High pressure fluidization

Polymers are often produced in pressurized fluidized beds. Large surface area and good mixing properties are key advantages of a fluidized bed. Despite decades of research, fluidization is still not completely understood. Especially since most academic research on fluidized beds is performed at atmospheric conditions. The objective of this work is to gain knowledge on fluidization of polymeric particles at elevated operating pressure, employing a combined modelling and experimental approach. The discrete particle model (DPM) and the two-fluid model (TFM) are used to gain detailed information of porosity distribution, bubble properties and solids mixing. Electrical capacitance tomography (ECT) was used to measure porosity distributions in a 30 cm diameter gas-fluidized bed. ECT is a relatively cheap and fast technique based on the difference in permittivity of air and polymeric particles. ECT requires a sophisticated reconstruction technique, for which the \cite{landweber1951} iteration method was used in this work. Since the permittivity and porosity are not linearly correlated, a concentration model is needed. In this work, an inverted Maxwell model is used for this purpose, since it represents the bubble emulsion structure best. Since opening and emptying the pressure vessel requires about 2 days, an advanced calibration method was developed to prevent frequent opening of the vessel. In this approach the permittivity of a packed bed is measured at the beginning and at the end of each measurement. If the calibration has changed during the measurement, the measurement is not used. Solids mixing is key in industrial reactors, since it prevents hot spots, it prevents undesired clustering and it ensures mixed product removal. Solids mixing is investigated using the DPM and TFM. A new method to quantify the degree of mixing based on the distance between particles and their initial neighbour was developed. The initial neighbour method performed better than existing methods since it is independent of the computational grid and the particle colouring, it can be used in all directions and it is highly reproducible. With increasing pressure five observations were made, which are explained below Emulsion phase becomes more porous. The emulsion phase becomes more porous with increasing operating pressure. At atmospheric operating pressure the porosity of the emulsion phase is similar to the porosity of a randomly packed bed (0.4), while at 20 bar the porosity of the emulsion phase rises to 0.5. Bubble-emulsion structure becomes less distinct. In both simulations and experiments it is observed that the clear distinction between bubbles and the emulsion phase gradually disappears with increasing pressure. At atmospheric pressure the emulsion phase is dense and the bubbles are clear voids containing little particles. At high pressure it is no longer possible to observe separate bubbles, although dense and porous regions in the bed still prevail, intermediate porosities occur just as frequent. Fluidization is more vigorous and bubbles behave more chaotic. From animations of simulations results (pressure drop fluctuations and bubble properties) it was observed that the fluidization is more vigorous at elevated pressure. Bubbles move chaotically through the bed and bubbles coalescence and break-up takes place frequently, although it is hard to distinct individual bubbles. (Micro) mixing is improved via increased granular temperature only caused by increased porosity. From DPM and TFM simulations it is observed that solids mixing is improved with increasing operating pressure. Based on DPM simulation results is found that this effect is caused by increased granular temperature. Granular temperature is not directly increased by the elevated operated pressure, but rather via the increased porosity of the emulsion phase, which creates more space for the neighbouring particles to attain different velocities. Bed expansion limits macro mixing. Micro mixing is mixing at the scale of individual bubbles, while macro mixing is at the scale of the entire bed. The micro mixing rate is increased with pressure because of the increased granular temperature. For pressures below 8 bar, macro mixing is enhanced with increasing operating pressure. At higher pressures, the bed expands, which decreases the mixing rate, since particles have to travel larger distances before they can become fully mixed

The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications

The XDEM multi-physics and multi-scale simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and multi-scale simulation tools. For this purpose the simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology

Non-invasive and non-intrusive diagnostic techniques for gas-solid fluidized beds – A review

