Fluidized bed technologies underpin critical industries but face increasing pressure to evolve under sustainability, economic, and performance constraints. Despite significant advances in modeling, diagnostics, and particle technology, industrial practice remains largely anchored in legacy systems and incremental optimization. This divergence reflects structural barriers, including misaligned incentives, risk aversion, limited data accessibility, and differing time horizons between academia and industry. This article argues that accelerating innovation requires a shift toward structured, outcome-oriented collaboration frameworks. Emerging opportunities lie in hybrid modeling, soft sensing, and digitalization, enabling improved process understanding and control within existing infrastructure. Electrification and alternative feedstocks further motivate the need for rapid translation of scientific advances into deployable solutions. We highlight the importance of industry-defined challenge problems, shared pilot-scale platforms, and integrated techno-economic evaluation to bridge the gap between discovery and implementation. Strengthening academia-industry synergy is essential to leverage the next generation of efficient, low-emission, and high-value fluidized processes.

Industrial multiphase reactors remain among the most challenging systems to model due to their complexity, multiscale coupling, and persistent uncertainties in turbulence, interphase transport, and constitutive closures. While traditional approaches combining first-principles physics, empirical correlations, and numerical pragmatism have enabled substantial progress, fundamental limitations persist. This perspective outlines how advances in artificial intelligence (AI), high-performance computing, and, eventually, quantum computing (QC) can steer multiphase modeling toward industry-ready predictive capability with an accuracy unthinkable today. AI enables more generalizable, physics-constrained closures, while graphics processing units (GPUs) and exascale platforms already enable industry-scale simulations at unprecedented fidelity. Although QC is a longer-term prospect, hybrid quantum-classical approaches offer pathways to address complexities beyond classical limits. These developments promise to transform modeling workflows and engineering practice, with direct implications for scale-up, reliability, sustainability, and cost reduction. We highlight key research priorities, including multiphase-aware turbulence models, AI-assisted closures, hybrid solvers, computing architectures, and rigorous verification, validation, and uncertainty quantification.

Despite more than 100 years of commercialization of wide-ranging fluidized bed reactors, scale-up tools and methods have remained quite similar. To exploit the benefits of fluidized beds for the time-critical sustainability challenges, scale-up has to be implemented quicker and better. Correspondingly, a companion Part 1 ( Ind. Eng. Chem. Res. 2024, 63, 2519-2533) reviewed the evolution of the tools used in scaling up fluidized beds. Leveraging that, the current Part 2 aims to first overview the traditional pathway for scale-up and then propose a new pathway. Notably, instead of the traditional way of focusing on a linear sequence of progressively larger units, the emphasis is on the Phases of Discovery, Research, and Development, which apply the new tools consistently, as well as address risk mitigation and economics throughout. Based on an acrylonitrile case study, a Monte Carlo analysis indicates the new proposed pathway offers more promising economic feasibility, with net present value over 20 years (NPV20) of $310MM higher, along with 35% and 42% reductions in start-up and break-even times, respectively. The increase in costs for incorporating modeling efforts is insignificant compared to the overall benefits with respect to time and economics.

The scaling up of fluidized beds has been purposefully pursued for more than 100 years. Yet, over that time, scale-up tools have not significantly changed. Data analysis is typically a standard analysis of variances statistical exercise, perhaps reinforced with a design of experimental procedure. Flowsheeting and equipment design are based on institutional knowledge, albeit graphical user interface-based process flow models make that job more manageable. Advanced models such as computational fluid dynamics are used but often as a supplement and not a primary driver. As a result, the scale-up process for a fluidized bed can take more than 10 years. Fluidized beds remain at the forefront of the present time-critical sustainability challenges, e.g., carbon capture by particulate sorbents, methane-to-hydrogen, plastic-to-chemicals, etc. In view of the exigency toward net zero, today's scale-up efforts need to be accelerated, leveraging the advanced new tools that have become readily available. The problem is that such tools are often neglected, inadequately implemented, ineffectively resourced, and/or poorly understood. This motivated the current effort, which is targeted at reviewing how scale-up tools have evolved over the years and the promising new tools, addressing some of the barriers of these tools in the design and scale-up of fluidized beds, as well as contemplating what can be done to circumvent these barriers. As a follow up, a companion part 2 (Cocco,R. A.; Chew,J. W.Ind. Eng.Chem.Res.,submittedforpublication) proposes a new scale-up path leveraging the advanced tools to achieve timely implementation of the new green fluidized bed processes.

