Showing papers in "Aiche Journal in 2018"

Machine learning for heterogeneous catalyst design and discovery

Abstract: Advances in machine learning (ML) are making a large impact in many fields, including: artificial intelligence, materials science, and chemical engineering. Generally, ML tools learn from data to find insights or make fast predictions of target properties. Recently, ML is also greatly influencing heterogeneous catalysis research due to the availability of ML (e.g., Python Scikit-learn, TensorFlow) and workflow management tools (e.g., ASE, Atomate), the growing amount of data in materials databases (e.g., Novel Materials Discovery Laboratory, Citrination, Materials Project, CatApp), and algorithmic improvements. New catalysts are needed for sustainable chemical production, alternative energy, and pollution mitigation applications to meet the demands of our world’s rising population. It is a challenging endeavor, however, to make novel heterogeneous catalysts with good performance (i.e., stable, active, selective) because their performance depends on many properties: composition, support, surface termination, particle size, particle morphology, and atomic coordination environment. Additionally, the properties of heterogeneous catalysts can change under reaction conditions through various phenomena such as Ostwald ripening, particle disintegration, surface oxidation, and surface reconstruction. Many heterogeneous catalyst structures are disordered or amorphous in their active state, which further complicates their atomic-level characterization by modeling and experiment. Computational modeling using quantum mechanical (QM) methods such as density functional theory (DFT) can accelerate catalyst screening by enabling rapid prototyping and revealing active sites and structure-activity relations. The high computational cost of QM methods, however, limits the range of catalyst spaces that can be examined. Recent progress in merging ML with QM modeling and experiments promises to drive forward rational catalyst design. Therefore, it is timely to highlight the ability of ML tools to accelerate

Chemical solvent in chemical solvent: A class of hybrid materials for effective capture of CO2

Abstract: Amino acid ionic liquids (AAILs) are chemical solvents with high reactivity to CO2. However, they suffer from drastic increase in viscosity on the reaction with CO2, which significantly limits their application in the industrial capture of CO2. In this work, 1-ethyl-3-methylimidazolium acetate ([emim][Ac]) which also exhibits chemical affinity to CO2 but low viscosity, and its viscosity does not increase drastically after CO2 absorption, was proposed as the diluent for AAILs to fabricate hybrid materials. The AAIL+[emim][Ac] hybrids were found to display enhanced kinetics for CO2 absorption, and their viscosity increase after CO2 absorption are much less significant than pure AAILs. More importantly, owing to the fact that [emim][Ac] itself can absorb large amount of CO2, the AAIL+[emim][Ac] hybrids still have high absolute capacities of CO2. Such hybrid materials consisting of a chemical solvent plus another chemical solvent are believed to be a class of effective absorbents for CO2 capture. © 2017 American Institute of Chemical Engineers AIChE J, 64: 632–639, 2018

Computer-aided design of ionic liquids as solvents for extractive desulfurization

Abstract: Although ionic liquids (ILs) have been widely explored as solvents for extractive desulfurization (EDS) of fuel oils, systematic studying of the optimal design of ILs for this process is still scarce. The UNIFAC-IL model is extended first to describe the EDS system based on exhaustive experimental data. Then, based on the obtained UNIFAC-IL model and group contribution models for predicting the melting point and viscosity of ILs, a mixed-integer nonlinear programming (MINLP) problem is formulated for the purpose of computer-aided ionic liquid design (CAILD). The MINLP problem is solved to optimize the liquid-liquid extraction performance of ILs in a given multicomponent model EDS system, under consideration of constraints regarding the IL structure, thermodynamic and physical properties. The top five IL candidates preidentified from CAILD are further evaluated by means of process simulation using ASPEN Plus. Thereby, [C5MPy][C(CN)3] is identified as the most suitable solvent for EDS. © 2017 American Institute of Chemical Engineers AIChE J, 2017

Machine learning for crystal identification and discovery

Abstract: As computers get faster, researchers -- not hardware or algorithms -- become the bottleneck in scientific discovery. Computational study of colloidal self-assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self assembly. We create numerical fingerprints -- inspired by bond orientational order diagrams -- of structures found in self-assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis methods to keep up with the data generation ability of modern high-throughput computing environments.

