The binary fluidization of Geldart-D type non-spherical wood particles and spherical LDPE particles was investigated in a laboratory-scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using Particle Image Velocimetry (PIV). The wood particles orientation and velocity are measured using Particle Tracking Velocimetry (PTV). A machine learning pixel-wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.

The degree of rate control quantitatively identifies the kinetically relevant (sometimes known as rate-limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, e.g. with finite differences. Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this work, we demonstrate the use of automatic differentiation in the evaluation of the degree of rate control. Automatic differentiation libraries are increasingly available through modern machine learning frameworks. Compared to the finite differences, automatic differentiation provides solutions with higher accuracy with lower computational cost. Furthermore, we illustrate a hybrid local-global sensitivity analysis method, the distributed evaluation of local sensitivity analysis (DELSA), to assess the importance of kinetic parameters over an uncertain space. This method also benefits from automatic differentiation to obtain high-quality results efficiently.

The droplet size distribution in liquid-liquid dispersions is a complex convolution of impeller speed, impeller type, fluid properties, and flow conditions. In this work, we present three a priori modeling approaches for predicting the droplet diameter distributions as a function of system operating conditions. In the first approach, called the two-fluid approach, we use high-resolution solutions to the Navier-Stokes equations to directly model the flow of each phase and the corresponding droplet breakup/coalescence events. In the second approach, based on an Eulerian-Lagrangian model, we describe the dispersed fluid as individual spheres undergoing ongoing breakup and coalescence events per user-defined interaction kernels. In the third approach, called the Eulerian-Parcel model, we model a sub-set of the droplets in the Eulerian-Lagrangian model to estimate the overall behavior of the entire droplet population. We discuss output from each model within the context of predictions from first principles turbulence theory and measured data.

High dimensional models typically require a large computational overhead for multiphysics applications, which hamper their use for broad-sweeping domain interrogation. Herein, we develop a modeling framework to capture the through-plane fluid dynamic response of electrodes and flow fields in a redox flow cell, generating a computationally inexpensive two-dimensional (2D) model. We leverage a depth averaging approach that also accounts for variations in out-of-plane fluid motion and departures from Darcy’s law that arise from averaging across three-dimensions (3D). Our Resulting depth-averaged 2D model successfully predict the fluid dynamic response of arbitrary in-plane flow field geometries, with discrepancies of < 5% for both maximum velocity and pressure drop. This corresponds to reduced computational expense, as compared to 3D representations (< 1% of duration and 10% of RAM usage), providing a platform to screen and optimize a diverse set of cell geometries.

Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. We propose an uncertainty quantification method for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure is based on the multiparameter delta method from statistics, which gives the standard error of the prediction. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability.

An integrated metal-organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design framework is presented for gas separation. It consists of two steps: descriptor optimization and MOF matching. In the first step, MOFs are represented as a large set of chemical and geometric descriptors from which the most influential ones are selected and treated as design variables. The valid design space is confined using a tailored classifier model and logic constraints. Based on collected adsorption isotherms of 471 different MOFs, data-driven isotherm models are developed. Combining the design space, isotherms, and process models, an integrated MOF and P/VSA process design problem is formulated. MOF descriptors and process operating conditions are optimized to maximize the process performance. The obtained optimal descriptors and isotherms can be used to guide the discovery of high-performance MOFs in a subsequent MOF matching step. This article addresses the first descriptor optimization step exemplified by propene/propane separation.

The force exerted on particles is of great significance to the flow and reaction characteristics of particles in gasifier. In this study, the unbalanced thrust, especially its magnitude, of a single char particle induced by chemical reactions during combustion process is investigated numerically, based on the random distribution of active sites. It is revealed that the nonuniform distribution of active sites directly leads to the nonuniform release of gaseous products, which accounts for the net induced thrust of particles. The effects of active site ratio, ambient gas temperature and particle diameter on the induced thrust of reaction particle were investigated. The results show that the induced thrust on particles could be equal to the magnitude of particle gravity. The induced thrust decreases with the increase of active area and particle diameter. And it is enhanced with the increase of ambient temperature.

Investigation on the miniaturized multichannel-based fixed-bed devices to enhance the heat and mass transfer performance is the key focus in the present study. Residence time distribution (RTD) is one of the most critical parameters to characterize the device’s flow distribution. In the current context, the RTDs of a liquid tracer for the air-water two-phase concurrent flows across the multichannel-based miniaturized fixed-bed devices (consist of 11 number of same dimensional parallel channels) with the variable heights were measured by the conductivity measurements and represented by axial dispersion model (ADM). The stream-flow rates of the two phases varied within the range of 8.33 × 10-8 – 3.83 × 10-7 m3 s-1. The axial dispersion coefficients and the specific energy dissipation values were analyzed. The impacts of pressure loss and the geometry on the hydrodynamic characteristics and mixing properties were well expressed. Based on the experimental data, new correlations were proposed.