Circulating fluidized bed (CFB) risers using Group A particles have traditionally, mostly, been considered to operate in the fast fluidization regime, which consists of a core-annulus flow profile with solids refluxing in the annulus layer. High gas and solids flow riser studies, however, suggest the existence of additional types of flow behaviours. Therefore, more studies are still needed to help clear uncertainties of local solids flow patterns in CFB risers of Group A particles, especially at gas and solids flow rates at or near those in commercial units. In this study, riser density and local solids flux profiles were measured in 0.3 m diameter risers of three CFB units at gas velocities of 9-16 m/s and solids fluxes of up to 700 kg/s center dot m(2). A variety of radial solids flux profiles were obtained, including a parabolic profile with a peak flux at about the radial centre, a nearly flat profile across the riser cross-section and an inverted parabola with peak upwards flux near the wall. At high gas velocity and solids flux, risers have no solids downflow at the wall. Multiple fluidization regimes were found to exist in the riser. The bottom dense part of the riser was in the dense suspension upflow regime, and the dilute upper part was in the dilute pneumatic transport regime. With commercial fluid catalytic cracking (FCC) risers operating at nearly similar conditions as tested here, it is likely that they also have one or both fluidization regimes and not the traditional fast fluidization regime. The data in this study fitted well on the Kim et al. fluidization regime map.

Numerical methods like computational fluid dynamics (CFD) for predicting multiphase flow involving solid particles offer promising benefits to a wide range of applications. Validated predictions of fluidization behavior would benefit such industrially relevant applications. Accurate representation of the fluid-particle momentum exchange, i.e., the drag and buoyancy forces, is one key to developing reliable mathematical tools for studying fluid-particle hydrodynamics. A significant number of drag correlations exist, yet little (widely accepted) guidance is available on which formulation is the most appropriate. The contributions focusing on predicting the drag from flows through random assemblies of monodisperse spheres alone comprises a substantial subset of the available literature. An assumption of homogeneous distribution of solids is at the core of an overwhelming majority of these drag correlations, i.e., homogeneous drag correlations. Hence, we review correlations for predicting the drag force for flows past homogeneous suspensions of spherical particles with narrow size distributions, which are the foundation for (i) predicating the drag force of flow past non-ideal particle assemblies (non-spherical, polydisperse, etc.), and (ii) developing sub-grid drag closures. Here, many of the commonly used homogeneous drag models are overviewed in detail, including the datasets and methods for development and validation. Potential sources of error between model prediction are identified and the accumulation of uncertainty in model development is presented. We demonstrate a lack of drag model development and/or validation over the complete parameter space (solids volume fractions, Reynolds numbers, density ratios, etc.) relevant to industrial fluid particle flows, and discuss the underpinning physical and computational limitations driving this gap.

The Geldart classification reported in 1973 is widely acknowledged as one of the seminal papers on fluidization. Today, it remains the most used classification for particle fluidization. This is a simple comparison of the differential between particle and air densities versus Sauter-mean diameter, which successfully demarcates all particles into four distinctly different fluidization behaviors. To commemorate the 50th anniversary of this classification, an overview of the fluidization characteristics of the four Geldart groups is presented along with other such classifications, providing perspectives on why the Geldart classification is so universal. Some of the precautions that need to be considered when using this classification are also highlighted. This study is a tribute to the Geldart classification and is expected to be valuable as a summary of the advancements in the understanding of the Geldart groups to date.

This work marks the third in a series of experiments that were in a semi-circular, gas-fluidized bed with side jets. In this work, the particles are 1 mm ceramic beads. The bed is operated just at and slightly above and below the minimum fluidization velocity and additional fluidization is provided by two high-speed gas located on the sides of the bed near the flat, front face of the unit. Two primary measurements are taken: high-speed video recording of the front of the bed and bed pressure drop from a tap in the back of the bed. PIV is used to determine particle motion, characterized as a mean Froude number, from the high-speed video. A CFD-DEM model of the bed is presented using the recently released MFIX-Exa code. Four model subvariants are considered using two methods of representing the jets and two drag models, both of which are calibrated to exactly match the experimentally measured minimum fluidization velocity. Although it is more difficult to determine the jet penetration depths in a straightforward manner as in the previous works using Froude number contours, the CFD-DEM results compare quite well to the PIV measurements. Unfortunately, the good agreement of the solids-phase is overshadowed by significant disagreement in the gas-phase data. Specifically, the predicted time-averaged standard deviation of the pressure drop is found to be over an order of magnitude larger than measured. Due to the low value of the measurements, just 1% of the mean bed pressure drop, it seems possible that the data is in error. On the other hand, the model may not be accurately capturing pressure attenuation through an under-fluidized region in the back of the bed. Without the possibility additional experiments to test the validity of the data, this work is simply being reported as is without being able to indicate which, either the simulation or the experiment, is more correct.

