The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
William J. Koros - One of the best experts on this subject based on the ideXlab platform.
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stabilization of porous morphologies for high performance carbon molecular sieve hollow fiber membranes
2012Co-Authors: Nitesh Bhuwania, William J. Koros, Paul Jason WilliamsAbstract:Carbon molecular Sieves (CMS) membranes having improved thermal and/or mechanical properties are disclosed herein. In one embodiment, a carbon molecular sieve membrane for separating a first and one or more second gases from a feed mixture of the first gas and one or more second gases comprises a hollow filamentary carbon core and a thermally stabilized polymer precursor disposed on at least an outer portion of the core. In some embodiments, the thermally stabilized polymer precursor is created by the process of placing in a reaction vessel the carbon molecular sieve membrane comprising an unmodified aromatic imide polymer, filling the reaction vessel with a modifying agent, and changing the temperature of the reaction vessel at a temperature ramp up rate and ramp down rate for a period of time so that the modifying agent alters the unmodified aromatic imide polymer to form a thermally stabilized polymer precursor.
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effect of processing on carbon molecular sieve structure and performance
Carbon, 2010Co-Authors: Mita Das, John D. Perry, William J. KorosAbstract:Sub-micron sized carbon molecular sieve (CMS) materials were produced via ball milling for subsequent use in hybrid material formation. A detailed analysis of the effects of the milling process in the presence of different milling environments is reported. The milling process apparently alters the molecular scale structure and properties of the carbon material. Three cases: unmilled, air milled and nitrogen milled, were analyzed in this work. The property changes were probed using equilibrium sorption experiments with different gases. Furthermore, WAXD and BET results also showed differences between milling processes. Finally in order to improve the interfacial polymer-sieve region of hybrid membranes, the CMS surface was chemically modified with a linkage unit capable of covalently bonding the polymer to the sieve. A published single-wall carbon nanotube (SWCNTs) modification method was adopted to attach a primary aromatic amine to the surface. Several aspects including rigidity, chemical composition, bulky groups and length were considered in selecting the preferred linkage unit. Fortunately kinetic and equilibrium sorption properties of the modified Sieves showed very little difference from unmodified samples, suggesting that the linkage unit is not excessively filling or obstructing access to the pores of the CMSs during the modification process.
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gas transport property performance of hybrid carbon molecular sieve polymer materials
Industrial & Engineering Chemistry Research, 2010Co-Authors: Mita Das, John D. Perry, William J. KorosAbstract:High-performance hybrid materials using carbon molecular sieve materials and 6FDA−6FpDA were produced. A detailed analysis of the effects of casting processes and the annealing temperature is reported. Two existing major obstacles, sieve agglomeration and residual stress, were addressed in this work, and subsequently a new membrane formation technique was developed to produce high-performing membranes. The successfully improved interfacial region of the hybrid membranes allows the Sieves to increase the selectivity of the membranes above the neat polymer properties. Furthermore, an additional performance enhancement was seen with increased sieve loading in the hybrid membranes, leading to an actual performance above the upper bound for pure polymer membranes. The membranes were also tested under a mixed-gas environment, which further demonstrated promising results.
Radmilo Colovic - One of the best experts on this subject based on the ideXlab platform.
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optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal chemometric and cfd approach
Advanced Powder Technology, 2017Co-Authors: Vojislav Banjac, Lato Pezo, Milada Pezo, Aleksandar Fišteš, đuro Vukmirovic, Dusica Colovic, Radmilo ColovicAbstract:Abstract In this study, sunflower meal is ground by a hammer mill after which air zigzag gravitational air classifier is used for separating sunflower hulls from the kernels in order to obtain protein rich fractions. Three hammer mill Sieves with sieve openings diameter of 3, 2 and 1 mm were used, while three air flows (5, 8.7 and 12.5 m 3 /h) and three feed rates (30%, 60% an 90% of bowl feeder oscillation maximum rate) were varied during air classification process. For describing the effects of the test variables on the observed responses Principal Component Analysis, Standard Score analysis and Response Surface Methodology were used. Beside experimental investigations, CFD model was used for numerical optimization of sunflower meal air classification process. Air classification of hammer milled sunflower meal resulted in coarse fractions enriched in protein content. The decrease in sieve openings diameter of the hammer mill sieve increased protein content in coarse fractions of sunflower meal obtained at same air flow, and at the same time decreased matching fraction yield. Increase in air flow lead to the increase in protein content along the same hammer mill sieve. Standard score analysis showed that optimum values for protein content and ratio of coarse and fine fractions have been obtained by using a sieve with 1 mm opening diameter, air flow of 12.5 m 3 /h and 60% of the maximum feeder rate. Fraction ratio and protein content were mostly affected by the linear term of air flow and the sieve openings diameter of the hammer mill sieve in the Second Order Polynomial model. The main focus of CFD analysis was on the particle simulation and the evaluation of the separation efficiency of the zigzag classifier.
Mita Das - One of the best experts on this subject based on the ideXlab platform.