A flowsheet model for the development of a continuous process for pharmaceutical tablets: An industrial perspective

Abstract: A dynamic model of a continuous direct compression process for pharmaceutical tablets is presented. The objective is to assess the impact of the variability from the feeder system on the concentration of drug in the powder in the feed frame of a tablet press. The model is based on principles of dispersed flow from the reaction engineering field. An estimability analysis was performed to understand the impact of the available measurements on the estimated parameters and suggest better ways to approach the parametrization. Predictions are successfully contrasted with experimental data. The model is used to produce residence time distributions at different process conditions and a graphical representation of the allowable range of disturbances in the feeders that can be mitigated by the process. The model was used in support of the method development for an online near infrared sensor. © 2017 American Institute of Chemical Engineers AIChE J, 64: 511–525, 2018

Experiments on Breakup of Bubbles in a Turbulent Flow

Abstract: The breakup of air bubbles in a turbulent water flow is studied experimentally. Water flows from a nozzle array, generating intense turbulence, and then flows downward through a cell. The velocity field is measured by PIV, and the local dissipation rate is estimated using a large-eddy PIV technique. Bubbles (1.8 to 5 mm) are injected in the bottom of the cell and rise toward the region of intense turbulence, where they break. The time spent by bubbles in various zones without breaking and the number of breakups are evaluated, providing information about the breakup frequency. The number of daughter bubbles and their size distribution are determined. The number of daughters depends on a Weber number 2ρe2/3 D'5/3/σ, where e is the turbulent energy dissipation rate, D' is the mother particle size, ρ and σ are the liquid density and surface tension. The daughter size distribution is a function of their number. This article is protected by copyright. All rights reserved.

Enhanced water flux through graphitic carbon nitride nanosheets membrane by incorporating polyacrylic acid

Abstract: Membranes assembled from two-dimensional (2D) layered materials have shown potential use in water purification. Recently, a 2D graphitic carbon nitride (g-C3N4) nanosheets membrane exhibit considerable separation performance in water purification. In this study, to further improve this water separation performance, polyacrylic acid (PAA) was introduced to tune the nanochannels formed between the g-C3N4 nanosheets. The fabricated g-C3N4-PAA hybrid membranes possessed higher water flux without sacrificing much rejection rate compared with that of the g-C3N4 membrane; however noticeable fouling was observed upon addition of the PAA into the membrane composite structure. In addition, the effect of PAA on the morphology, surface hydrophilicity, separation performance, and antifouling properties of the g-C3N4 membrane were examined in detail. Overall, incorporating PAA into the g-C3N4 nanosheets membrane was an effective and convenient method to improve the water separation performance, which could promote the application of the 2D g-C3N4 Separations: Materials, Devices and Processes AIChE Journal DOI 10.1002/aic.16076 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/aic.16076 © 2018 American Institute of Chemical Engineers (AIChE) Received: Jul 04, 2017; Revised: Dec 27, 2017; Accepted: Dec 28, 2017 This article is protected by copyright. All rights reserved. 2 nanosheets membrane in practical ultrafiltration processes.

Formation of liquid–liquid slug flow in a microfluidic T-junction: Effects of fluid properties and leakage flow

Abstract: Characteristics of liquid–liquid slug flow are investigated in a microchannel with focus on the leakage flow that bypasses droplets through channel gutters. The results show that the leakage flow rate varies in a range of 10.7–53.5% and 8.3–30.9% of the feed flow rate, during the droplet formation (i.e., at T-junction) and downstream flow (i.e., in the main channel), respectively, which highly depends on Ca number and wetting condition. Empirical correlations are proposed to predict them for perfectly and partially wetting conditions. Leakage flow contribution is further used to improve the Garstecki model for size scaling in order to extend its suitability for both squeezing and shearing regimes. The instantaneous flow rates of the immiscible phases are found to fluctuate periodically with the formation cycles, but in opposite behavior. The effect of the presence of leakage flow on such fluctuation are investigated and compared with gas–liquid systems. © 2017 American Institute of Chemical Engineers AIChE J, 2017

Liquid–liquid two-phase flow in ultrasonic microreactors: Cavitation, emulsification, and mass transfer enhancement

