Towards realistic large-scale simulations of fixed bed chemical reactors: Bridging the gap between discrete element and porous media computational fluid dynamics models

Meeting our climate goals and achieving sustainable development requires the bulk production of renewable and carbon-neutral hydrocarbon fuels and chemicals. Industrial-scale heterogeneous fixed bed chemical reactors will play a key role in this. Their overall performance optimisation, however, requires deep knowledge and understanding of all length-scales and physicochemical phenomena involved in their operation. Such knowledge can be acquired through multidisciplinary studies, where experimental investigations are combined with validated computational tools. For the latter, Computational Fluid Dynamics (CFD) models hold great potential. Their unique ability to couple the internal bed structure with flow-related parameters can prove invaluable to advance the technology readiness level (TRL) and aid in the further optimisation of industrial-scale fixed bed reactors.Experimental investigations of methanol synthesis from mixtures of CO2/CO/H2, using a Cu/ZnO/Al2O3 catalyst, have been used throughout the literature to understand and describe the kinetic mechanisms of the reaction. From such studies, several kinetic models have been produced, each considering unique interactions between the species and the catalyst. The accuracy, adaptability, and potential of CFD models was explored here by investigating and validating the predictions made by two kinetic models against experimental data, yielding excellent accuracy. These two models considered distinct mechanisms and roles for CO, with only one of them considering a direct pathway between CO and methanol. By comparing the flow profiles predicted by them, the key differences in the predictions made by the two kinetic models were highlighted. Specifically, the inclusion or exclusion of a direct CO hydrogenation pathway in the kinetic model significantly altered the behaviour of the involved species, with one model even predicting the loss of methanol for CO production. These observations can act as guidelines for further experimental investigations to prove or disprove the observed behaviours.Throughout this investigation the utilised CFD model treated the bed as a porous continuum, where the bed structure was approached through effective properties and momentum sinks. Treating the catalytic bed either as a porous continuum or as a structure formed by homogeneous spherical particles, however, is an oversimplification. Realistic, lab-scale packed beds are formed by sieving, a process which offers limited control over the size and shape of the catalytic particles. To quantify this, the internal structure of six catalytic beds was reproduced through micro-Computed Tomography scans, and the size and form of each individual particle was quantified. It was observed for the first time that repeated passes through sieves of pre-determined sizes offered a higher level of control over the particle size range within the bed, promoting homogeneity, while highly irregular particles were filtered out. Moreover, repeated passes were highly efficient in greatly reducing the population of dust particles, &lt;100 µm in size, which was prominent within the beds formed after a single sieve pass. The bulk, radial, and axial porosities of the catalytic beds were also analysed. The range of particle sizes, shapes, and orientations existing within the bed create highly heterogeneous and random structures. Their radial porosity profiles, when compared with those predicted for homogeneous spherical beds, appear smooth, as any heterogeneities are averaged out. Reproducing the radial porosity profiles seen in the actual beds would require correlations that take into account both size and shape heterogeneities. Using ethanol dehydration as a case study, it was also observed that multiple sieving passes accelerated the reaction rate and the product formation. Rationalising this behaviour simply through the outlet product formation, however, is not possible. A higher level of investigation is required, which would combine the actual bed structure with CFD simulations.Meshing the scanned bed structure and using it as a computational geometry for CFD models to simulate the flow field through it would yield unique observations. Due to the bed complexity, however, the computational resources required for this task would quickly become prohibitive. As a result, novel computational approaches are needed, able to reproduce particle arrangements and their contribution to the flow while keeping the computational demands to feasible levels. Such a novel approach is presented here, referred to as the Semi-Realistic (SR) model. In the SR approach, the effective properties considered by porous continuum models were spatially localised. This allowed the creation of distinct interparticle and intraparticle zones with unique physicochemical mechanisms for each. With this method, the SR model was able to reproduce particle structures within the bed and replicate their impact in the flow profiles as accurately as particle-resolved CFD models. Combined with its significantly reduced computational demands, the SR model is a unique, highly flexible, and adaptable tool, which can be tailored to either the study of heterogeneous particle beds or of industrial-scale fixed bed reactors.As list of novelties in this thesis, the following are highlighted:1) The application of CFD models as detailed investigative tools to compare and to evaluate the predictions of kinetic models. With CFD models revealing the species behaviour and interactions within the catalytic bed, the feasibility and accuracy of the considered mechanisms can be quantified. This approach then guides experimental setups towards a deeper understanding of reaction mechanisms, promoting both novel catalyst and reactor engineering.2) A unique insight into the morphology of realistic catalytic beds, formed by particles produced by sieving. Specifically, the Computed Tomography scans identified in-situ catalytic bed parameters, under real-world experimental setups. The produced scans provided for the first time insights into how sieving affected the shapes and sizes of the produced catalytic particles, thus identifying their highly heterogeneous nature. Through this analysis, the accuracy of semi-empirical correlations to describe the interparticle porosity of polydispersed beds was quantified. Furthermore, the scanned geometry can be meshed and used for CFD simulations, thus offering a direct coupling between experiments and simulations, and promoting the accuracy of CFD models.3) A novel CFD method to simulate the catalytic particles within fixed bed chemical reactors without explicitly resolving them. This new method significantly reduced the computational demands required by particle-resolved CFD models, while also being highly flexible in its approach of catalytic particles. Specifically, the particles were approached through two distinct porosity terms, a macro-porosity term responsible for the hydrodynamic profile of the particle, and a micro-porosity term responsible for its physicochemical (i.e., diffusion and reaction) phenomena. Consequently, this enables CFD models to more accurately describe the intraparticle structure of particles with multi-pore-scale porosities. By showcasing the impact of dual-scale porosity particles in the full bed scale, the need for a deeper understanding and experimental characterisation of the intraparticle structure of porous catalytic particles was highlighted.<br/

