaluated for these blends. It was found that a parabolic relationship existed between the composition (by weight fraction) and MFI and that the MFI and $\overline{M_w}$ relationship held for this set of polymeric materials. Additionally, all properties and relationships were evaluated over five extrusion cycles, which showed minimal to no deviations over the five cycles.     

Effect of initial conditions on promotion and inhibition of breakup during filament contraction

Liquid filaments surrounded by a gas arise in nature and applications. As filaments contract, they either retract into spheres or disintegrate to form numerous droplets. In most previous computational studies, the filament's initial shape at time t = 0 has been idealized as a perfect cylinder capped off at both ends by identical hemispheres (radii R) and the fluid within which is at rest. Motivated by the fact that in experiments and applications the filament fluid is often not quiescent at t = 0, the effect of a nonzero initial velocity profile is examined for Newtonian filaments. A comprehensive phase diagram is presented in the space of L₀ (initial aspect ratio) and v_z,max (initial tip velocity) for filaments of intermediate Ohnesorge number (density ρ, viscosity μ, and surface tension γ) that delineates regions of the parameter space where breakup occurs from those where filaments contract to spheres without breakup.     

Artificial neural network to predict the power number of agitated tanks fed by CFD simulations

The power consumption of the agitator is a critical variable to consider in the design of a mixing system. It is generally evaluated through a dimensionless number known as the power number $N_p$. Multiple empirical equations exist to calculate the power number based on the Reynolds number $Re$ and dimensionless geometrical variables that characterize the tank, the impeller, and the height of the fluid. However, correlations perform poorly outside of the conditions in which they were established. We create a rich database of 100 k computational fluid dynamics (CFD) simulations. We simulate paddle and pitched blade turbines in unbaffled tanks from $Re$ 1 to 100 and use an artificial neural network (ANN) to create a robust and accurate predictor of the power number. We perform a mesh sensitivity analysis to verify the precision of the $N_p$ values given by the CFD simulations. To sample the 100 k mixers by their geometrical and physical properties, we use the Latin hypercube sampling (LHS) method. We then normalize the data with a MinMax transformation to put all features in the same scale and thus avoid bias during the ANN's training. Using a grid search cross-validation, we find the best architecture of the ANN that prevents overfitting and underfitting. Finally, we quantify the performance of the ANN by extracting 30% of the database, predicting the $N_p$ using the ANN, and evaluating the mean absolute percentage error. The mean absolute error in the ANN prediction is 0.5%, and its accuracy surpasses correlations even for untrained geometries.     

Effect of carbon doping on the structure and superconductivity in AlB2-type (Mo0.96Ti0.04)0.8${(\text{Mo}_{0.96}\text{Ti}_{0.04})}_{0.8}$B2

We report the effect of carbon doping in Ti-stabilized nonstoichiometric molybdenum diboride ${({\text{Mo}}_{0.96}{\text{Ti}}_{0.04})}_{0.8}$B₂, which exhibits bulk superconductivity below T_c = 7.0 K. It is found that ${({\text{Mo}}_{0.96}{\text{Ti}}_{0.04})}_{0.8}$${({\mathrm{B}}_{1-x}{\mathrm{C}}_x)}_2$ maintains the AlB₂-type phase with a uniform elemental distribution for x = 0.12 and 0.16. The substitution of carbon for boron leads to a slight increase in a-axis, a remarkable reduction in c-axis, the formation of planar defects along the (100) crystallographic planes, and a shift of the B 1s peaks toward higher binding energies. Contrary to ${({\text{Mo}}_{0.96}{\text{Ti}}_{0.04})}_{0.8}$B₂, however, no superconductivity is observed down to 1.8 K for the C-doped samples, which is ascribed to the electron filling of boron π bands resulting from the carbon doping.