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The adipic acid is an important intermediate in the production of nylon, polyurethane and polyester resins. The industrial approach for preparing adipic acid is through the liquid catalytic oxidation of KA oil with nitric acid. In this work, a comprehensive model is developed for this reaction based on the kinetic study conducted in a continuous flow tubular reactor. The kinetic model fits well with the experimental results across the experimental conditions, and the average relative error between the calculated and experimental values is 5.7%. Results show that there was an induction period at the early stage of reaction. Moreover, it is found that at temperature range of 328–358 K, the formation rate of adipic acid strongly dependents on the temperature and nitric acid concentration. The developed model is used to predict the yield of adipic acid at 359–368 K. The work in this study could provide much knowledge for industrial tubular reactor design.

In this work, the effect of non-drag forces (lift force, turbulent dispersion force, wall lubrication force and virtual mass force) and bubble induced turbulence (BIT) on the CFD-PBM simulation of ...

The kinetics of the ultraviolet light-induced solution atom transfer radical polymerization (ATRP) of methyl acrylate using titanium oxide (TiO2) was investigated through experiments and modeling b...

This work aims at comparing surface tension models in VOF (Volume of Fluid) modeling and investigating the effects of gas distributor and gas velocity. Hydrodynamics of a continuous chain of bubbles inside a bubble column reactor was simulated. The grid independence study was first conducted and a grid size of 1.0 mm was adopted in order to minimize the computing time without compromising the accuracy of the results. The predictions were validated by comparing the experimental studies reported in the literature. It was found that all surface tension models can describe the bubble rise and bubble plume in a column with slight deviations.

Particle-resolved (PR) high-fidelity simulations, e.g., direct numerical simulation (DNS), have emerged as a powerful tool to precisely capture the full details of complex fluid-particle heat and mass transfer behaviors. The captured information can be used to constitute closures for unresolved conservation equations. However, such simulations are commonly performed under a specific variety of operating conditions. To broaden the range of closure model applicability, this fundamental study develops an enhanced correlation for the gas-particle transport rate in terms of Nusselt (Sherwood) number via collecting data points (e = [0.35, 1], Rep = [0, 550]) from open sources. The collected data are predicted with a mean relative error of 9.3%. The extended correlation is then systematically validated by comparison with experimental data and PR-DNS results. Finally, the correlation is applied to integrate with a macroscopic computational fluid dynamics (CFD) reactor model. Validation results reveal an enhanced improvement in mass and heat transfer predictions. Moreover, the overall thermal and reactive behaviors computed from the coupled reactor model achieve reasonably good accordance with PR-DNS predictions over various process conditions. The developed correlation is hopeful to improve the accuracy of coarse-grained simulation of interphase heat and mass transfer accompanied by heterogeneously catalyzed chemical reaction.

An electrochemically mediated atom transfer radical polymerization (eATRP) of methyl acrylate (MA) in 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM-PF6) ionic liquid (IL) was reported. Remarkably, the kinetic results revealed an extremely fast and well controlled polymerization in the presence of tris(2-(dimethylamino)ethyl)amine (Me6TREN). The monomer conversion reached more than 90% within a period of 300 s. Computational and simulation results indicated that the IL induced acceleration of polymerization can be attributed to the increased value of k t / k p 2 compared to the associated literature value. Additionally, polymerizations under different conditions, including ligand types, monomer/IL ratios, catalyst loadings, and targeted degrees of polymerization were explored. All the kinetic plots suggested superfast polymerization rates with good control over molecular weight and dispersity. Furthermore, the livingness of MA polymerization was confirmed by chain extension experiment. This work provides a new insight into eATRP in IL through experimentation and simulation and thus enriches the knowledge of reaction features of eATRP.

