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.     

Robust control of discrete minimum and non-minimum phase systems via data-driven virtual reference feedback tuning and IMC

In this paper, we develop a novel robust control approach for discrete minimum and non-minimum phase systems via a combined data-driven virtual reference feedback tuning ($\text{VRFT}$) and internal model control (IMC) scheme. The first step in the conventional $\text{VRFT}$ method controller design is the selection of the closed-loop reference model ($M\left(z\right)$), and $M\left(z\right)$ selection is still an open problem. The integration of the $\mathrm{IMC}$ scheme and the VRFT method provides the advantage of flexibility in controller design due to the incorporation of the $\mathrm{IMC}$ filter. As a result, the proposed design method begins with the selection of $M\left(z\right)$ and $\mathrm{IMC}$ filter. Unlike the standard $\text{VRFT}$ method, the proposed combined $\text{VRFT}$ and $\mathrm{IMC}$ design approach has the unique feature of taking into account a robustness property of dynamics, namely, maximum sensitivity ($M_{\mathrm{s}}$) as the design specification for the $M\left(z\right)$ and IMC filter selection. Moreover, the proposed approach includes a robustness specification that resolves the trade-off between performance and robustness in real-time controller design. Furthermore, the robustness guarantee with plant uncertainties and controller fragility is elucidated. The proposed approach is validated using numerical simulations and experimental validation through the temperature control process. Compared to conventional $\text{VRFT}$ controllers, experimental and simulation results show that the proposed controllers have less tracking error, minimize control effort, and improve robustness.     

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.     

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     

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.     

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.     

Computational fluid dynamic study of polycaprolactone nanoparticles precipitation in a co-flow capillary micro-tube

Polycaprolactone nanoparticles (NPs) were produced in the co-flow glass capillary device with $250\mathrm{\mu m}$ tip dimension. NPs size was 990 nm for continuous phase velocity, and $u_c=0.05\mathrm{m}/\mathrm{s}$ and 426 nm for $u_c=0.2\mathrm{m}/\mathrm{s}$. The droplet formation process in the co-flow microchannel was also simulated using computational fluid dynamic (CFD). Employing a digital image analysis technique, a cut-off value of 0.81 for the dispersed phase volume fraction was proposed for the determination of droplet size. A scenario was expressed for NP formation from micro-droplets. Moreover, after conducting 27 CFD simulations, a dimensionless exponential form correlation was proposed for droplet size estimation. In this generalized map, there were three distinct regions based on the relative capillary numbers of continuous to dispersed phases. It was revealed that at a constant dispersed phase velocity, by increasing the continuous phase velocity, more small NPs are formed. The results show that the ratio of micro-droplets to NPs size was between 735 and 755.     

Development of poly(ε‐caprolactone)/pine resin blends: Study of thermal,mechanical, and antimicrobial properties

Blends of poly(ε‐caprolactone) (PCL) with pine resin, an extract from the plant Pinus caribaea—Hondurensis was prepared by melt mixing in mass ratios from 90/10 to 50/50. The thermal, crystallization, morphological, and mechanical properties of the blends were studied by differential scanning calorimetry, Fourier transform infrared spectroscopy, polarized optical microscopy, scanning electron microscopy, and tensile test. Enzymatic degradation of the blends was investigated using porcine pancreatic and Candida rugosa lipase. Antimicrobial activity of the blends was tested against four strains of bacteria; Staphylococcus aureas, Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa using the zone inhibition method. Miscibility of the blends was confirmed by the depression in the equilibrium melting temperature ( $T_{\mathrm{m}}^0$) of PCL estimated from Hoffman–Weeks plot and the presence of extinction rings in the spherulites of blended PCL. Interactions between the two components involved the carbonyl and the C‐O‐C groups. The tensile strength of the blends with low pine resin content was comparable to PCL but decreased with higher pine gum content. Enzymatic degradation of the blends increased with increasing pine resin content. The blends showed antimicrobial property with all the bacteria except E. coli. The developed biomaterial shows promising candidacy in medical applications. POLYM. ENG. SCI., 59:E32–E41, 2019. © 2018 Society of Plastics Engineers     

