Computational Fluid Dynamics of Reacting Flows at Surfaces: Methodologies and Applications

This review presents the numerical algorithms and speed-up strategies developed to couple continuum macroscopic simulations and detailed microkinetic models in the context of multiscale approaches to chemical reactions engineering. CFD simulations and hierarchical approaches are discussed both for fixed and fluidized systems. The foundations of the methodologies are reviewed together with specific examples to show the applicability of the methods. These concepts play a pivotal role to enable the first-principles multiscale approach to systems of technological relevance

Training set design for machine learning techniques applied to the approximation of computationally intensive first-principles kinetic models

We propose a design procedure for the generation of the training set for Machine Learning algorithms with a specific focus on the approximation of computationally-intensive first-principles kinetic models in catalysis. The procedure is based on the function topology and behavior, by means of the calculation of the discrete gradient, and on the relative importance of the independent variables. We apply the proposed methodology to the tabulation and regression of mean-field and kinetic Monte Carlo models aiming at their coupling with reactor simulations. Our tests – in the context both of mean-field kinetics and kinetic Monte Carlo simulations – show that the procedure is able to design a dataset that requires between 60 and 80% fewer data points to achieve the same approximation accuracy than the one obtained with an evenly distributed grid. This strong reduction in the number of points results in a significant computational gain and a concomitant boost of the approximation efficiency. The Machine Learning algorithms trained with the results of the procedure are then included in both macroscopic reactor models and computational fluid dynamics (CFD) simulations. First, a Plug Flow Reactor is employed to carry out a direct comparison with the solution of the full first-principles kinetic model. The results show an excellent agreement within 0.2% between the models. Then, the CFD simulation of complex tridimensional geometry is carried out by using a tabulated kMC model for CO oxidation on Ruthenium oxide, thus providing a showcase of the capability of the approach in making possible the multiscale simulation of complex chemical reactors

CFD modeling of multiphase flows with detailed microkinetic description of the surface reactivity

The analysis of catalytic processes and the development of innovative technologies require a deep comprehension of the complex interplay between the intrinsic functionality of the heterogeneous material and the surrounding environment in the reactor. This is particularly important for multiphase catalytic reactors where complex interactions among the phases distribution, the inter- and intra-phase transport and the catalytic material occur. In this work, a computational framework has been developed to couple the solution of the hydrodynamics of multiphase flow using Computational Fluid Dynamics (CFD) with the detailed description of the surface reactivity through first-principles microkinetic models. In particular, the methodology employs an algebraic Volume-Of-Fluid (VOF) approach for the advection of the phases and takes advantage of the Compressive-Continuous Species Transfer (CST) for the modeling of the species mass interfacial transfer. The heterogeneous chemistry is included as source terms to the mass and energy equations acting at the catalytic surface, while the solution of the mass balance equation employs an operator splitting approach. The numerical framework has been assessed with respect to simple geometries by direct comparison with analytical and fully coupled solutions followed by examples of application in the context of the nitrobenzene hydrogenation to aniline. The envisioned approach is the first step toward the first-principles-based multiscale analysis of multiphase catalytic processes paving the way toward the detailed understanding and development of innovative and intensified technologies

Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing

The intensification of catalytic reactors is expected to play a crucial role to address the challenges that the chemical industry is facing in the transition to more sustainable productions. An advanced design paradigm is necessary to develop customized and process-tailored reactor solutions able to provide the optimal operating conditions, transport properties and geometry. This can be achieved by a detailed understanding of the catalyst functionality in the reactive environment. Multiscale Modeling provides such in-depth insights into the complex physical-chemical phenomena enabling to achieve a first-principles-based understanding and design of the most suitable reactor geometry and configuration. To overcome the intrinsic complexity of the approach, Machine Learning can be synergically employed to reduce the computational cost fostering the inclusion of detailed numerical simulations since the early stage of the design process. Moreover, hybrid machine learning models trained with the data and enforced by the physics are envisioned to assist the work of designers facilitating the development of disruptive intensified solutions. The manufacturing of these unconventional systems requires adequate techniques. Additive Manufacturing is showing enormous potential in this direction and their future developments are expected to make it possible to routinely fabricate intensified reactors

Analysis of the effective thermal conductivity of isotropic and anisotropic Periodic Open Cellular Structures for the intensification of catalytic processes