Fluidized bed strippers play a significant role in fluid catalytic cracking (FCC) and fluid coking hydrocarbon processing operations. In the FCC process, the catalyst particles retained by the reactor cyclones contain substantial product hydrocarbon vapors. Failure to remove these vapors from the catalyst results in the loss of valuable products and undesirably high temperatures in the regenerator. In FCC applications, entrained and adsorbed hydrocarbon vapors are typically removed from the catalyst in a fluidized bed stripper using steam. In a fluid coker stripper, steam is propelled upwards to remove entrained and adsorbed hydrocarbons from the coke particles downflowing from the fluid coker reactor, thereby minimizing the carry-under of valuable hydrocarbon product. For both applications, several different proprietary and standard baffles have been used in these fluidized bed strippers. The most prevalent types of baffles are disk and donut trays, grating trays, horizontal sheds, and structured packings. This review summarizes studies available in the open literature on fluidized bed strippers as applied to the FCC and fluid coker unit operations. Overall, fluid catalytic cracking strippers, in particular disk and donut strippers, have received the most attention due to their much wider industrial usage. In past studies, experimental efforts have been dedicated to stripping efficiencies of various internals as a function of solids mass flux and stripping gas velocity, as well as stripper flooding and other flow dynamics issues. Typical commercial stripper operating problems have been pointed out, and methods to diagnose the problems have been identified. Fundamental theories on mass transfer in strippers are lacking.

This study was targeted at understanding the local flow and flooding behaviors of fluidized bed strippers. Bed density, pressure fluctuations and bubble void fraction were measured in a 0.6 m diameter stripper for disk and donut, as well as grating internals using FCC catalyst particles. The grating stripper had a higher bed density and operated smoothly without flooding over a wider range of gas and solids flows than the disk and donut stripper. Flooding in the disk and donut stripper was a sudden rather than a grad-ual occurrence when a limiting solids flux was exceeded. Increasing fines composition and adding holes to the disk and donut trays mitigated flooding to some extent. The gratings stripper had flattened para-bolic radial bubble void fraction profiles whereas the disk and donut stripper had characteristic M -shape profiles. The results here are expected to be valuable for the operation and design of gas-solids flu-idized bed strippers.(c) 2022 Published by Elsevier Ltd.

Particle attrition can be detrimental to fluidized bed applications. The jet cup test is commonly used to assess the relative tendency to attritbute gaps in the understanding stymie quantitative links between the lab-scale jet cup test and commercial-scale operation. In this study, high-speed video imaging was performed to understand better the particle hydrodynamics and kinetic energy distribution of Geldart Groups A and B particles in a conical jet cup, since particle-particle and particle- wall interactions are positively correlated with attrition rate. Particle tracking velocimetry (PTV) was used to measure the particle velocity and change in the particle velocity due to collisions. The results of these measurements were also compared with computational fluid dynamics- discrete element method (CFD-DEM) simulations. A mechanistic attrition model can be developed by measuring the kinematics of particle-wall interactions and how particles are recycled back into the jet. This represents a first step to creating this understanding by demonstrating the relative contributions of particle-particle and particle-wall collisions from experiments and the use of compartment models for understanding how particles cycle through regions of high collision probability. Once this mechanistic model is in place, quantitative comparisons between jet cup results and commercial fluidized bed operations can be made.

