A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean-squared-error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Non-estimable parameters are fixed at their initial values to prevent overfitting. The method will aid error-in-variables parameter estimation in many situations involving limited data.
In this article, a sustainable defect-engineering strategy for dealumination of Y zeolite is described. This strategy includes the green synthesis of a well-crystallized Y zeolite with point defects arising from the incorporation of Fe atoms by using a Fe-containing perlite and the subsequent preparation of ultra-stable Y (USY) zeolite by effective dealumination. The systematic characterizations verify that Fe atoms originally existing in the perlite are incorporated into the as-synthesized Y zeolite and function as point defects, leading to the distortion of framework Al. The step-by-step investigation of the dealumination process shows that vacancies are formed by the extraction of framework Fe in the ammonium exchange, and the framework dealumination is promoted under the combined effect of the distorted framework Al and the formed vacancies during the steaming treatment. The resulting USY zeolite owns excellent features in (hydro)thermal stability, pore structure and acid property, and thus exhibits outstanding catalytic cracking performance.
Electrochemical nitrate reduction reaction (NtrRR) towards ammonia, as an emerging and appealing technology alternative to the energy-intensive Haber-Bosch process and inefficient nitrogen reduction reaction, has recently aroused wide concern and research. However, the current research of the NtrRR towards ammonia lacks the overall performance comparison of various electrocatalysts. Given this, we here make a comparison of 12 common transition metal oxide catalysts for the NtrRR under a high cathodic current density of 0.25 A cm-2, wherein Co3O4 catalyst displays the best NtrRR selectivity towards ammonia with a highest Faradaic efficiency (85.15%) and a moderate NtrRR activity. Other external factors, such as nitrate concentrations in the electrolyte and applied potential ranges, have also been specifically investigated for the NtrRR. This work can provide constructive guidance to engineer the NtrRR electrocatalysts with higher activity, selectivity and stability in the future.
The exploration of efficient and environmentally friendly oxidation method is highly desirable to overcome the critical problems of poor selectivity and heavy metal contamination for the fine chemicals industry. Herein, a self-supported 3D Se-Ni5P4 nanosheet electrocatalyst was rationally designed and fabricated. Benefiting from the synergistic effect of aminoxyl radical and mesoporous Se-Ni5P4/GF, an excellent performance of ≥98% selectivity and 33.12 kg/(m3·h) space-time yield was obtained for sterol intermediate oxidation with the enhanced mass transfer effect of the continuous flow system. The doping of anionic selenium and phosphorus modulated the electronic structure of Se-Ni5P4, and the oxyhydroxides generated by surface reconstruction accelerated the turnover of TEMPO, thereby enhancing the intrinsic electrocatalytic activity. A scale-up experiment was conducted with stacked-flow electrolyzer demonstrated the application potential. This work provided an efficient synergistic electrocatalytic strategy to facilitate rapid electron and mass transfer for electrochemical alcohol oxidation and highlighted the potential for practical electrosynthesis applications.
To design D-amino acid dehydrogenase (DAADH) for enhanced stability, the interactions of the subunit interfaces of DAADH were analyzed. Interaction network analysis of DAADH indicated that there are only weak interactions between the A and B subunits. Several co-evolved residue pairs were selected for mutation to enhance interfacial interactions of subunits, and 11 designed MDHs were obtained. DA06 and DA11 were selected for experimental verification for their salt bridges are 1.4 and 1.2-fold of that of DAwild, respectively. DA11 can maintain 93% activity in 80℃, while it was only 40% for DAwild. Thermostabiliy study indicated the half-life of DA11 was 2-fold of DAwild. Molecular dynamics simulations revealed that the extraordinary stability of the DA11 was due to the formation of extra interfacial salt bridges. The paper provided a strategy of mutations outside the active site of enzyme by co-evolutionary analysis which can reduce the effect of the activity-thermostability trade-off.
The distribution of gas-liquid two-phase flow is one of significant effects on heterogeneous catalytic reactions. Ceramic membrane gas distributors (CMGD) were applied in improving gas-liquid distribution, and flow behavior of gas as dispersed phase in liquid phase was visualized via high-speed photograph. The average diameters of multi-scale bubbles were measured and modeled ranging from 10-5 to 10-2 m. The coalescence and trajectory of bubbles during rising process were observed, and two typical trajectories straight and spiral types were tracked. In order to inhibit coalescence of bubbles during rising process, internals manufactured by 3D printing were installed in the channel of ceramic membrane. The average bubble size of CMGD decreases 12 % from 392 to 345 μm compared to that of the original CMGD. The CMGD with internals enhances the heterogeneous catalytic reaction performance via providing large quantity of stabile multi-scale bubbles which could match the porous structure of catalyst.