Gas-solid fluidized-bed systems offer great advantages in terms of chemical reaction efficiency and temperature control where other chemical reactor designs fall short. For this reason, they have been widely employed in a range of industrial application where these properties are essential. Nonetheless, the knowledge of such systems and the corresponding design choices, in most cases, rely on a heuristic expertise gained over the years rather than on a deep physical understanding of the phenomena taking place in fluidized beds. This is a huge limiting factor when it comes to the design, the scale-up and the optimization of such complex units. Fortunately, a wide array of diagnostic techniques has enabled researchers to strive in this direction, and, among these, non-invasive and non-intrusive diagnostic techniques stand out thanks to their innate feature of not affecting the flow field, while also avoiding direct contact with the medium under study. This work offers an overview of the non-invasive and non-intrusive diagnostic techniques most commonly applied to fluidized-bed systems, highlighting their capabilities in terms of the quantities they can measure, as well as advantages and limitations of each of them. The latest developments and the likely future trends are also presented. Neither of these methodologies represents a best option on all fronts. The goal of this work is rather to highlight what each technique has to offer and what application are they better suited for

Cluster Fluid Dynamics in Down Flow Reactors: Experimental and Modeling Study

Gas–solid concurrent downers possess unique features when compared to other gas–solid systems. Establishing their fluid dynamic properties requires both experimental measurements of gas-solid flow properties and computational modeling. Measuring gas-solid flow properties such as cluster solid concentrations, individual cluster slip velocities, and cluster sizes, involves the use of specialized optical equipment, as well as a rigorous data analysis methodology. In addition, the modeling of the fluid dynamics of gas-solid flows in downer units offers special challenges such as establishing a proper drag model, cluster configuration and sizes, sphericity, boundary conditions, among other issues. In this PhD dissertation, the fluid dynamics of gas-solid flows in downer reactor units are analyzed in the context of a wide range of operating conditions. To accomplish this, local cluster particle characteristics are determined for the first time, using two separate downer units and a significantly enhanced data analysis. This involves individual cluster signals recorded by the CREC-GS-Optiprobes and a method for setting the data baseline using solid mass balances. The proposed methodology allows the calculation of individual cluster slip velocities, agglomerate particle sizes, individual particle cluster size distributions, and cluster drag coefficients. Gas-solid flows in downers are simulated in the present PhD dissertation, using a Computational Particle Fluid Dynamics (CPFD) Numerical Scheme. The CPFD model includes particles represented as clusters. This model is validated with experimental data obtained from the two independent downer units which have different downer-column internal diameters (a 1 inch ID and a 2 inch ID). CPFD simulations are implemented using average particle cluster sizes as obtained experimentally. Experimentally observed time-averaged axial and radial velocities, solid concentration profiles, and cluster particle acceleration regions are successfully simulated by a CFPD model. These findings support: a) a narrow distribution of particle cluster catalyst residences, b) the characteristic particle “forward” mixing, and c) the relatively flat radial solid concentrations and solid cluster velocities. It is found that CPFD simulations agree well with experimentally determined particle cluster velocity and the solid void fraction in the downer core region, with this being the case for all the operating conditions studied

Numerical Simulation of Catalytic Ozone Decomposition Reaction in a Gas-solids Circulating Fluidized Bed Riser

Computational fluid dynamics (CFD) modeling of catalytic ozone decomposition reaction in a circulating fluidized bed (CFB) riser using iron impregnated FCC particles as catalyst is carried out. The catalytic reaction is defined as a one-step reaction with an empirical coefficient. Eularian-Eularian method with kinetic theory of granular flow is used to solve the gas-solids two-phase flow in the CFB riser. The simulation results are compared with experimental data, with the reaction rate modified using an empirical coefficient to provide better simulation results than the original reaction rate. Moreover, the particle size has great effects on the reaction rate. Studies on solid particle distribution show that the influence of wall boundary condition, determined by specularity coefficient and particle-wall restitution coefficient, plays a major role in the solids lateral velocity that affects the solids distribution in the riser. The generality of the CFD model is further validated under different operating conditions of the riser