The droplet generation mechanism in the step T-junction remains unknown, especially for the transition stage from dripping to jetting . In this work, the droplet generation mechanism was systematically investigated in a novel modified step T-junction. We found that under different fluid regimes, different factors take action. In dripping regime, the interfacial tension dominated the formation mechanism when the surfactant concentration was controlled below micelle concentration (CMC). In jetting regime, our experimental results showed that the influence of the surfactant concentration on the size of generated droplets was rather negligible while the phase ratio indeed determined such a parameter. In the dripping-jetting transition stage, an abnormal increase of droplet size was observed despite the increase of continuous phase flow. To the best for our knowledge, it is the first study to report generation mechanism in modified step T-junction from dripping to jetting regimes.

This article focuses on the catalytic hydrodenitrogenation (HDN) mechanism of indole under hydrothermal conditions. Both gaseous hydrogen and liquid hydrogen donor formic acid (FA) can improve indole conversion and total yield of denitrogenated products. Ru/C showed the highest activity among the catalysts for indole conversion in all temperature conditions with the existence of H2 and 91.17 % indole was converted at 400 °C and 60 min. Based on reaction kinetic experiments, a kinetic model was developed to describe the hydrothermal HDN reaction of indole over the home-made Ni80Ru20/γ-Al2O3 catalyst, which clearly captured all data trends and fitted the temporal variation of all major liquid products. High activation energy for formation of O-containing substance o-cresol from both mathematical fitting and density functional theory (DFT) calculation indicated a rare occurrence of reaction between pyrrole ring-opening product methyl aniline and H2O, consistent with experimental observation that only a trace of o-cresol was detected.

A rigorous mathematical model was developed for a complex liquid-liquid-solid system in a batch reactor. The approach is general but particularly well applicable for the indirect epoxidation of vegetable oils according to the concept of N. Prileschajew. The model considers intra- and interfacial mass transfer effects coupled to the reaction kinetics. The liquid phases were described with chemical approach (aqueous phase) and a reaction-diffusion approach (oil phase). The oil droplets were treated as rigid spheres, in which the overall reaction rate is influenced by chemical reactions and molecular diffusion phenomena. The model was tested with a generic example, where two reactions proceeded simultaneously in the aqueous and oil phases. The example (i.e. fatty acid epoxidation à la Prileschajew) illustrated the power of the real multiphase model in epoxidation processes. The proposed modelling concept can be used for optimization purposes for many applications, which comprise a complex water-oil-solid catalyst system.

High heat duty is an urgent challenge for industrial applications of amine-based CO2 capture. In this work, we report a novel, stable, efficient, and inexpensive Ni-HZSM-5 catalyst to reduce the heat duty. The density functional theory (DFT) calculations successfully explain the catalytic performance. The catalytic activity associates with the combined properties of MSA × B/L × Ni2+. The 7.85-Ni-HZ catalyst presents an excellent catalytic activity for the CO2 desorption: it increases the amount of desorbed CO2 up to 36%, reduces the heat duty by 27.07% compared with the blank run, and possesses high stability during five cyclic tests. A possible catalytic mechanism for the Ni-HZSM-5 catalysts through assisting carbamate breakdown and promoting CO2 desorption is proposed based on experimental results and theoretical calculations. Therefore, the results present that the 7.85-Ni-HZ catalyst significantly accelerates the protons transfer in CO2 desorption and can potentially apply in industrial CO2 capture.

This study presents the chromatographic purification of immunoglobulin G (IgG) from human plasma using a two-column process integrating the peptide-based adsorbents LigaGuardTM, which captures non-Ig plasma proteins in flow-through mode, and LigaTrapTM, which isolates IgG in bind-and-elute. Buffer composition and column loading were optimized for both adsorbents. Two process configurations were evaluated. In the first design, plasma was fed to a LigaGuardTM column to capture plasma proteins, the effluent was loaded on the LigaTrapTM column, and the bound IgG was eluted with 63.8% global recovery and 99.7% purity; in comparison, Protein G agarose afforded ~67% recovery and 97.2% purity. In the alternative design, the LigaGuardTM column was utilized to polish the LigaTrapTM elution stream, affording 82.3% global recovery and 98.8% purity. Collectively, these results demonstrate the potential of a fully chromatographic process for purifying polyclonal IgG from plasma feedstocks.