Basic thermodynamic data plays an important role in chemical applications. However, the traditional acquisition of thermodynamic data through experiments is laborious. Thermodynamic data prediction is considered as an alternative to the experiments, especially when qualitative analysis is needed prior to experimental studies. In this work, we report a successful machine-learning based approach to predict the fundamental thermodynamics characteristics of vapor-liquid equilibrium (VLE) process. A new dataset of the VLE experimental data of 210 kinds of binary mixture with screened descriptors were constructed. The obtained results show that the VLE characteristics of the target system can be fully revealed for a pre-analysis by ML methods and the RF model has more excellent predictive ability on the VLE behavior than the ANN model. This work pioneers the development of the generalized model on the prediction of the VLE data and provide useful information for mechanistic study on the VLE phenomenon.
The bubble size, gas holdup, and interfacial area in a swirling contactor were investigated through experiments and simulations. The interfacial area was obtained for liquids and gases with Reynolds numbers Rel and Reg, respectively. The contactor was divided into 12 subregions. When Reg=23.8 and Rel =20075.4, regions near the side wall and center of the swirl contactor exhibited small bubbles with diameters of 0.33–0.40 and 0.38–0.45 mm, respectively. Rel was negatively related to bubble size, gas holdup, and interfacial area, whereas Reg was positively related. The maximum bubble interfacial area among the 12 subregions was 530 m-1,and for the entire swirling contactor was 196.3 m-1 with a gas–liquid ratio of 0.022. Euler-Euler simulations using the population balance model accurately predicted this area. Larger areas were obtained at lower Rel values. Increasing the liquid velocity is not necessary to achieve larger areas, which indicates a contactor with lower energy consumption.
Economical uranium adsorption from seawater remains a crucial task for energy and environmental safety. Aiming for improving the mass transfer rate of uranium adsorption. Herein, a novel 2D porous aromatic framework(PAF) based on nucleophilic substitution of 2,5-dichloro benzonitrile was synthesized, with an ordered prous structure, excellent stability and selectivity of uranium extraction from seawater. PAF shows excellent uranium adsorption capacity of 637 mg/g and 3.22 mg/g in simulated and real seawater because of highly accessible pores on the walls of open channels. In addition, benefiting from the super-hydrophilicity due to the presence of amidoxime groups attributes high selectivity and ultrafast kinetics with an uptake rate of 0.43±0.03 mg/g.day and allowing half-saturation within 1.35±0.09 day. This strategy demonstrates a potential of PAF not only in uranium trap but also possess a power to monitor water quality. This technique can be extended in other applications by sensible planning target ligands
We carried out 3-D simulations of monodisperse particle suspensions subjected to a constant shear rate with the view to investigate the effect of electrical double layers around the particles on apparent suspension viscosities. To this end, expressions for Debye length, zeta potential and ionic strength (pH) of the liquid were incorporated into our in-house lattice Boltzmann code that uses the Immersed Boundary method and includes sub-grid lubrication models. We varied the solids concentration and particle radius, keeping the particle Reynolds number equal to 0.1. We report on results with respect to the effect of pH (in the range 9 through 12) and Debye length on apparent viscosity and spatial suspension structures, particularly at higher solids volume fractions, and on the effect of flow reversals.
A hybrid pore-scale simulation method using Lattice-Boltzmann (LB) coupled with Langevin-Dynamics (LD) is proposed to investigate the transport physics of nanoparticles in microchannel. The controlling factors (i.e., ionic strength, particle diameter and Reynolds number) are investigated in the attachment process of NPs. It is observed that a threshold value of attachment efficiency exists as the ionic strength increases to about 0.01 M. Moreover, the ionic strength of aqueous phase has critical effect on the transport behavior of NPs. For the purpose of quantitatively characterizing the structure of NP suspensions under varying conditions, a general phase diagram including three flow patterns (isolated, transitional and clustered regime) is first proposed for NP suspension with specified ionic strength and Reynolds number. The outcomes of this work provide valuable insight on the critical importance of the particle size, ionic strength and hydrodynamic effects on the attachment and transport process of NPs in porous media
This paper presents an innovative approach to determine and model the kinetics of the catalytic oxidation of urea in alkaline medium on nickel(III) sites. Firstly, the kinetic law is established by considering two types of active sites, either from a chemically synthesized Ni-based powder or from a massive nickel electrode. Thus, the electrochemical regeneration kinetics of nickel(III) can be differentiated from the kinetics of the purely chemical pathway of NiOOH solid particles consumption by urea. Secondly, a mechanism for the urea indirect oxidation mediated by the nickel(III)/nickel(II) system is proposed to predict the formation of all the by-products, contained in the liquid phase that have been experimentally identified in our previous work. Finally, a model combining kinetic laws with diffusive and convective transport phenomena is constructed. The robustness and relevance of the latter are proven by comparing the experimental results obtained during laboratory-scale electrolyzes with those predicted by the model.
