The liquid-gas mass transfer coefficient in open channel flow is correlated to the turbulent kinetic energy at the interface

Mass transfer coefficients at the gas-liquid interface were investigated for different flow configuration systems, a stirred tank reactor and a gravity pipe. Computational fluid dynamics (CFD) simulations were performed for all tested experimental conditions. Since a poorly soluble gas (oxygen) was used, the overall mass transfer coefficient was clearly correlated to the hydrodynamic conditions in the liquid phase. However, a generic correlation between averaged interfacial liquid velocity and mass transfer coefficients was not found for both geometries. Finally, the averaged turbulent kinetic energy (TKE) at the interface is the most relevant parameter that was correlated to the mass transfer coefficient for both systems. The same relationship between oxygen mass transfer coefficient K _L,O2 and TKE () can be applied for the two geometries investigated.     

Experimental Methods in Chemical Engineering: Particle Size Distribution by Laser Diffraction—PSD

Particle size is the top cited physical property researchers report in The Canadian Journal of Chemical Engineering and among the top properties in all science disciplines. ^[1] Techniques to measure particle size distribution (PSD) include physical operations like sieving and sedimentation, and spectroscopic techniques like laser diffraction image analysis based on optical and electron microscopy, and elecro‐zone instruments. Here we concentrate on laser diffraction analysis (LDA) and review its basic principles, operations, limitations, uncertainties, and mention how it compares to other techniques. LDA is an instantaneous, user‐friendly, convenient, and non‐destructive method to assess PSD of inorganic powders. It measures the scattering angle and intensity of light after it passes through diluted particle dispersions suspended in either a gas or liquid. The Mie theory is an exact solution to resolve the diffraction intensity of light caused by particles that applies to while the Fraunhoffer approximation applies only to particles greater than 20 m. The 95 % confidence interval of five measurements of 56 m and 0.1 m irregularly shaped polyhedrons was . Based on a bibliometric analysis of LDA of the top 10 000 cited articles in 2016 and 2017, the major research clusters are: particle measurement, powder behaviour, pharmacy, comminution, and adsorption. Future work will continue to introduce more laser sources, combine multiple technologies, implement mobile light sources (dynamic light scattering), and better define characterize irregularly shaped particles.     

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.     

Experimental methods in chemical engineering: X‐ray photoelectron spectroscopy‐XPS

X‐ray photoelectron spectroscopy (XPS) is a quantitative surface analysis technique used to identify the elemental composition, empirical formula, chemical state, and electronic state of an element. The kinetic energy of the electrons escaping from the material surface irradiated by an x‐ray beam produces a spectrum. XPS identifies chemical species and quantifies their content and the interactions between surface species. It is minimally destructive and is sensitive to a depth between 1–10nm. The elemental sensitivity is in the order of 0.1 atomic %. It requires ultra high vacuum ( Pa) in the analysis chamber and measurement time varies from minutes to hours per sample depending on the analyte. XPS dates back 50 years ago. New spectrometers, detectors, and variable size photon beams, reduce analysis time and increase spatial resolution. An XPS bibliometric map of the 10 000 articles indexed by Web of Science^[1] identifies five research clusters: (i) nanoparticles, thin films, and surfaces; (ii) catalysis, oxidation, reduction, stability, and oxides; (iii) nanocomposites, graphene, graphite, and electro‐chemistry; (iv) photocatalysis, water, visible light, and ; and (v) adsorption, aqueous solutions, and waste water.     

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.     

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.     

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.     

Nonlinear desorption activation energy from TPD curves: Analysis of the influence of initial values for the regression procedure

Thermal programmed desorption (TPD) is a powerful technique for materials and catalysts characterization. By analyzing TPD curves, it is possible to calculate important parameters as the desorption activation energy, E _d , that depends on the surface coverage (θ) by a nonlinear polynomial function, ie, . The Polanyi-Wigner equation, , can be used as theoretical basis to calculate this parameter, by a fitting regression procedure starting from experimental TPD data. Different degrees (k) for this polynomial equation and different initial values of the frequency factor A(θ) were considered and discussed to obtain the univocal value of desorption energy. Three different Pt and Co based catalysts, suitable for hydrogenation reactions, have been considered as case studies for the application and validation of the proposed calculation procedure.     

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.     