Abstract: The effects of ultrasound on the hydrodynamic and mass transfer behaviors of immiscible liquid-liquid two-phase flow were investigated in a domestic ultrasonic microreactor. Under ultrasonic irradiation, cavitation bubble was generated and underwent violent oscillation. Emulsification of immiscible phases was initiated by virtue of oscillating bubbles shuttling through the water/oil interface. The pressure drop was found to decrease with increasing ultrasound power, with a maximum decrement ratio of 12% obtained at power 30 W. The mass transfer behavior was characterized by extraction of Rhodamine B from water to 1-octanol. An enhancement factor of 1.3-2.2 on the overall mass transfer coefficient was achieved under sonication. The mass transfer performance was comparable to passive microreactor at similar energy dissipation rate (61-184 W/kg). The extraction equilibrium was reached under a total flow velocity 0.01 m/s and input power 20 W and 30 W, exhibiting its potential use in liquid-liquid extraction process. This article is protected by copyright. All rights reserved.

Combined effects of soot load and catalyst activity on the regeneration dynamics of catalytic diesel particulate filters

Abstract: The combined effects of soot load and catalyst activity on the regeneration dynamics of a catalytic diesel particulate filter have been investigated through transient CFD‐based simulations of soot combustion in a single‐channel configuration. The soot load was changed by varying the amou...

Fenton-Like Degradation of Rhodamine B over Highly Durable Cu-Embedded Alumina: Kinetics and Mechanism

Abstract: Cu-embedded mesoporous alumina, as a Fenton-like catalyst prepared via a sol-gel method, showed excellent activity and durability for the degradation of refectory compounds. The origin of active sites for the generation of hydroxyl radicals (•OH) were thoroughly studied using multi techniques. Cu, as the only active element, could be penetrated into the bulk of alumina and some Cu atoms were embedded into the framework. The dynamic structure of surface Cu species (the variety of Cu+/Cu2+ ratio) during the reaction were determined as well. Furthermore, the structure plasticity of catalyst has proved by optimizing preparation and reaction conditions. A 98.53% degradation of RhB was recorded within 30 min, following a pseudo-first-order reaction rate expression. Electron spin resonance spectra and •OH scavenging experiments have confirmed that •OH is the main reactive oxidant for the elimination of RhB. By the surface-enhanced Raman spectroscopy and gas chromatography-mass spectrometer results, plausible pathways of RhB degradation were elaborated. This article is protected by copyright. All rights reserved.

Plasma assisted nitrogen oxide production from air : using pulsed powered gliding arc reactor for a containerized plant

Abstract: The production of NOx from air and air + O2 is investigated in a pulsed powered milli-scale gliding arc (GA) reactor, aiming at a containerized process for fertilizer production. Influence of feed mixture, flow rate, temperature, and Ar and O2 content are investigated at varying specific energy input. The findings are correlated with high-speed imaging of the GA dynamics. An O2 content of 40–48% was optimum, with an enhancement of 11% in NOx production. Addition of Ar and preheating of the feed resulted in lower NOx production. Lower flow rates produced higher NOx concentrations due to longer residence time in the GA. The volume covered by GA depends strongly on the gas flow rate, emphasizing that the gas flow rate has a major impact on the GA dynamics and the reaction kinetics. For 0.5 L/min, 1.4 vol % of NOx concentration was realized, which is promising for a containerized process plant to produce fertilizer in remote locations. © 2017 American Institute of Chemical Engineers AIChE J

Automated measurements of gas‐liquid mass transfer in micropacked bed reactors

Abstract: Gas-liquid mass transfer in micro-packed bed reactors is characterized with an automated platform integrated with in-line Fourier transform infrared spectroscopy. This setup enables screening of a multidimensional parameter space underlying absorption and with chemical reaction. Volumetric gas-liquid mass transfer coefficients (kLa) are determined for the model reaction of CO2 absorption in a methyl diethanolamine/water solution. Parametric studies are conducted varying gas and liquid superficial velocities, packed bed dimensions and packing particle sizes. The results show that kLa values are in the range of 0.24∼0.64 s−1, which is about one-to-two orders of magnitude larger than those of conventional trickle beds. An empirical correlation predicts kLa in micro-packed bed reactors in good agreement with experimental data. This article is protected by copyright. All rights reserved.