This year, 2022, we celebrate the 100-year anniversary of the commercialization of the fluidized bed reactor. In those years, many new processes have been developed, with many of them considered breakthrough technolo-gies, replacing technologies that were no longer considered competitive. Fluidized beds have the advantages of superior heat transfer and the ability to continuously move solids during operation, along with a list of other attributes. As a result, processes spanning coal and biomass gasification, pyrolysis, fluidized catalyst cracking, acrylonitrile, polyethylene, oxychlorination, and polycrystalline silicon have prospered with the application of fluidized bed technology. Expect that list to continue with current efforts in chemical looping, plastic pyrolysis, methane pyrolysis, and propane dehydrogenation, to name a few. In the past 100 years, the landscape of fluidized beds and circulating fluidized bed reactors has grown in numbers and applications. With those applications comes 100 years of scaling up and optimizing those fluidized beds. We have seen engineering go from an incremental approach to a more fundamental approach using so-phisticated models and relevant cold flow experimentation. That engineering work process is still changing. With the onset of better computational platforms, models, and experimental techniques, combined with better sta-tistical tools (e.g., machine learning) and control systems (e.g., artificial intelligence), the application of fluidized bed technology will require less capital, less operating costs, and allow for commercialization in less time.

Measurements of gas-solid particle flow in a large-scale stripper unit are reported. The experiments tar-get industrial (pilot) scale measurements of gas-particle flow that can be used to validate numerical methods for modeling multiphase flows, such as computational fluid dynamics coupled to the discrete element method (CFD-DEM). Specifically, experiments were performed with Geldart Group B (533 lm) glass beads in a stripper unit. In general, previous CFD-DEM validation datasets were performed in bench -top experimental systems and limited to O(105) particles. Here, the number of particles in the isolated section of the stripper is O(107) for all tests. Measurements are reported for a-2 m tall section of the-1 m diameter stripper. Radial mass flux profiles measured at five axial positions and three axial pressure drop measurements are reported for six operating conditions. (c) 2022 Elsevier Ltd. All rights reserved.

In order to enhance the understanding of the influences of bubble characteristics in bubbling fluidized beds of Gaussian and lognormal particle size distributions (PSDs) of Geldart Group B particles, machine learning tools were harnessed. The PSDs had the same Sauter-mean diameter and widths (i.e., ratio of standard deviation to Sauter-mean diameter) of between 10 and 30% and 10-70%, respectively. Self-organizing maps (SOMs) analysis of more than a thousand data rows each of Gaussian and lognormal PSD data indicate that bubble velocity, frequency, length, and probability are highly correlated. The optimal number of data assemblies that either the Gaussian or lognormal dataset can be divided into per the Calinski-Harabasz criterion was determined to be three, and it was found that the critical parameter that underlies the demarcation of the datasets was PSD width. This agrees with an earlier study on clusters, wherein the non-monodispersity of the particle systems was also responsible for the division of the dataset into distinct data assemblies. Furthermore, the number-based frequency of the particle species was better correlated with the bubbles than the mass-based one. The key highlight is the predominant influence of PSD width in demarcating the datasets into distinct data assemblies, which underscores the need to account for the polydispersity of particle systems and provides valuable insights towards model development.

The overall goal of this two-phase project is to implement performance improvements of the Multiphase Flow with Interphase Exchanges (MFIX) Discrete Element Model (DEM) code that enable a transformative shift for industrial use. Prior to this effort, the largest simulations performed using MFIX are O(107) particles. This falls short of the O(109) particle simulations that must be completed on a timescale of days or weeks (vs. months or years) to enable simulations with physically-relevant domain sizes to be incorporated into industrial design cycles within five years. This was accomplished by tailoring best-in-class practices to bear on the unique challenges posed by the MFIX-DEM algorithm and code base. Scientific simulations (e.g., in cosmology, turbulent combustion) routinely use massively parallel computing to update far more particles in short wall clock times. Results from Phase 1 (1.5 years in duration) indicated significant gains in speed were possible for a wide range of benchmark cases. Moreover, a survey sent to >35 companies indicates that the timing is ideal for such an enhanced tool, with >80% of the respondents indicating that DEM is already value-added or will be within the next 5 years, and >70% of the respondents indicating that improved speed is the top computational priority. In Phase 2 (3.5 years in duration), the two major barriers that hinder industry from effectively using multiphase Computational Fluid Dynamics (CFD) to cut costs and improve performance, namely computational overhead and confidence in predictions, continued to be addressed. Regarding the former, the results from Phase 1 to guide the effort, with enhancements focused on an improved time-stepping algorithm and particle sorting. Four target problems of 1 billion particles each and increasing complexity were identified: homogeneous cooling, tumbler with continuous particle size distribution, discharge from a rectangular hopper and a cylindrical riser. Each of these were successfully simulated for relevant time scales (on order of seconds) using less than 24 hours of wall clock time. These represent the first 1-billion particle DEM simulations performed with MFIX, namely using the MFIX-Exa code. This code is currently under development at NETL in collaboration with Lawrence Berkeley National Laboratory. Regarding the second barrier on predictive uncertainty, experiments from Phase 1 (interacting nozzles - hydrodynamics only) and Phase 2 (very small-scale segregation experiments) were used to demonstrate the ability of two simplified approaches to uncertainty quantification (UQ). By limiting the number of particles, UQ based on the simplified treatment was compared to standard UQ, which was shown to have much higher computational demands. Experiments were also performed on a pilot-scale stripper unit to provide validation data for future CFD-DEM simulations and UQ.