Nanomaterial drug delivery systems have become one of the most important targeted therapy technologies. Although great efforts have been made to study the self-assembled mesoscopic structure of nanoparticles and understand drug loading and release mechanisms, the interaction between nanoparticles and cell membranes has not yet been clearly studied. Moreover, the research of experimental methods in this field has been greatly restricted due to its special time-space scale, so it is necessary to apply computer simulations to visualize the cell internalization of the nanoparticle. This review covers modelling methods and the current status and viewpoints of research on the influencing factors of the nanoparticle-biomembrane interaction mechanism. In particular, we discussed in detail the positive and negative effects of various nanoparticle properties. This article may assist researchers in rationally optimize the nanoparticle structure to improve therapeutic efficiency.

The advanced use of a pH-responsive biomaterial-based injectable liquid implant for effective chemotherapeutic delivery in glioblastoma multiforme brain (GBM) tumour treatment is presented. As an implant, we proposed a water-in-oil-in-water multiple emulsion with encapsulated doxorubicin. The effectiveness of the proposed therapy was evaluated by comparing the cancer cell viability achieved in classical therapy (chemotherapeutic solution). The experimental study included doxorubicin release rates and consumption for two emulsions differing in drop sizes and structures in the presence of GBM-cells (LN229, U87 MG), and a cell viability. The results showed that the multiple emulsion implant was significantly more effective than classical therapy when considering the reduction in cancer cell viability: 85% for the emulsion-implant, and only 43% for the classical therapy. A diffusion-reaction model was adapted to predict doxorubicin release kinetics and elimination by glioblastoma cells. CFD simulations confirmed that the drug release kinetics depends on multiple emulsion structures and drop sizes.

Efficient and economical separation of 1,3-butadiene (C4H6) from C4 hydrocarbons is imperative yet challenging in industrial separation processes. Herein, a guest-induced flexible Mn-bpdc MOF has been employed to separate C4H6 from C4 hydrocarbons, including n-butene (n-C4H8), iso-butene (iso-C4H8), n-butane (n-C4H10) and iso-butane (iso-C4H10). Significantly, C4H6 can instantaneously induce gate-opening of Mn-bpdc MOF at 0.13 bar and 298 K, thus significant amounts of C4H6 can be adsorbed, while other C4 hydrocarbons cannot induce the gate-opening even at 1 bar. The uptake selectivities of Mn-bpdc MOF for C4H6/n-C4H8 and C4H6/iso-C4H8 are up to 40.0 and 45.0 at 298 K and 1 bar, respectively, both surpassing all the reported adsorbents. In addition, breakthrough experiments verified that C4H6/n-C4H8, C4H6/iso-C4H8, C4H6/n-C4H10 and C4H6/iso-C4H10 mixture can be efficiently separated. More importantly, Mn-bpdc possesses excellent water stability and outstanding regeneration ability for C4H6 separation, making it a new benchmark for C4H6 purification.

In this study, different calorimetric and analytical techniques were used to evaluate the thermal behaviour of the preparation of 1-nitronaphthalene through the nitration of naphthalene with mixed acid. Differential scanning calorimetry and adiabatic calorimetry revealed that 1-nitronaphthalene lacks thermal decomposition characteristics. The results of reaction calorimetry, high-performance liquid chromatography, and the density function theory method were combined to analyse the reaction mechanism. An expanded Stoessel criticality diagram was developed to evaluate the process safety of the semibatch nitration reaction. The results revealed a higher degree of risk than that obtained when using the traditional Stoessel criticality diagram. Kinetics parameters of the reaction were investigated.

We develop a simulation toolset employing density functional theory (DFT) in conjunction with grand canonical Monte Carlo (GCMC) to study coke formation on Fe-based catalysts during propane dehydrogenation (PDH). As expected, pure Fe surfaces develop stable graphitic coke structures and rapidly deactivate. We find that coke formation is markedly less favorable on Fe3C and FeS surfaces. Fe-Al alloys display varying degrees of coke resistance, depending on their composition, suggesting that they can be optimized for coke resistance under PDH conditions. Electronic structure analyses show that both electron-withdrawing effects (on Fe3C and FeS) and electron-donating effects (on Fe-Al alloys) destabilize adsorbed carbon. On the alloy surfaces, a geometric effect also isolates Fe sites and disrupts the formation of graphitic carbon networks. This work demonstrates the utility of GCMC for studying the formation of disordered phases on catalyst surfaces and provides insights for improving the coke resistance of Fe-based PDH catalysts.