It is of great significance to study the stability of foams in the petroleum industry. Therefore, the stability mechanism of Span 20, the fluorinated surfactant FCO-80 and their compound system FS in a CO2 oil-based foam system was studied by molecular simulation. The sandwich model of CO2 oil-based foam was constructed to reveal the stability of the foam system from the microscopic perspective. The result shows that the oil-CO2 distance of the FS foam system is 16.087 Å, and the coordination number of oil molecules is 2.65. The diffusion coefficient of CO2 in the FS foam system is 3.94×10-6 cm2/s. This shows that under the synergistic effect of Span 20 and FCO-80, the diffusion coefficients of CO2 molecules are small, and the surface tension is reduced, which can improve the stability of foam. The results can supplement previous experimental results on the stability of oil-based foam.
Molecular imprinting technology has gained increasing attention and application in protein adsorption and separation. Bacterial growth on the imprinted material would reduce the adsorption selectivity of the imprinted cavity, contaminate the isolation products and shorten the service life of the material. To solve the above problems, carrier materials with dual antibacterial ability are constructed for the first time and novel surface protein imprinted microspheres (GO-PEI/MXene@MIPs) are manufactured. Thanks to the large exterior surface area, the saturation adsorption amount of GO-PEI/MXene@MIPs reaches 312.63 mg/g with an imprinting factor (IF) value of 3.16 within 90 min. Meanwhile, this imprinted material also exhibits a high ability to separate real samples as well as reusability. In addition, this material has excellent broad-spectrum antibacterial effects, which will significantly extend its service life in real-world environments. This study provides a feasible solution for the application of surface protein imprinted materials in real-world environments.
The thermodynamic properties at variable temperature and pressure, such as density (ρ) and viscosity (η) are necessary in chemical process design. The quantitative structure-property relationship (QSPR) is a quick and accurate method to obtain the properties from a large number of potential ionic liquids (ILs). The QSPR models for ρ and η may have “pseudo-high” robustness validated by leave-one-out cross-validation (LOO-CV) and weakened stability with the unbalanced data point distribution. A rigorous model evaluation method named the leave-one-ion-out cross-validation (LOIO-CV) was proposed to evaluate robustness of ILs QSPR models. Balancing the distribution of data points in ILs, two f(T,P,I)-QSPR models were developed with norm index (I) to predict ρ and η of ILs at variable temperature and pressure. LOIO-CV method can enhance the stability QSPR model in predicting the properties of ILs with new cations and anions, which is essential for data driven design of ILs.
A novel integrated rotary reactor for NOx reduction by CO and air preheating (iNA reactor) was proposed. NOx removal performance was investigated in a fixed-bed reactor, which was used to simulate the working conditions change in iNA reactor. Lab-synthesized Cu/FeCeOx were used as catalyst. Two different modes were tested with iNA reactor: short cycles and long cycles. Excellent NOx removal efficiencies of over 95% and 90% for short cycles and long cycles were observed in iNA reactor. Moreover, compared with the constant-temperature rotary reactor, better H2O and SO2 resistances were also found in iNA reactor. The reaction mechanism was proposed based on in-situ DRIFT study. NOx was stored as nitrates in the adsorption zone, and then decomposed rapidly by both high temperatures and CO, leading to the deep catalyst regeneration. Therefore, temperature swinging and the feed of CO were key to having high iNA reactor performance for NOx removal.
We describe a technique for particle-based simulations of heterogeneous catalysis in open-cell foam structures, which is based on isotropic Stochastic Rotation Dynamics (iSRD) together with Constructive Solid Geometry (CSG). The approach is validated by means of experimental results for the low-temperature water-gas shift reaction in an open-cell foam structure modeled as inverse sphere packing. Considering the relation between Sherwood and Reynolds number, we find two distinct regimes meeting approximately at the strut size Reynolds number 10. For typical parameters from the literature, we find that the catalyst density in the washcoat can be reduced considerably without a notable loss of conversion efficiency. We vary the porosity to determine optimum open-cell foam structures, which combine low flow resistance with high conversion efficiency and find large porosity values to be favorable not only in the mass transfer limited regime but also in the intermediate regime.
Micro-tomography (µCT) and nuclear magnetic resonance (NMR) have been used to characterize porous media for decades. Magnetic Resonance Imaging (MRI) enables direct visualization of pore architecture and many pulse sequences exist. In this work, we tested the MRI pulse sequence Zero Echo Time (ZTE) to study sandstone and carbonate for its ability to address short relaxation times. We aimed at resolving two fluid conduit scales, i.e. pores and fractures. In this research, we study tighter porous systems than those previously reported using ZTE. Additionally, Pore Cluster Analysis (PCA), combined with ZTE, can be used to analyze pore-fracture connectivity of relatively large core plugs. We show that ZTE can resolve two-scale pore systems simultaneously, i.e. fractures and pores. By combining Time-Domain NMR pore-size analysis and PCA, we show that careful selection of resolution is necessary to understand transport in porous media.