Mixing in a co‐current upflow bubble column reactors with and without internals

Liquid phase mixing is a phenomenon that results mainly due to convective and turbulent flow fields, which are generated by hydrodynamic interactions between the gas and liquid phases within a continuous co‐current upflow bubble column reactor. The extent of liquid phase mixing is usually quantified through the mixing time, or the axial dispersion coefficient. In the present work, the computational fluid dynamics (CFD) simulations for mixing and RTD in a continuous bubble column (with and without internals) are performed by using OpenFOAM 2.3.1. The superficial gas velocities were 0.014, 0.088, and 0.221 m/s and the superficial liquid velocities were 0.005 and 0.014 m/s. The simulations have been performed for three different configurations of the bubble column, that is, (a) an open bubble column, (b) a column with one vertical central rod of 36 mm diameter, (c) a column with the same central rod and four vertical additional rods of 12 mm diameter. The effects of superficial gas and liquid velocities and column internals were investigated on liquid phase mixing and the axial dispersion coefficient. Comparisons have been made between the experimental measurements and the CFD simulations.

     

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.     

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.     

Advanced treatment of phenol by H2O2/UV/activated carbon coupling: Influence of homogeneous and heterogeneous phase

In this work, a heterogeneous catalytic wet peroxide process combining activated carbon (AC) and hydrogen peroxide (H₂O₂)/ultraviolet radiation was applied for the aqueous‐phase removal of phenol. The influence of the pH and peroxide concentration were determined according to a factorial plan. The kinetic contribution of radical mechanisms () was estimated using a radical scavenger (tert‐butyl alcohol). The degradation kinetics was modelled by a global pseudo‐first‐order kinetic model based on the sum of the effects during the treatment process. The results showed that these two variables significantly affected the percentage removal. The peroxide concentration exerted a positive effect (i.e., as the H₂O₂ concentration increased, the percentage removal also increased). Additionally, as the pH value increased, the degradation accelerated, and the kinetic constant (k_homogeneous) increased from 0.00938 min^?1 to 0.02772 min^?1. The results obtained in the presence of AC demonstrated the ability of AC to ameliorate the degradation of phenol; for example, was 45.69 % to 41.35 %.

     

The critical mass for the unconfined vapour cloud explosion of compressed and liquid hydrogen

The (unconfined) vapour cloud explosion (VCE) is a dramatic phenomenon that generates a severe pressure wave with a high potential to damage assets and produce injuries in the far field. This definition applies also to hydrogen. Nevertheless, no clear tools and methodology have been so far developed and tested for this highly reactive gas, and even advanced numerical simulations lack validation and suffer from large uncertainties. In this view, the comprehension of the physic which subtends this dramatic phenomenon for the specific case of hydrogen is still a central issue. This paper revises some of the most adopted theories on VCE based on classical acoustic theory and models for pressure wave propagation and provides a consequence-based, threshold (minimum) value for the critical mass of hydrogen $m_f^{\text{crit}}\left(4.0\mathrm{kg}\right)$ which is needed—at a stoichiometric concentration in air—for a vapour cloud to behave as a VCE. To this regard, any non-stoichiometric hydrogen concentration in air or lower amount of hydrogen would decrease either the flame Mach number $M_{\mathrm{f}}$ or the total energy, thus resulting in negligible overpressure. In this sense, the effects of buoyancy, diffusivity, and weather conditions on the dispersion of hydrogen should be taken into account. The results are valid either for compressed or cryogenic liquid tanks and can be adopted for the sake of distinction between hydrogen flash fire and VCE; for the hazard analysis of hydrogen production and storage; and more in general for the risk assessment of hydrogen systems.     

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.     

An effective characterization procedure for petroleum reservoir fluids using molecular type methods and cubic equations of state

Two eminent molecular type composition methods (paraffins, naphthenes, and aromatics [PNA] and saturates, aromatics, and polynuclear aromatics [SAP]) are employed to construct new characterization procedures for predicting the phase behaviour of petroleum fluids using a modified Peng–Robinson equation of state. The PNA and SAP methods divide a petroleum fraction into (PNA) and (SAP) homologous groups, respectively. Two generalized models are developed to predict the physical properties ($\mathrm{Mw},\mathrm{SG},T_b$) and equation of state (EOS) parameters ($T_c,P_c,\omega$) for both PNA and SAP sub-fractions. Each generalized model covers 18 different correlations in a single mathematical form that enables the model to return 18 outputs for PNA and SAP sub-fraction parameters. A new lumping method is also developed to convert triple PNA or SAP pseudo-components into single characterized fractions. Accordingly, seven different characterization procedures are introduced and compared with one another. The first two procedures are completely constructed based on the proposed models, and the other procedures encompass the models already developed. The results obtained from the simulation of the differential liberation test for 12 diverse reservoir fluids and bubble pressure prediction for 40 oil samples revealed that the first two methods (1 and 2) could enhance the abilities of the traditional characterization procedures for reservoir fluid modelling. The mean value of average absolute relative deviations (AARDs) over a total of 52 oil samples is about 6.5% for the proposed methods and is about 13.2% for the best previously existing methods. Moreover, an efficient workflow is provided for the parameter tuning process, which is notably capable of reducing the level of prediction errors using only three adjustable parameters.     

