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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...

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 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 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.

In this study, three-phase flow model together with a thermal-equilibrium model was developed to study the operation of downdraft biomass gasifiers. Gasification experiments were conducted to obtain pyrolysis kinetics and validate the models. A good agreement was found between experiment data and model predictions, in terms of syngas composition and temperature, respectively. Kinetics based on experimental study improves the accuracy of simulation. The thermal-equilibrium model was applied to study the effects of air to biomass ratio on gas composition, LHV (lower heating value), and temperature. The 3D multiphase flow model was applied to investigate the spatial distributions of various parameters (i.e. pressure, gas velocity, temperature, and gas composition) inside the gasifier that are critical to the design of gasifier. A rough division of four gasification zones was determined based on temperature profile. It was also found that the cold gas efficiency was around 63% based on CFD (computational fluid dynamic) simulation. The temperature distributions could be used to guide the application of heat resistant materials inside the gasifier. In addition, the simulation results indicated that blockage of the gasifier has a high chance to occur at the top of reduction bell when using feedstock of high metal contents. Effects of reduction bell dimension and operation conditions on the temperature distribution and syngas production were also investigated by the 3D CFD model, which sheds light on the improvement of the design and operation of reactor. The syngas production could be enhanced by varying the size of reduction bell.

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.

A challenge for the design of nonlinear polymerization is the full appreciation of the impact of side reactions, demanding the development of modeling techniques to determine the associated kinetic parameters while using the most important experimental responses. Here the combination of computationally inexpensive method of moments (MoM) kinetic simulations and dedicated principal component analysis (PCA) is put forward as a promising strategy to be successful in this respect. Focus is on (radical) vinyl grafting of chains containing unsaturations, selecting styrene (St) as monomer and polybutadiene (PB) as backbone, and low St conversions accounting for diffusional limitations on termination. It is highlighted that the less studied macropropagation cannot be directly ignored and a combined set of experimental responses related to free polystyrene and grafted copolymer (GC) average product properties is recommended for kinetic parameter estimation. This is supported by regression analysis considering in silico generated experimental data compensated for random noise and considering a validated end-chain approximation.

This work extends filtered models to investigate the influence of particle polydispersity on the filtered fluid-particle drag and heat transfer behaviors based on well-resolved computational fluid dynamics-discrete element model (CFD-DEM) simulations. By systematically filtering the CFD-DEM simulation data, it is found that the markers used for the monodisperse systems, (i.e., the filtered slip velocity and the gas phase pressure gradient for the drag correction; the filtered interphase temperature difference for the heat transfer correction) are indispensable in the bidisperse systems. Furthermore, the dependence of the filtered corrections on the particle size ratio and volume fraction ratio is identified. The increase in the proportion of small particles generally corresponds to the reduction of drag and interphase heat transfer corrections. The Sauter mean diameter is applicable to quantify the effect of particle bidispersity. Eventually filtered models of fluid-particle drag and heat transfer corrections for the bidisperse systems are developed.

A developed three-fluid computational fluid dynamics model was used to investigate the three-phase system in a polyethylene industrial fluidized bed in condensed mode. This model was incorporated with a liquid vaporization modeling scheme comprising heat transfer model, droplet vaporization model, and population balance model to describe liquid evaporation. Numerical results demonstrate that liquid vaporization affects gas velocity distribution, thereby influencing the flow structure. With increasing condensed-liquid contents, the bed temperature gradient at the bed bottom increases and the overall bed temperature decreases. The vaporization rate increases with increasing droplet Sauter mean diameter. Hence, the influence of liquid vaporization is sensitive to droplet diameter.