Developing, verifying and validating sub-grid methods is of crucial significance for enabling accurate coarse-grained two-fluid modeling of rapid gas-fluidized flows. However, very few studies in the literature so far have been focused on how the sub-grid modification depends on material properties. As an extension of previous sub-grid efforts, this fundamental investigation attempts to derive a material-property-dependent drag modification based on generated data from an initially homogeneous state of periodic gas-particle suspensions. The newly constituted model is then verified by highly-resolved two-fluid simulation results. We further validate the accuracy of model predictions via systematic assessments to experimental results that encompass a wide variety of material properties in five three-dimensional large-scale circulating fluidized bed (CFB) risers. Besides, we introduce a deviation index (DI) to quantify the predictive capability of the extended model. Computational results demonstrate an essential dependence of drag correction on material properties as an additional factor. Hydrodynamic validation predictions achieve satisfactory accordance with experiments. The current model is potential to serve as a more general tool for efficiently reducing the number of pilot-scale tests and effectively designing and controlling industrial process devices.

Radiation intensity is commonly assumed to be the same for simplification in a lab-scale photoreactor, however, the radiation along propagating direction attenuates exponentially owing to the absorption and scattering of photons by catalysts and bubbles. Hence, it is necessary to develop a model that can predict the spatial radiation distribution (SRD), which is a critical factor in designing industrial-scale photoreactors. In this study, a SRD model was developed to compute the radiation intensity as a function of the volume fraction of the medium and the distance between the observation point and the source. Subsequently, numerical simulations and analyses for the gas-liquid-solid mini-fluidized bed were performed by coupling the proposed SRD model with the reaction kinetic model based on a three-dimensional hybrid Euler-Lagrange method. The simulation results fit well with experimental data. Furthermore, the radiation intensity and the hydrodynamic factors exert different impacts on the degradation ratio with increasing reaction time, respectively. Under low concentration, the main influence on the degradation ratio is hydrodynamics, including reactant mass fraction, air volume fraction and catalyst volume fraction. The developed model is of potential value in industrial photocatalytic reactors.

Hydrogenation of CO2 into methanol is promising for achieving the sustainable energy economy, but still has some problems, e.g. low methanol selectivity and high operation pressures (>10 atm). Herein, we prepared a Pt/film hybrid with highly dispersed Pt nanoparticles, and combined Pt/film with In2O3 to form a Pt/film/In2O3 catalyst. By using a dielectric barrier discharge (DBD) plasma reactor, a CO2 conversion of 37.0% and a methanol selectivity of 62.6% are achieved in the hydrogenation of CO2 with H2 on Pt/film/In2O3 at 1 atm and 30 °C. These are higher than those on Pt/In2O3 prepared by the conventional high-temperature H2 reduction (24.9% and 36.5%) and commercial Cu/ZnO/Al2O3 (25.6% and 35.1%). The high-energy electrons of the DBD plasma trigger the CO2 hydrogenation at 1 atm and 30 °C. The higher Pt nanoparticles dispersion, film and In2O3 promote the adsorption of CO2 on Pt/film/In2O3, thus enhancing the hydrogenation of CO2 into methanol. These results are helpful for efficient methanol production from CO2 hydrogenation under atmospheric pressure.

A water-soluble organic photoredox catalyst, 3,7-methoxypolyethylene glycol 1-naphthalene-10-phenoxazine (Naph-PXZ-PEG), that can catalyze aqueous organocatalyzed atom transfer radical polymerization (ATRP) via oxidative quenching cycle has been reported for the first time. Comparative studies of Naph-PXZ-PEG and EosinY involved aqueous organocatalyzed ATRP systems have been done via polymerization experiment and kinetic modeling approach. Results showed that the polymerization via oxidative quenching cycle in Naph-PXZ-PEG system proceeded much faster and higher initiator efficiency than the polymerization via reductive quenching cycle in Eosin Y system under same conditions. Detailed information of the Naph-PXZ-PEG was presented by experiments and density functional theory (DFT) simulation. A series of kinetics experiments under different catalyst loadings, initiator concentrations, “on-off” switch of light and chain extension have been conducted and confirmed the good controllability of the current system and high end-group fidelity. This work provides a systematic study on developing an effective water soluble organic catalyst for the preparation of the well-defined polymers by a “green” and sustainable ATRP.