A new approach for describing and solving the reversible Briggs-Haldane mechanism using immobilized enzyme

Recently immobilized enzymes have been widely used in industrial processes due to their outstanding advantages, such as high stability and recyclability; however, their kinetic behaviour is generally controlled by mass diffusion effects. Thus, in order to improve these enzymatic processes, a clear discernment between the kinetic and diffusion mechanisms that control the production of the metabolite require investigation. In practice, it is typical to establish apparent kinetics for immobilized enzyme operations, and the validity of the apparent kinetics is restricted to the studied cases. In this work, a new approach for mathematically describing the kinetic and diffusion mechanics in an immobilized biocatalyst bead is established, in which the fraction of residual enzymatic activity is included, and is defined as a measure of the active and available enzymes in the bead porous network. In addition, the diffusion and kinetic mechanisms are described by the effective diffusion coefficient and the free enzyme kinetics, since the porous network of the bead is assumed as the bioreaction volume. Therefore, free enzyme kinetics were determined from glucose to fructose bioconversion using a stirred tank reactor with free glucose-isomerase, in which substrate and enzyme concentrations and temperature were varied. The fraction of residual enzymatic activity () and the effective diffusion coefficient () were obtained from the isomerization of glucose to fructose using a stirred tank reactor with immobilized glucose-isomerase in calcium alginate beads at different substrate and enzyme concentrations. Finally, simulations were carried out to establish the bioreaction solid-phase characteristics that most significantly influence productivity.     

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.     

Numerical study of the mixing characteristics in an air swirling fluidized bed with monodisperse and multi-sized particles

Powder mixers are used in many industries. In the present work, a new type of air swirling mixer was designed and optimized with eight horizontally arranged inlet pipes at the tangential inlet angle of 35°. The mixing of multi-sized spherical particles (2.0, 3.0, 4.0, and 5.0 mm) was numerically investigated in the air swirling mixer by coupled computational fluid dynamics–discrete element method. The numerical results showed that multi-sized particles achieved comparable mixing performance to monodisperse particles. The Lacey index for multi-sized particles increased initially, and then reached a maximum value at 0.824. The upward velocity of the particles, $v_{\mathrm{z}}$, increased initially, and then decreased to zero along the bed height. The maximum value of $v_{\mathrm{z}}$ occurred at a height of 40 mm. Particle radial velocity was larger near the wall than at the mixer tube centre area. The smallest particles aggregated in three layers. The collision number of the particles reached a maximum at bed height of 120 mm, which was consistent with the position of the maximum stress of the particles against the tube wall.     

Experimental methods in chemical engineering: Reactors—fluidized beds

Industry relies on fluidized beds to synthesize chemicals (acrylonitrile, maleic anhydride, titanium dioxide, vinyl chloride), combust coal, dry powders, and treat waste. Fluidized bed folklore declares that they are hard to scale‐up and the gas phase is backmixed. Commercial failures that disregard standard design criteria around powder management, gas/solids injection, and mixing reinforce this belief. However, engineers select fluidized beds for processes that are impractical with conventional technologies to achieve economies of scale for highly exothermic, endothermic, or explosive reactions, for catalysts that deactivate in seconds (or minutes), and for chemistry that requires multiple dosing cycles. Failures are more frequent for these challenging applications. For this reason, researchers study reaction kinetics in fixed beds despite internal mass transfer limitations and axial and radial temperature and concentration gradients. Fluidized bed hydrodynamics vary with powder properties (particle diameter, size distribution, density, sphericity), operating conditions (gas density, viscosity, temperature, pressure), reactor geometry (diameter, height, mass, grid geometry). The minimum fluidization velocity (U_mf) is a property that identifies the transition from the fixed bed regime to the fluidized bed regime and equals the gas velocity at which the upward drag force equals the weight of the powder. At the experimental scale, fluidized beds operate isothermally, solids are completely backmixed, and the gas phase is close to plug flow (). Here, we describe the relationship between powder properties and fluidization quality, list experimental techniques, describe recent applications, and gas phase hydrodynamics and uncertainties.