CFD modeling of biomass combustion and gasification in fluidized bed reactors

Biomass is an environmentally friendly renewable energy source and carbon-neutral fuel alternative. Direct combustion/gasification of biomass in the dense particle-fluid system is an important pathway to biomass energy utilization. To efficiently utilize biomass for energy conversion, a full understanding of biomass thermal conversion in lab/industrial-scale equipment is essential. This thesis aims to gain a deeper understanding of the physical and chemical mechanisms of biomass combustion/gasification in fluidized bed (FB) furnaces using computational fluid dynamics (CFD) simulations. A three-dimensional reactive CFD model based on the Eulerian-Lagrangian method is developed to investigate the hydrodynamics, heat transfer, and gasification/combustion characteristics of biomass in multiple-scale FB furnaces. The CFD model considered here is based on the multi-phase particle-in-cell (MP-PIC) collision model and the coarse grain method (CGM). CGM is computationally efficient; however, it can cause numerical instability if the clustered parcels pass through small computational cells, resulting in the over-loading of solid particles in the cells. To address this issue, a distribution kernel method (DKM) is proposed. This method is to spread the solid volume and source terms of the parcel to the surrounding domain. The numerical stiffness problem caused by the CGM clustering can be remedied using DKM. Validation of the model is performed using experimental data from various lab-scale reactors. The validated model is employed to investigate further the heat transfer and biomass combustion/gasification process. Biomass pyrolysis produces a large variety of species in the products, which poses great challenges to the modeling of biomass gasification. A conventional single-step pyrolysis model is widely employed in FB simulations due to its low computational cost. However, the prediction of pyrolysis products of this model under varying operating temperatures needs to be improved. To address this issue, an empirical pyrolysis model based on element conservation law is developed. The empirical parameters are based on a number of experiments from the literature. The simulation results agree well with the experimental data under differentoperating conditions. The pyrolysis model improves the sensitivity of gasification product yields to operating temperature. Furthermore, the mixture characteristics of the biomass and sand particles and the effect of the operating conditions on the yields of gasification products are analyzed. The validated CFD model is employed to investigate the fluidization, combustion, and emission processes in industrial-scale FB furnaces. A major challenge in the CFD simulation of industrial-scale FB furnaces is the enormous computational time and memory required to track quadrillions of particles in the systems. The CFD model coupling MP-PIC and CGM greatly reduces the computational cost, and the DKM overcomes the unavoidable particle overloading issue due to the refined mesh in complex geometry. The CFD predictions agree well with onsite temperature experiments in the furnace. The CFD results are used to understand the granular flow and the impact of operating conditions on the physical and chemical processes in biomass FB-fired furnaces

Computational Fluid Dynamics of Catalytic Reactors

Today, the challenge in chemical and material synthesis is not only the development of new catalysts and supports to synthesize a desired product, but also the understanding of the interaction of the catalyst with the surrounding flow field. Computational Fluid Dynamics or CFD is the analysis of fluid flow, heat and mass transfer and chemical reactions by means of computer-based numerical simulations. CFD has matured into a powerful tool with a wide range of applications in industry and academia. From a reaction engineering perspective, main advantages are reduction of time and costs for reactor design and optimization, and the ability to study systems where experiments can hardly be performed, e.g., hazardous conditions or beyond normal operation limits. However, the simulation results will always remain a reflection of the uncertainty in the underlying models and physicochemical parameters so that in general a careful experimental validation is required. This chapter introduces the application of CFD simulations in heterogeneous catalysis. Catalytic reactors can be classified by the geometrical design of the catalyst material (e.g. monoliths, particles, pellets, washcoats). Approaches for modeling and numerical simulation of the various catalyst types are presented. Focus is put on the principal concepts for coupling the physical and chemical processes on different levels of details, and on illustrative applications. Models for surface reaction kinetics and turbulence are described and an overview on available numerical methods and computational tools is provided

Project Report On Modeling and simulation of gas-liquid interfacial area in three phase fluidized and semi-fluidized bed

Three phase systems are vital part of chemical industry,as reactions involving gas, liquid solid are often encountered in chemical process industry [Yatish Shah 2000]. The most common occurrence of this type of three phase systems is in hydro processing industry in which variety of reactions between hydrogen and oil phase and solid catalyst have been found. The other common three phase reactions are catalytic oxidation and hydration reactions These and other numerous similar gal-liquid–solid reactions are carried out in reactors. The three-phase system is subcategorized as • Reactions where the gas, liquid and solid are either reactants or products • Gas-Liquid reactions with solid as a catalyst. • Two reaction phases and third as inert phase. • All three phases are inert as found in unit operations. Examples of first two types can be found very often in chemical process industry, as each phase is essential in the reaction mechanism. In the third type, one inert phase in especially added to get the advantage of three-phase system. The third inert phase induces better momentum exchange between the phases, helps in better distribution of reactant species and good temperature control. The filtration operation can be example of fourth type of three phase syste