Neural network feature and architecture optimization for injection molding surface defect prediction of model polypropylene

The industry is heading towards digitalization with production and quality assurance of processes and products. Novel practices such as machine learning or artificial intelligence make it possible for highly complex and nonlinear occurrences to be modeled and predicted with immense accuracy when real experiences are used to train the algorithm. For example, supervised learning in a closed-loop system allows the user to analyze and predict outcomes and gives it the ability to adapt and add intelligence to the current system. This study focuses on the development of a neural network (NN) for surface defect prediction in injection molding of model polypropylene. Feature optimization allows us to conclude that rheological parameters such as the melt flow index and relaxation time ($\lambda$) can improve predictive accuracy. Furthermore, Bayesian optimization is implemented to optimize the NN structure. The optimization approach allowed for a cross-validation (CV) accuracy of 90.2% ± 4.4% with only five input parameters, while the seven-input parameter optimized structure arrived at a CV accuracy of 92.4% ± 11.4%. Although the full-feature structure optimized with Bayesian optimization concluded with slightly higher accuracy, the error range dramatically increased, meaning that this structure tends to overfit.     

A modified fuzzy clustering framework for catalyst activity monitoring in the selective catalytic reduction system

The catalyst activity monitoring in the selective catalytic reduction (SCR) system is of great importance for safety and economic operation in the power plant. To address the problem, a framework based on clustering considering time delay has been proposed. A compound parameter, $q$, is put forward in this paper as a strategy to remove the influences from gas volume (power output), inlet NO_x concentration, and outlet NO_x concentration to the ammonia amount. A modified entropy-based fuzzy clustering (EFC) method is proposed by a threshold varying model and then tested for its efficiency by four datasets from the University of California, Irvine (UCI) machine learning repository. With the maximum mutual information entropy coefficient (MIC) method for detecting time delay and the modified EFC method, process data from three working levels are handled for automatically obtained clustering centres. The proposed activity value, $\mu$, is then calculated based on 1440 process data before and after the catalyst replacement shown in boxplot figures. The results of the framework are analyzed to be in accordance with the real working conditions, with $\mu$ values and fluctuation ranges starting to fall near first from the 721st sample in the 24th box.     

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.     

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.     

Drawbacks of phase change models in simulating flashing of steam with a subsaturation downstream boundary condition

The development of phase change models applicable to a wide range of temperatures, pressures, and mass flow rates is primarily limited by the metastable or partially stable behaviour of the fluid. Due to this, the fluid does not change phase even after crossing saturation conditions. Most liquid–vapour phase change models have been developed primarily for the cavitation process where the phase change is not sustained, occurs in a very narrow region of space, or occurs under equilibrium conditions. In this paper, the mechanism of phase change is discussed along with the review of three different computational fluid dynamics (CFD)-based phase change methods available in OpenFOAM, which are used to simulate flashing of steam, in applications related to steam assisted gravity drainage (SAGD) systems. The first method is based on barotropic compressibility (BC) used with the realizable $k-\epsilon$ turbulence model, the second combination is of mass transfer model (MTM) with the realizable $k-\epsilon$ turbulence model, and the third one is based on two fluid (TF) method along with a family of $k-\omega$ turbulence models. These methods are tested on a converging–diverging nozzle with pressure driven phase change. It is demonstrated that these methods are not able to adjust their physics to different depressurization rates, do not account for liquid to be in superheated conditions, and have significant discrepancies with experimental results. In the end, better approaches to model this category of phase change are discussed.     

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 ${\mathit{Re}}_m$.     