Data for Doctoral thesis&#39;Towards realistic large-scale simulations of fixed bed chemical reactors: Bridging the gap between discrete element and porous media computational fluid dynamics models&#39;

Data submitted for doctoral thesis 1) filename: Chapter_3_Fig_Data -&gt; Excel file, with the data for Figure A.10 presented in Appendix C 2) Chapter_4_Fig_Data -&gt; Excel file, data for all figures in Chapter 4 of the thesis. 3) Chapter_5_Fig_Data -&gt; Excel file, data for all figures in Chapter 5 of the thesis. 4) 5S_beds_analysis_Avizo_method -&gt; Excel file, containing all data of the 5S 100-300, 300-500, and 500-700 particles. 5) Bed_Characteristics_Avizo_method_v4 -&gt; Excel file, containing all data of the 1S 100-300, 300-500, and 500-700 particles. 6) Chapter_6_Fig_Data -&gt; Excel file, kinetic data derivation for the Ethanol dehydration reaction used in Chapter 6, shown in Appendix B7. 7) Profiles_Comparison_DEM_SR -&gt; Excel file, containing all data for the radial profiles of all parameters of Chapter 6, for all three DEM, PM, and SR models. 8) Parallel_Profiles_Comparison_DEM_SR -&gt; Excel file, containing all data for the axial profiles of all parameters of Chapter 6, for all three DEM, PM, and SR models. 9) Parametric_Studies -&gt; Excel file, containing all data for the parametric studies related to Chapter 6, i.e., mesh independency study (Sheet &quot;Mesh&quot;), intraparticle porosity (Sheet &quot;Porosity&quot;), temperature (Sheet &quot;Temperature&quot; and &quot;Bed-Temperature&quot; for the 1- and 27-particle cases, respectively), WHSV (Sheet &quot;WHSV&quot; and &quot;Bed - WHSV&quot; for the 1- and 27-particle cases, respectively), and exothermicity (Sheet &quot;Exothermicity&quot;). 10) Comp_time_Complete -&gt; Excel file, data for the computational resources of all cases of Chapter 6, i.e., Table 6.2, Figure 6.30, and section F.9 11) Final_cases_DEM_vs_SR -&gt; PowerPoint presentation containing all contour plots of figure 6 Associated publications: The data of Chapter 4 has been published in the paper: DOI: 10.1039/D0FD00136H The data of Chapter 5 has been published in the paper: DOI: 10.1016/j.apt.2022.103932 Contributors for data collection: 1) Matthew E. Potter, Orchid ID: 0000-0001-9849-3306, for help with the 5S catalytic beds of Chapter 5 2) Katy Rankin, Orchid ID: 0000-0002-8458-1038, for doing the Computed Tomography (CT) scans of the catalytic beds of Chapter 5. </span