Development and validation of sub-grid models are of pivotal importance for coarse-grid two-fluid modeling of gas-particle fluidization. In prior study (Zhu et al., 2018, Chem. Eng. Sci. 192, 759–773), we developed an effective three-marker sub-grid drag model to consider the local heterogeneity in gas-particle flows. In this study, we perform a comprehensive 3D hydrodynamic validation analysis of the developed model to its predictability. Specifically, the validation covers more flow situations with respect to rapid, turbulent and bubbling fluidization. Besides, we introduce a simplified wall correction factor for assessing how the bounding walls impact on flow hydrodynamics. Computational results accord well with the experimental data over various flow regimes. The developed model can adequately capture the macroscopic flow properties without an additional wall modification. Compared with the fine-grid two-fluid modeling using the uniform drag model for a lab-scale riser, approximately 44 times speedup in computational time is achieved via the developed model. A much more significant reduction in computational time can be consequently expected for industrial-scale reactors.

The Kolbe-Schmitt reaction is the traditional method for preparing 2,4- and 2,6- dihydroxybenzoic acid (2,4- and 2,6-DHBA). In this work, accurate kinetic models for the Kolbe-Schmitt synthesis of 2,4- and 2,6-DHBA were successfully developed. The relative errors between the theoretical and experimental 2,4-DHBA equilibrium yields are less than 3.7% as T = 348–473 K, [C6H6O2] = 0.4–0.8 M and [KHCO3] = 1.2–4.0 M. The effects of reaction temperature, reaction time, KHCO3 and resorcinol (C6H6O2) concentrations on the formation rates and the yields of 2,4- and 2,6-DHBA were investigated by the developed models. Results show that the 2,4-DHBA equilibrium yield exhibits a strong dependence on the KHCO3 concentration, and the formation rate of 2,4-DHBA is strongly dependent on the reaction temperature. Furthermore, the reaction time is a critical factor in controlling the ratio of 2,4-DHBA to 2,6-DHBA as the reaction temperature varies from 433 to 473 K. Additionally, the results of the present work deepen the understanding of the reaction mechanism, e.g. the rate-determining step and main reaction pathway of 2,6-DHBA formation.

Particle aggregation and breakage phenomena are widely found in various industries such as chemical, agricultural and pharmaceutical processes. In this study, a physics-informed neural network is developed for solving both the forward and inverse problems of particle aggregation and breakage processes. In this method, the population balance equation is directly embedded in the loss function of a neural network so that the network can be trained efficiently and fulfil physical constraints. For the forward problems, solutions of population balance equations are obtained through the optimization of the neural network where the predictions well match the analytical solutions. In the inverse modelling, the data-driven discovery of model parameters of population balance equations is investigated. The sensitivity regarding the selection of different neural network structures is also investigated. The developed population balance equations embedded with neural network approach is promising for solving inverse problems of particle aggregation and breakage processes with noisy observation data.

The past decades have witnessed an increasing interest in developing advanced polymerization techniques subjected to external fields. Various physical modulations, such as temperature, light, electricity, magnetic field, ultrasound, and microwave irradiation, are noninvasive means, having superb but distinct abilities to regulate polymerizations in terms of process intensification and spatial and temporal controls. Gas as an emerging regulator plays a distinctive role in controlling polymerization and resembles a physical regulator in some cases. This review provides a systematic overview of seven types of external-field-regulated polymerizations, ranging from chain-growth to step-growth polymerization. A detailed account of the relevant mechanism and kinetics is provided to better understand the role of each external field in polymerization. In addition, given the crucial role of modeling and simulation in mechanisms and kinetics investigation, an overview of model construction and typical numerical methods used in this field as well as highlights of the interaction between experiment and simulation toward kinetics in the existing systems are given. At the end, limitations and future perspectives for this field are critically discussed. This state-of-the-art research progress not only provides the fundamental principles underlying external-field-regulated polymerizations but also stimulates new development of advanced polymerization methods.

The kinetic rate constant of volatile organic compounds (VOCs) degradation represents an important parameter, which is valuable for evaluating the degradation efficiency and ecological risk of pollutants. In this study, the multiple-linear-regression method using quantum chemical and norm descriptors is utilized to develop a room-temperature quantitative structure-property relationships (QSPR) model for kinetic rate constant estimation. The correlation coefficient (R2) and root-mean-square error (RMSE) are 0.8918 and 0.4086 for the training set, as well as 0.9096 and 0.3901 for the test set, respectively, which suggests the as-developed model has good stability and predictability. Applicability domain analysis demonstrates that the model is reliable and generalizable for assessing the -logk·OH of VOCs covering a wide variety of molecular structures. In addition, an external prediction is made to assess the degradation rate constants of nine hydrofluoroethers, which implies the predictability of the model. It is worth noting that the quantum mechanical parameters, i.e., natural population analysis and orbital energy for atoms are introduced to norm descriptors, which expands the number/type of norm descriptors and greatly improves the accuracy of the model. Such combinational quantum chemical and norm descriptors are expected to be used for building accurate and robust models for other chemical properties prediction.