This work presents both an experimental and a numerical study on the effect of solid loading and inlet aspect ratio on cyclone performances in a circulating fluidized bed process (CFB). The unit operates at ambient temperature, atmospheric pressure with air and Geldart Group A glass bead particles of median diameter of 42.2 mu m. The experimental study investigates the effect of solid loading (i.e. normalized solid loading C/C-max from 0.014 to 0.41) and inlet aspect ratio (from 3 to 7, keeping the inlet area constant) on both gas separation and solid collection efficiencies (fractional and global) and pressure drop. Experimental results showed that cyclone pressure drop is primarily affected by solid loading. This parameter first decreases for normalized loadings up to about 0.127 before increasing for higher values. It was also found that global solid efficiency increases with solid loadings and is strongly favored by increasing the inlet aspect ratio. The numerical study was carried out with the software Barracuda VR (R). It first consists on establishing a methodology on how to simulate cyclones, especially regarding the type of dipleg boundary conditions (BC). The gas flow within the cyclone dipleg, either upward or downward, was found to be a key information for the simulation. For normalized solid loadings up to 0.127, CFD results showed that the gas flows upward in the dipleg with a gas flow rate equal to the loop seal aeration of the CFB while for higher values, the gas flow is downward corresponding to a certain amount of gas underflow. The most appropriate boundary condition to employ in the cyclone dipleg outlet in order to represent the gas flow behavior was found to be a pressure BC whose value leads to either an upward or a downward gas flow. Con sidering this pressure BC and adapting its value to match the experimental dipleg aeration or gas underflow, the CFD results were found to be in very good agreement compared to experimental data. (C) 2020 Elsevier B.V. All rights reserved.

Both fast and turbulent fluidized beds exhibit entrainment, but the differences in the flow phenomena are not well understood.This study targeted a comparative analysis of the cluster (or streamer), mass flux, and segregation datasets from these two fluidization regimes.The particle systems were narrow particle size distributions (PSDs), binary mixtures, or broad PSDs of Geldart Group B particles.Relative to the fast fluidized bed, the turbulent bed exhibited (i) higher cluster probability and frequency, but lower cluster duration; (ii) lower local mass flux; and (iii) similar segregation extents.Regarding clusters, the relative dominance of the variables on probability was similar for both regimes, but there was a difference for probability and frequency.For overall mass flux, particle-related properties were more dominant with the turbulent bed.As for segregation, the radial position was the most influential in the fast fluidized bed, but the least in the turbulent one.

The clustering phenomenon is an important characteristic of fluidized bed systems, so much attention has been given to understanding such unstable, transient features through both experiments and simulations. A review has pointed out that, because of the interplay of multiple factors, relationships are at times unclear even within the same study. For such non-linear and multi-dimensional problems, machine learning tools are proficient. In this study, self-organizing map (SOM) analysis was harnessed to classify 1188 circulating fluidized bed (CFB) riser cluster datasets of Geldart Group B particles into potential smaller data assemblies, in order to determine the key influence(s) responsible for the demarcation. Two distinct data assemblies were identified, with one constituted by the monodisperse particle systems (i.e., three narrow particle size distributions (PSDs)), while the other by the non-monodisperse particle systems (i.e., two binary mixtures and one broad PSD). Specifically, the clusters formed by the non-monodisperse systems were distinctively smaller than those of monodisperse ones. This suggests that multiple particle types hindered the growth of clusters, which has been tied to hydrodynamic screening, unequal charging and unequal damping effects that are unique to particle mixtures. More studies are needed to unveil the underlying mechanisms of such different clusters between the monodisperse versus nonmonodisperse particle systems.