Hydrodynamic profiles of computed tomography-scanned polydispersed beds produced by sieving

Computational Fluid Dynamics (CFD) models are a valuable tool for design, optimization, and scaling-up of fixed bed chemical reactors. However, the realistic representation of the catalytic bed structure and the mesh quality of the 3D geometry is of paramount importance to improve the accuracy of CFD models. For the former, computed tomography (CT) is a non-destructive method to map and generate the internal structure of actual fixed bed reactors, formed by catalytic particles produced by sieving, thus directly coupling experiments with CFD models. Due to the local topological complexity of these beds, however, meshing their entire volume would lead to exhaustive computational demands. To reduce these, a suitable sample section should be selected, which respects the bulk and radial porosity of the full bed as accurately as possible. Three distinct sample sections were quantified here for their accuracy, identifying that, due to the highly heterogeneous nature of the full beds, sample selection is case sensitive. A selected section was then meshed, and its hydrodynamic profile resolved, to evaluate its mesh independency. The results highlight the importance of choosing a suitable bed section and mesh size to reduce the computational demands, minimise the computational errors, and achieve the desired level of solution detail

Image processing of computed tomography scanned poly-dispersed beds for computational fluid dynamic studies

Achieving our emission reduction goals requires the bulk production of carbon-neutral fuels and chemicals, which are catalytically produced through heterogeneous fixed bed chemical reactors. To optimise and scale-up these reactors, accurate and validated Computational Fluid Dynamics (CFD) models are crucial. Of especial importance to CFD simulations is the accurate depiction of the 3D bed structure used during the experimental setup. A direct one-to-one coupling between experiments and simulations can be achieved by scanning the experimental bed using computed tomography and reconstructing the scanned images as a 3D geometry for CFD simulations. However, processing of the scanned images is necessary to minimise highly coarse features that could impact the overall mesh size. A highly poly-dispersed lab-scale fixed bed reactor, previously scanned and analysed, is processed using various image-processing operations. Depending on the number and the crudeness of the processing operations, the bed is progressively deformed, which impacts both its porosity and its interparticle pore connectivity. The impact of image-processing becomes more evident when the hydrodynamic behaviour, i.e., X-, Y-, and Z-velocity and static pressure, of the beds is explored. CFD simulations revealed highly heterogeneous flow profiles, with the maximum velocity reached being 16-times higher than the average superficial velocity within the bed. Moreover, small modifications in local topological features introduce significant changes to the flow profiles, while the 3D pore interconnectivity was seen to play an equally important role as the interparticle porosity. A particle size study revealed that large particles form less interconnected networks with higher pore volumes, which significantly reduce the flow velocity and the pressure drop experienced by the flow. The generated results yield key insights towards a deeper understanding of the behaviour of fixed bed chemical reactors, highly valuable for catalyst and reactor engineering.</p

Impact of particle size on the selection of a representative bed section for poly-dispersed fixed bed reactors