Melt crystallization is a promising and widely used method for obtaining pure compounds. Nonetheless, separation of 3,3-dilauryl thiodipropionate (DLTP) has not yet been realized by melt crystallization. Solid-liquid equilibrium is the fundamental for the separation process design. In this work, we evaluated the feasibility of the purification of DLTP from the DLTP/lauryl alcohol (LA) binary mixture by melt crystallization technique. For this purpose, the solid-liquid equilibrium data were measured using differential scanning calorimetry (DSC) technique. The experimental data were correlated by Wilson, Margules-3-suffix and Non-Random Two-Liquid (NRTL) activity coefficient models to obtain the binary parameters of the three models. Eutectic composition and temperature were calculated using NRTL model, which were xLA = 0.961 (molar fraction) and 295.79 K, respectively. On the basis of the solid–liquid equilibrium, the multistage countercurrent separation configurations were proposed for the separation of the binary system. The formulated Mixed Integer Nonlinear Programming (MINLP) problem was solved using the branch-and-bound algorithm. The conceptual designs for multistage countercurrent configuration were derived based on MINLP optimization by comparing results at various conditions. Results showed that at least 3 crystallization stages with keff ≤ 0.4 are required to obtain the 99% (mass fraction) DLTP product within the initial concentration range from10% to 30% (mass fraction).

This study develops filtered two-fluid model (fTFM) closures by coupling computational fluid dynamics (CFD) and deep learning algorithm (DL) for enabling coarse-grid simulations at reactor scales. Mesoscale drag, solids pressure and viscosity are modeled using an isotropic or anisotropic method. Subsequently, a priori analysis and a posteriori analysis of the present models along with other previously proposed closures are conducted. Comparison with the experimental data covering a broad range of operating conditions indicates that the mesoscale solids stress can be neglected in bubbling and turbulent fluidization regimes. However, the contribution of solids stress is clearly not insignificant at very low superficial gas velocities. Moreover, the drag model considering the anisotropy shows better prediction performance in the turbulent fluidization regime. In short, the present study develops and validates a DL-fTFM coupling algorithm applicable for gas-particle simulations.

The isomerization reaction of pinene is one of the most important chemical reactions in the deep processing of pinene. The purpose of this study is to improve the performance of the metatitanic acid by composite. The composite metatitanic acid catalyst TiM was prepared by adding Mn elements in the preparation process. The catalytic performance of TiM was evaluated. Comparison of TiM and metatitanic acid catalyst (Ti-FGP), the reaction rate of TiM catalyst was faster, and after the reaction, the yield of camphene and tricyclene increased about 1%. The catalysts were characterized by an SEM, FT-IR and laser particle size analyzer. The results show that the pinene isomerization reaction requires the synergistic action of the Brӧnsted acid and Lewis acid. Brӧnsted acid has great influence on the activity of catalyst, and Lewis acid has a great influence on the selectivity of the catalyst. The structure and morphology of the catalyst have a certain effect on the selectivity of pinene isomerization reaction.

The effect of meso-scale structures on hydrodynamic predictions is not considered in the classically uniform drag models when the coarse grid is used. To address this issue, this study tries to develop an effective three-marker drag correlation via straightforward sub-grid modeling, which accounts for a parabolic spatial concentration distribution within a computational grid. The reliability and accuracy of the developed model is then assessed in detail. How the uniform drag inputs affect the derived sub-grid correction is quantified for the first time. Besides, a comprehensive comparison between several typical sub-grid models and present work is implemented. Results reveal a systematic dependence of our drag modification on the concentration gradient as an additional marker. Coarse-grid hydrodynamic validation shows that the developed model yields a fairly improved agreement with experiments under various operating conditions in a 3D turbulent fluidized bed. Furthermore, results demonstrate that the present model using different uniform drag inputs still can exhibit satisfactory performance. The developed model is able to resolve the heterogeneous flow behavior both cheaply and adequately, which is potentially applied for industrial reactor design and optimization.