X-ray characterization of three possible edible oleogelators: Experiment and theory

Three oleogelator molecules (Triacontane (TC), Stearic acid (SA), and Behenyl Lignocerate (BL)) were studied individually, in pairs, or all together to make an oleogel using triolein as the oil. WAXS, SAXS and USAXS were used to elucidate the solid structures from angstroms to a few micrometers. A two-dimensional mapping of atomic positions for each molecule was carried out to understand the crystalline multilayer structures formed. We assumed that the molecules were rigidly extended and that they underwent no significant (hindered) rotations so that the free energy is determined by the Lennard-Jones interactions of closely packed multilayers. TC molecules were predicted to form a tilt angle of ${\theta }_t\approx 33°$, yielding a SAXS line at $q\approx 0.194$ Å^─1, in acceptable agreement with the measured $q=0.181{\AA }^{-1}$. For SA crystals ${\theta }_t\approx 33°$ (predicted) yielding a SAXS line at $q=0.150{\AA }^{-1}$ compared to $q=0.159{\AA }^{-1}$ (observed). No mixed crystals were observed for any pair of molecules or when all three were used. USAXS data showed that SA forms large nanocrystals compared to TC and BL. All three combinations of molecular pairs showed basic scatterers smaller or similar to those of individual molecules. The theory presented here, together with the experimental results, showed why no mixed crystals are formed from two or all three molecules. Data from the USAXS region suggested that, when using all three molecules, a more compact fractal structure was obtained, compared with those if one or two of the molecules were used.     

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.     

SSAE-KPLS: A quality-related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares

Kernel partial least squares (KPLS) is widely employed to address the issue of nonlinearity inherent in complex industrial processes. However, KPLS can only extract shallow features from process measurements. This paper proposes a new quality-related process monitoring method via integrating stacked sparse autoencoder (SSAE) with KPLS (SSAE-KPLS). First, an SSAE model is employed to exploit the nonlinearity within process variables. Through SSAE, hierarchical features are learned to extract latent representations of process variables from multiple sparse autoencoder (SAE) layers. Second, the learned hierarchical features from SSAE are used as input, and the final quality variables are used as output. A KPLS model is then built to exploit the nonlinear relationship between the hierarchical features extracted from process variables and the final product quality for process monitoring. Third, Hotelling's $T^2$ and $Q$ statistics are employed to detect the quality-unrelated and quality-related faults, respectively. Finally, experiments on a numerical example and the commonly used industrial benchmark of the Tennessee Eastman process (TEP) are conducted to illustrate the efficacy and merits of the proposed SSAE-KPLS based quality-related process monitoring method by comparing it with other related methods.     

Insight of molecular interactions between short-chain tetraalkylammonium bromides and cetyltrimethylammonium bromide: A spectroscopic and thermodynamic approach

The influence of tetraalkylammonium salts, viz., tetraethylammonium, tetrapropylammonium, and tetrabutylammonium bromides (0.005, 0.010, 0.015 mol kg⁻¹) on the micellar behavior of aqueous solutions of the cationic surfactant cetyltrimethylammonium bromide (CTAB, 0.2–2 mmol kg⁻¹) over the 298.15–313.15 K temperature range has been studied by conductometric method. From conductivity versus surfactant concentration plots, the critical micelle concentration (CMC) of CTAB has been determined, which shows that the tetraalkylammonium bromides promote the formation of CTAB aggregates. Further, from the temperature dependence of CMC values, the degree of ionization, the counterion binding constant along with some thermodynamic parameters of micellization, such as standard free energy change ($\mathrm{\Delta }{G}_m^o$), standard enthalpy change ($\mathrm{\Delta }{H}_m^o$), standard entropy change ($\mathrm{\Delta }{S}_m^o$) have been calculated. From the values of $\mathrm{\Delta }{G}_m^o$, $\mathrm{\Delta }{H}_m^o$ and $\mathrm{\Delta }{S}_m^o$, it has been concluded that our ternary system is both enthalpy as well as entropy controlled. Similar CMC values were obtained from UV–Visible spectrometry measurements, using pyrene as a probe at ambient temperature. Also ¹H-NMR and FTIR methods give a greater understanding of the molecular scale interactions between the tetraalkylammonium bromides and the cationic surfactant.