Successful fabrication of mussel-inspired V-shaped copolymer based materials for pH controllable oil/water separation was reported. Triblock copolymer, polydimethylsiloxane-block-poly(2-hydroxyethyl methacrylate)-block-poly(2-(dimethylamino)ethyl methacrylate) (PDMS- b -PHEMA- b -PDMAEMA) was designed and synthesized through copper(0)-mediated reversible-deactivation radical polymerization (RDRP). Hydroxyl-containing PHEMA block enabled the covalent reaction to occur between polymer and polydopamine (PDA) functionalized substrates. The mixed polymer brushes of oleophilic/hydrophobic PDMS and pH-responsive PDMAEMA endowed the substrates with good pH responsive oil/water wettability and stability. As a proof of concept, the functionalized mesh can separate a range of different immiscible oil/water mixtures with high separation efficiency over 98.5% and high oil flux of 8800–9500 L h −1 m −2 and water flux of 7400–7800 L h −1 m −2 . Additionally, the functionalized sponge realized reversible oil capture and release in aqueous media. Because dopamine through self-polymerization can form strong adhesive films on inorganic and organic substrates in alkalescence solution, the method used in current contribution would be also suitable for producing polymer brush functionalized intelligent materials based on other common substrates.

A low-ppm-level iron (Fe)-based photoinduced atom-transfer-radical polymerization (ATRP) under visible-light irradiation was developed. Various ligands, tris(4-methoxyphenyl)phosphine (TMPP), 4,4′-dinonyl-2,2′-dipyridyl (dNbpy), and tris[2-(dimethylamino)ethyl]amine (Me6TREN), were used to enhance the catalytic activity of Fe complexes. Activator FeII complexes were formed by the reduction of FeIII complexes with a monomer under visible-light irradiation. Linear semilogarithmic plots and low polydispersities (Mw/Mn dNbpy > Me6TREN. Additionally, this polymerization could be ceased and restarted, responding to light off and light on. Retention of the chain-end functionality was anal...

Partially water-soluble monomers achieve phase equilibrium between aqueous and dispersed phases, and the interphase mass transfer plays a significant role in suspension polymerization. This work developed a computational fluid dynamics model to simulate the liquid–liquid suspension polymerization process. Complex multiphase flow behaviors and multiscale polymer properties were simultaneously simulated using the combination of an Eulerian–Eulerian two-phase flow model, an atom transfer radical polymerization kinetics model, a population balance model (PBM), and an interphase transfer model. The simulation results for monomer conversion, molecular weight (Mn), polydispersity index (PDI), and Sauter mean diameter (d32) agreed well with the experimental data, thereby validating the soundness of the multiscale model. The proposed model was then employed to predict key variables of the polymerization system. The coupled model can benefit the optimization and scale-up of suspension polymerization reactors with s...

Knowledge of the heat transfer coefficients in bubble columns can offer valuable references for the design and scale-up of reactors. This work aims to develop reliable correlations that can be applied in precisely predicting the global and local heat transfer coefficients in air-water or air-water-glass beads bubble columns. The influence of superficial gas velocities, column dimensions, volume fraction of particles, internals and radial positions on the heat transfer coefficients is considered in the developed correlations. In addition, the effect of the function forms on the accuracy of calculated local heat transfer coefficients is investigated. The Levenberg-Marquardt Algorithm is used to determine the values of the parameters in the empirical correlations. The recommended correlations can be applied in accurately predicting the heat transfer coefficients in bubble columns.

This study investigated the performance of microreactor system in conducting the oxidation of KA oil with nitric acid through mathematical models. Experiments were also carried out via a self-designed microreactor system. Two one-dimensional (1D) pseudohomogeneous models and a two-dimensional (2D) reactor model were developed according to the mass balance and power law kinetics. The calculated results based on different models were compared with experimental data to evaluate the model reliability. The results showed that 1D axial dispersion model and 2D reactor model exhibited comparable accuracy in predicting the conversion of the intermediates, while 2D reactor model was more accurate in predicting the yield of products. With the 2D reactor model, the sensitivity study showed that temperature was the most sensitive parameter. Further studies on the effect of different factors were carried out via the developed model to shed lights on the design and operation of industrial-scale microreactors.