This modeling study investigates the influence of spatially distributed pore size and porosity on diffusional and reactive features inside the methanol-to-olefin (MTO) porous catalyst particle. The model developed in this study integrates mass, momentum and heat balance equations, spatial pore size and porosity distribution models, multicomponent diffusion and lumped kinetic models. The proposed model was first validated by using the experimental and empirical data. The simulation results demonstrated that the pore diameter and porosity that decrease toward the particle core is the optimal distribution for the MTO catalyst particle. Subsequent parametric sensitivity analysis indicated that the temperature plays the most significant role in the effectiveness factor. The smaller pore diameter favors the larger ratio of the ethylene to the propylene. Comparing with the traditional trial-and-error methods, the proposed model is simple but effective, which is valuable for the design and optimization of porous catalyst particles.

Suspension polymerization of methyl methacrylate (MMA) has been considered as a liquid–liquid reactive polydispersity system, which involves the complex multiphase flow behavior at multiple time and length scales. The polymerization kinetic characteristics (i.e., gel effect and glass effect) and liquid–liquid dispersion phenomena (i.e., breakage and coalescence of liquid droplets) appearing in this process make the study of suspension polymerization complicate. In this work, a three dimensional (3D) multiscale model including Eulerian–Eulerian two-fluid model, polymerization kinetics model, population balance model (PBM) and some other constitutive models was developed to elaborate those multiscale phenomena in polymerization course. The current model was validated using the reported data in terms of conversion, molecular weight as well as droplet Sauter diameter. The developed model was then employed to investigate the influence of key operating conditions on polymerization kinetic characteristics and liquid–liquid dispersion phenomena. Furthermore, the effects of reactor structure on droplet breakage and coalescence were studied in detail. This simulation work may contribute to the preparation of polymer products and the scaling up of stirred tank polymerization reactors with multiphase flow and multiscale characteristics.

Solid–liquid suspensions are commonly encountered in industrial production processes. The dynamics of the solid–liquid suspension behaviors depends on both liquid–particle and particle–particle interactions. In this work, an Eulerian–Eulerian model is used to characterize the suspension dynamics and the role of particle–particle interactions in solid-liquid mixing vessels is studied. The collision and friction of coarse particles are considered by calculating solid pressure and viscosity based on a modified kinetic theory of granular flow (KTGF). The solid phase holdup and the velocity are predicted and compared with semi-empirical models. Effects of several key model parameters are investigated as well. The comparison between computational fluid dynamics simulations and experimental data shows a satisfactory agreement, which validates the robustness of the multi-fluid model. The proposed model is then applied to study the influences of particle size and solid loading for exploring the importance of particle–particle interactions. The obtained simulation results show that particle–particle interactions can influence suspension characteristics in the case of large particle size and high solid loading.

The effect of geometric configurations on flow hydrodynamics, heat transfer, and residence time distribution (RTD) in a pilot-scale biomass pyrolysis vapor phase upgrading (VPU) reactor is comprehensively studied by numerical simulations. More specifically, three types of carrier gas structure, three types of particle feed structure, and five types of outlet structure are included in the present study. The results demonstrate that the effect of geometric configurations on heat transfer and RTD is much more pronounced than that on flow hydrodynamics such as the axial particle distribution, probably due to the low particle inventory. In this case, it is necessary to verify the difference of the temperature field in the grid independence analysis in addition to flow dynamics. Furthermore, we find that the carrier gas inlet and particle feed have an important influence on the particle distribution at the bottom of the reactor and thus affect the back-mixing behavior at both the bottom and outlet. As a consequence, such back-mixing flows further affect the temperature field and RTD substantially. Moreover, reducing the abrupt structure contributes to the catalyst particles well-distributed at the bottom of the reactor. This can reduce the mean residence time and variance, thereby decreasing the particle and gas temperature in the reactor. This work has a potential to provide a feasible guideline for biomass pyrolysis VPU reactor design and optimization.