Novel semi-interpenetrating network structural phase change composites with high phase change enthalpy

Abstract: High phase change enthalpy, controllable temperature, and stable shape can expand the application of phase change materials (PCMs) in energy storage. In this study, a series of novel form-stable PCMs with high phase change enthalpy (169–195 J/g) and controllable temperature (45.3–61.4°C) were prepared. The PCMs exhibited a semi-interpenetrating polymer network (semi-IPN) structure resulting from the combination of polyethylene glycol (PEG) and a three-dimensional (3-D) network gel. The gel itself featured an inherent phase change characteristic and a 3-D network structure. Thus, it improved the phase transition enthalpy of the materials and facilitated the formation of a semi-IPN that endowed the materials with excellent form-stable properties. In addition, the latent heat of the composites (169–195 J/g) is much higher than most of the previously reported composites using PEG as phase change component (68–132 J/g). © 2017 American Institute of Chemical Engineers AIChE J, 64: 688–696, 2018

Modular methanol manufacturing from shale gas: Techno‐economic and environmental analyses of conventional large‐scale production versus small‐scale distributed, modular processing

Abstract: This paper presents comparative techno-economic and environmental analyses of four representative shale gas monetization options, namely, conventional shale gas processing, large-scale methanol manufacturing, modular methanol manufacturing with shale gas supplied by pipelines, and modular methanol manufacturing with consideration of plant relocation. We first present shale gas supply models for the four gas monetization options. Next, the process designs for shale gas processing and methanol manufacturing from shale gas are described. We develop detailed process simulation models for shale gas processing and methanol manufacturing with different scales using raw shale gas extracted from the Marcellus, Eagle Ford, and Bakken shale plays. On this basis, techno-economic analyses and environmental impact analyses are conducted for the four shale gas monetization options to systematically compare their economic and environmental performances based on the same conditions. The results show that modular methanol manufacturing is more economically competitive than conventional shale gas processing, although it leads to higher environmental impacts. Besides, modular methanol manufacturing is better than large-scale methanol manufacturing for raw shale gas produced from distributed, remote wells from both economic and environmental perspectives. This article is protected by copyright. All rights reserved.

Nonspherical particles in a pseudo-2D fluidized bed : Experimental study

Abstract: Fluidization is widely used in industries and has been extensively studied, both experimentally and theoretically, in the past. However, most of these studies focus on spherical particles while in practice granules are rarely spherical. Particle shape can have a significant effect on fluidization characteristics. It is therefore important to study the effect of particle shape on fluidization behavior in detail. In this study, experiments in pseudo-2D fluidized beds are used to characterize the fluidization of spherocylindrical (rod-like) Geldart D particles of aspect ratio 4. Pressure drop and optical measurement methods (Digital Image Analysis, Particle Image Velocimetry, Particle Tracking Velocimetry) are employed to measure bed height, particle orientation, particle circulation, stacking, and coordination number. The commonly used correlations to determine the pressure drop across a bed of nonspherical particles are compared to experiments. Experimental observations and measurements have shown that rod-like particles are prone to interlocking and channeling behavior. Well above the minimum fluidization velocity, vigorous bubbling fluidization is observed, with groups of interlocked particles moving upwards, breaking up, being thrown high in the freeboard region and slowly raining down as dispersed phase. At high flowrates, a circulation pattern develops with particles moving up through the center and down at the walls. Particles tend to orient themselves along the flow direction.

A Comprehensive Analysis of the BET Area for Nanoporous Materials

Abstract: The Brunauer-Emmett-Teller (BET) method has been used extensively to characterize the surface areas of porous materials by semi-empirical fitting of gas-adsorption isotherms. However, questions arise recently concerning the applicability and the exact meaning of the BET areas. In particular, there has been much debate about whether the BET method provides a faithful description of the geometrical areas of porous materials if the atomic structures are exactly known. In this work, we provide a comprehensive analysis of the BET areas for both model slit pores and crystalline porous materials using the grand canonical Monte Carlo simulation. Based on extensive simulation data for nitrogen adsorption at 77 K and the conventional models of materials characterization, we find no simple correlation between the BET and geometrical surface areas. For materials with the same BET area, their geometric surface areas may vary over one order of magnitude. This article is protected by copyright. All rights reserved.