Conductive metallic Periodic Open Cellular Structures (POCS) are considered a promising solution for the intensification of heat-transfer limited catalytic processes thanks to their enhanced thermal conductivity. Herein, the heat conduction in the solid matrix has been investigated through 3D numerical simulations. The porosity together with the intrinsic conductivity of the material have a major effect on the effective thermal conductivity, while a negligible influence of the cell shape and size is found. A correlation previously derived for the description of open cell foams shows an excellent agreement with the results of POCS structures. POCS are produced by additive manufacturing, e.g. 3D printing, providing degrees of freedom in the geometry design. Anisotropic cubic cell structures have been investigated for the first time to explore the possibility to promote or decrease preferentially the heat conduction in the radial or the axial direction. At constant solid fraction and cell size, these structures can improve the effective thermal conductivity of the solid matrix up to 40 % and 100 % for structures thickened in two or one direction respectively. This concept paves the way to the design of metamaterials with tailored properties, granting additional degrees of freedom for the intensification of heat-transfer limited catalytic processes

A Fundamental Investigation of Gas/Solid Heat and Mass Transfer in Structured Catalysts Based on Periodic Open Cellular Structures (POCS)

In this work, we investigate the gas-solid heat and mass transfer in catalytically activated periodic open cellular structures, which are considered a promising solution for intensification of catalytic processes limited by external transport, aiming at the derivation of suitable correlations. Computational fluid dynamics is employed to investigate the Tetrakaidekahedral and Diamond lattice structures. The influence of the morphological features and flow conditions on the external transport properties is assessed. The strut diameter is an adequate characteristic length for the formulation of heat and mass transfer correlations; accordingly, a power-law dependence of the Sherwood number to the Reynolds number between 0.33 and 0.67 was found according to the flow regimes in the range 1-128 of the Reynolds number. An additional -1.5-order dependence on the porosity is found. The formulated correlations are in good agreement with the simulation results and allow for the accurate evaluation of the external transfer coefficients for POCS

Rich H2 catalytic oxidation as a novel methodology for the evaluation of mass transport properties of 3D printed catalyst supports

In this work, we propose a novel methodology for the evaluation of the external mass transfer properties of 3D printed catalyst supports. This protocol relies on the use of a lab scale SLA 3D printer with a resin characterized by a high heat deflection temperature (HDT) for the manufacturing of samples at a lower price and with higher accuracy than equivalent metallic 3D printed structures. Periodic open cellular structure (POCS) samples with Tetrakaidecahedron unit cells (TKKD) were 3D printed and catalytically activated by depositing a 3% Pd/CeO2 washcoat by spin-coating. The washcoat was then consolidated with a two-step heat treatment composed by in-situ calcination in N2 and reduction in N2/H2 stream. Catalytic tests of rich H2 combustion showed the possibility to reach the external mass transfer control at temperatures below the resin HDT. Sherwood numbers were eventually estimated from the oxygen conversions under full external mass transfer control assuming a PFR behaviour. To validate the methodology, 3D printed replicas of open cell foams were also tested, and the results were successfully compared against a well-established literature correlation. Moreover, a one-to-one comparison was performed between the Sherwood numbers of a resin 3D printed structure, tested with the proposed methodology, and a metallic 3D printed structure, tested with the conventional CO oxidation approach. The two methods lead to superimposed results, thus providing the experimental evidence of the equivalence of the two methodologies for the evaluation of the external mass transport properties of complex catalyst substrates

Packed foams for the intensification of catalytic processes: assessment of packing efficiency and pressure drop using a combined experimental and numerical approach

Thermally conductive packed foams have been proposed as an effective solution for the intensification of non-adiabatic catalytic processes in tubular reactors where high heat transfer rates and large catalyst inventories are necessary. Some of the open issues of this innovative solution for its scale-up to industrial process are the packing efficiency and the pressure drop. In this work, these aspects were addressed by performing both experimental activities on 3D printed foams and simulations on packed foam structures. The packing efficiency was studied by considering different spherical pellets and foam samples. The ratio between the foam window and the pellet diameter, R, was identified as the governing parameter: only pellets smaller than the window size can be packed inside the cavities of open cell foams. The packing efficiency increases with R, reaching the same asymptotic value of random packing in a tube at R > 5; for R less than 1.3 the porosity exceeds 50% and local channeling may be present. Due to commercial foam specifications, this limits the application of packed foams to processes where pellets smaller than 2 mm are employed. Pressure drop in packed foams was studied as well both by experimental tests and by numerical simulations. Despite the presence of the foam structure, pressure drops in packed foams are comparable or lower than the pressure drops in packed beds with the same pellet diameter due to the increase of the porosity inside the system. An Ergun like correlation corrected by the overall void fraction and the total wetted surface is able to describe the pressure drop in these systems with reasonable accuracy

Impact of the partitioning method on multidimensional adaptive-chemistry simulations

The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms' level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In thiswork, two different clustering algorithms for the partitioning of the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteady laminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach based on the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is the Local Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction (i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtained for both the tested methods, but LPCA showed superior performances in terms of reduced mechanisms uniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lower sensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also being optimal, with respect to SKM, for massive dataset clustering tasks