Abstract: Publication date: 2018Source: Advances in Chemical Engineering, Volume 53Author(s): Stefan Radl, Federico Municchi The method of spatial filtering as a universal approach to support closure development for models of dense fluid–particle systems is presented. Starting from a set of governing equations, an overview of closures that are required at different levels of a model hierarchy is provided. A focus is on closures to be used in mesoscale models, highlighting recent developments that aim at the description of the variability of exchange coefficients, as well as wall effects. Furthermore, attempts to describe the anisotropy of mesoscale stresses, as well as the effect of cohesive particle–particle interactions in the context of macroscale models are summarized. Computational aspects, together with selected verification cases are documented to stimulate a broader use of the filtering approach. Finally, three future research directions that appear most fruitful are outlined. These final thoughts may help to establish more reliable and robust multiphase flow models for reactive systems.

Abstract: Publication date: 2018Source: Advances in Chemical Engineering, Volume 53Author(s): Liqiang Lu, Sofiane Benyahia This chapter provides the full description of a coarse-grained discrete particle method based on a novel hard-sphere contact model for the simulation of industrial-scale fluidized bed reactors. This method is based on simple models that are easy to understand and implement in numerical codes. This technique is verified and validated for several small-scale fluidized systems where numerical data based on finer methods as well as experimental data are available. The speed of execution of this method is increased several orders of magnitudes compared to particle-based discrete methods, which allows for thousands of seconds of flow, heat, and mass transfer simulations of industrial reactors such as fluidized catalytic cracking regenerator, Methanol to Olefins reactor, and Rare Earth Elements leaching reactor, achieved in just few days using commonly available computer resources. It is now possible for the common engineer to conduct simulations of large-scale fluid–particle reactors to understand, design, and troubleshoot, as well as optimize the performance of these complex multiphase flow systems.

Abstract: Publication date: 2018Source: Advances in Chemical Engineering, Volume 53Author(s): Maike W. Baltussen, Kay A. Buist, Elias A.J.F. Peters, Johannes A.M. Kuipers In large-scale industrial processes involving granulation, coating, and production of base chemicals and polymers dense particulate flows with coupled mass, momentum, and heat transfer are frequently encountered. Both (effective) fluid–particle and (dissipative) particle–particle interactions need to be accounted for because the mutual competition between these phenomena govern the key features of dense gas–particle flows such as regime transitions. These interactions prevail at different length scales and consequently a multiscale approach is adopted to arrive at a quantitative description of these complex flows. In this approach detailed models, taking into account the relevant details of fluid–particle interaction (DNS) and particle–particle interaction (DEM) are used to obtain closure laws to feed two-fluid models (TFMs) which can be used to simulate the flow on a much larger (industrial) scale. In this chapter, we will discuss recent advances in the multiscale simulation of dense gas-fluidized beds. The governing equations will be presented as well as the key features of the numerical solution method. For each model type, illustrative computational results will be presented. Finally, areas which need substantial further attention will be discussed.

Abstract: Publication date: 2018Source: Advances in Chemical Engineering, Volume 52Author(s): Salvatore Falzone, Antonio Buffo, Marco Vanni, Daniele Luca Marchisio The accurate prediction of the size distribution in liquid–liquid or gas–liquid turbulent dispersions is of fundamental relevance in many industrial applications. The distribution can be predicted with computational fluid dynamics coupled with population balance models (PBM). PBM needs, in turn, suitable models to account for droplet and bubble coalescence and breakage. In this work a critical analysis of the most commonly employed breakage and coalescence kernels is performed. In particular, the presence of contaminants on breakage and coalescence rates is analyzed and the appropriate modifications on the kernels are discussed. Furthermore, the most important models for the daughter size distribution are reviewed. Eventually, a comparison among the most employed breakage and coalescence model was carried out.

Abstract: Publication date: 2018Source: Advances in Chemical Engineering, Volume 52Author(s): Ziad Hamidouche, Enrica Masi, Pascal Fede, Renaud Ansart, Hervé Neau, Mehrdji Hemati, Olivier Simonin This study deals with mathematical modeling and numerical simulations of reactive multiphase flows in dense fluidized beds. These flows involve complex physical mechanisms related to the coupling between the bed hydrodynamic and the reactions, which are still poorly understood. In this context, numerical simulations can provide explanatory access to the underlying physics taking place in the reactor, thus supplementing the experimental results. The present contribution focuses on the natural gas combustion in a dense fluidized bed reactor, for which experimental results are available from the literature (Dounit, 2001; Dounit et al., 2001, Dounit et al., 2008). In their experiments, the authors pointed out the essential role played by the particle projection zone, above the bed surface, in the global thermal efficiency of the reactor operating at relatively low temperatures (600°C–800°C). In the present study, this point is further investigated by analyzing the results obtained by the numerical simulations. The unsteady 3D numerical simulations were performed using NEPTUNE_CFD code which is based on an Euler–Euler approach; the latter computes both the gas and the particulate phases in an Eulerian framework, accounting for specific closures modeling the interphase momentum and energy transfers. Time-averaged quantities were then computed and compared with the available experimental measurements. Numerical results (especially the gas temperature) were found to be very sensitive to the mesh refinement for the selected operating point. A further analysis at mesoscopic and macroscopic scales was carried out. This analysis pointed out the crucial role of the toroidal loop, which extends from the bed, close to the ejection zone, to the freeboard, on the fuel conversion. In numerical simulations, this loop must be accurately reproduced in order to provide reliable combustion predictions.

Abstract: Publication date: 2018Source: Advances in Chemical Engineering, Volume 52Author(s): Rodney O. Fox The numerical simulation of multiphase chemically reacting flows is very challenging due to their multiscale nature. In this chapter, the focus is on disperse multiphase flows with a continuous phase (gas or liquid) surrounding one or more disperse phases (e.g., particle, drops, or bubbles). In such flows, the disperse phase is usually characterized by a size, concentration, and/or temperature distribution that must be accounted for in the mathematical model. In this context, a population balance model (PBM) for the number density function is the most convenient way to account for polydispersity. In addition, the continuous phase is often turbulent, so mixing and chemical reactions in a turbulent flow must also be modeled. This can be done using probability density function (PDF) methods. In this chapter, computational methods for approximating solutions to PBM and PDF transport equations are reviewed in the context of computational fluid dynamic (CFD). In particular, quadrature-based moment methods (QBMM) have proven to be accurate and numerically efficient when combined with CFD simulations. Applications with increasing degrees of complexity in the flow physics are introduced to illustrate how QBMM are used to solve real problems in chemical engineering.