Computational Fluid Dynamics (CFD) models are a valuable tool for the design, optimization, and scaling-up of fixed bed chemical reactors. However, the realistic representation of the catalytic bed structure and the mesh quality of the 3D geometry is of paramount importance to improve the accuracy of CFD models. For the former, computed tomography (CT) is a non-destructive method to map and generate the internal structure of experimental fixed bed reactors, enabling a direct 1-to-1 coupling between experiments and simulations. In our previous work, the internal structure of highly poly-dispersed fixed bed reactors, formed by sieved particles, was analysed. The particles that formed them displayed a wide range of sizes, shapes, and orientations. Due to the local topological complexity of these beds, meshing and simulating their entire volume would lead to exhaustive computational demands. To reduce these, a suitable sample section should be selected, which accurately represents both the bulk and the radial porosity of the full bed. Three distinct sample sections were quantified here for their accuracy, identifying that, due to the highly heterogeneous nature of the full beds, sample selection is case sensitive. In addition, compared to smaller particles, larger particles form more heterogeneous local structures, thus requiring longer sections to accurately represent the full bed. A selected 10% section was then meshed, and its hydrodynamic profile resolved, to evaluate its mesh independency. The results highlight the importance of choosing a suitable bed section and mesh size to reduce the computational demands, minimise the computational errors, and achieve the desired level of solution detail

Enhancing the methanol yield of industrial-scale fixed bed reactors using computational fluid dynamics models

Achieving our decarbonisation goals for the maritime transport sector requires the bulk production of liquid hydrocarbon fuels, suitable for the existing infrastructure, such as methanol (MeOH). For this, optimisation of fixed bed chemical reactors is vital. Unlike demonstration-scale setups, optimising the design of such reactors using Computational Fluid Dynamics (CFD) models is both cheaper and faster. In this study, using a 2D pseudo-homogeneous CFD model, a single tube from a multi-tubular industrial-scale MeOH reactor is simulated. Using available experimental data, the CFD model was validated yielding excellent accuracy, with an average error of 2.6 %. The model identified a temperature hot-spot near the bed entrance, while the predicted pressure drop was 11 % of the operating pressure. Both effects could considerably reduce the efficiency of the reactor, either by catalyst deactivation or by increasing the compressor requirements, respectively. Through a parametric study where the tube’s length and diameter were varied within the ±50 % range, 53 total cases are produced and solved, creating an extensive pareto set of data for the key output parameters, such as methanol production, pressure, and temperature. A response surface was also generated, revealing the interconnection between the tube’s design and the operating parameters. This enabled a performance optimisation of the reactor design for two industrially relevant scenarios, with the optimised reactor achieving a 6.9 % higher MeOH yield and a 75 % reduced pressure drop. Through industrial involvement, the reactor can be further optimised, and would act as a critical foundation for more extensive techno-economic and socio-economic evaluations for sustainable carbon–neutral methanol production

Quantifying the impact of intraparticle convection within fixed beds formed by catalytic particles with low macro-porosities

Computational fluid dynamics (CFD) modeling plays a pivotalrole in optimizing fixed bed catalytic chemical reactors to enhance performancebut must accurately capture the various length- and time-scales that underpinthe complex particle−fluid interactions. Within catalytic particles, a range ofpore sizes exist, with micro-pore scales enhancing the active surface area forincreased reactivity and macro-pore scales enhancing intraparticle heat andmass transfer through intraparticle convection. Existing particle-resolved CFDmodels primarily approach such dual-scale particles with low intraparticlemacro-porosities as purely solid. Consequently, intraparticle phenomenaassociated with intraparticle convection are neglected, and their impact inthe full bed scale is not understood. This study presents a porous particle CFDmodel, whereby individual particles are defined through two distinct porosity terms, a macro-porosity term responsible for theparticle’s hydrodynamic profile and a micro-porosity term responsible for diffusion and reaction. By comparing the flow profilesthrough full beds formed by porous and solid particles, the impact of intraparticle convection on mass and heat transfer, as well as ondiffusion and reaction, was investigated

Dataset in support of the article &#39;Rationalising catalytic performance using a unique correlation matrix&#39;

Primary, raw data used for the &ldquo;Rationalising catalytic performance using a unique correlation matrix&rdquo; articled published in ChemComm. </span