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effect of processing on carbon molecular sieve structure and performance
Carbon, 2010Co-Authors: Mita Das, John D. Perry, William J. KorosAbstract:Sub-micron sized carbon molecular sieve (CMS) materials were produced via ball milling for subsequent use in hybrid material formation. A detailed analysis of the effects of the milling process in the presence of different milling environments is reported. The milling process apparently alters the molecular scale structure and properties of the carbon material. Three cases: unmilled, air milled and nitrogen milled, were analyzed in this work. The property changes were probed using equilibrium sorption experiments with different gases. Furthermore, WAXD and BET results also showed differences between milling processes. Finally in order to improve the interfacial polymer-sieve region of hybrid membranes, the CMS surface was chemically modified with a linkage unit capable of covalently bonding the polymer to the sieve. A published single-wall carbon nanotube (SWCNTs) modification method was adopted to attach a primary aromatic amine to the surface. Several aspects including rigidity, chemical composition, bulky groups and length were considered in selecting the preferred linkage unit. Fortunately kinetic and equilibrium sorption properties of the modified Sieves showed very little difference from unmodified samples, suggesting that the linkage unit is not excessively filling or obstructing access to the pores of the CMSs during the modification process.
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gas transport property performance of hybrid carbon molecular sieve polymer materials
Industrial & Engineering Chemistry Research, 2010Co-Authors: Mita Das, John D. Perry, William J. KorosAbstract:High-performance hybrid materials using carbon molecular sieve materials and 6FDA−6FpDA were produced. A detailed analysis of the effects of casting processes and the annealing temperature is reported. Two existing major obstacles, sieve agglomeration and residual stress, were addressed in this work, and subsequently a new membrane formation technique was developed to produce high-performing membranes. The successfully improved interfacial region of the hybrid membranes allows the Sieves to increase the selectivity of the membranes above the neat polymer properties. Furthermore, an additional performance enhancement was seen with increased sieve loading in the hybrid membranes, leading to an actual performance above the upper bound for pure polymer membranes. The membranes were also tested under a mixed-gas environment, which further demonstrated promising results.
Xiaohong Chen - One of the best experts on this subject based on the ideXlab platform.
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estimation of nonparametric conditional moment models with possibly nonsmooth generalized residuals
Econometrica, 2011Co-Authors: Xiaohong Chen, Demian PouzoAbstract:This paper studies nonparametric estimation of conditional moment restrictions in which the generalized residual functions can be nonsmooth in the unknown functions of endogenous variables. This is a nonparametric nonlinear instrumental variables (IV) problem. We propose a class of penalized sieve minimum distance (PSMD) estimators, which are minimizers of a penalized empirical minimum distance criterion over a collection of sieve spaces that are dense in the infinite dimensional function parameter space. Some of the PSMD procedures use slowly growing finite dimensional Sieves with flexible penalties or without any penalty; others use large dimensional Sieves with lower semicompact and/or convex penalties. We establish their consistency and the convergence rates in Banach space norms (such as a sup-norm or a root mean squared norm), allowing for possibly non-compact infinite dimensional parameter spaces. For both mildly and severely ill-posed nonlinear inverse problems, our convergence rates in Hilbert space norms (such as a root mean squared norm) achieve the known minimax optimal rate for the nonparametric mean IV regression. We illustrate the theory with a nonparametric additive quantile IV regression. We present a simulation study and an empirical application of estimating nonparametric quantile IV Engel curves.
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large sample sieve estimation of semi nonparametric models
Handbook of Econometrics, 2007Co-Authors: Xiaohong ChenAbstract:Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric models are more flexible and robust, but lead to other complications such as introducing infinite-dimensional parameter spaces that may not be compact and the optimization problem may no longer be well-posed. The method of Sieves provides one way to tackle such difficulties by optimizing an empirical criterion over a sequence of approximating parameter spaces (i.e., Sieves); the Sieves are less complex but are dense in the original space and the resulting optimization problem becomes well-posed. With different choices of criteria and Sieves, the method of Sieves is very flexible in estimating complicated semi-nonparametric models with (or without) endogeneity and latent heterogeneity. It can easily incorporate prior information and constraints, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. It can simultaneously estimate the parametric and nonparametric parts in semi-nonparametric models, typically with optimal convergence rates for both parts. This chapter describes estimation of semi-nonparametric econometric models via the method of Sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve M-estimates, pointwise normality of series estimates of regression functions, root-n asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite-dimensional parameters. Examples are used to illustrate the general results.
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chapter 76 large sample sieve estimation of semi nonparametric models
Handbook of Econometrics, 2007Co-Authors: Xiaohong ChenAbstract:Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric models are more flexible and robust, but lead to other complications such as introducing infinite-dimensional parameter spaces that may not be compact and the optimization problem may no longer be well-posed. The method of Sieves provides one way to tackle such difficulties by optimizing an empirical criterion over a sequence of approximating parameter spaces (i.e., Sieves); the Sieves are less complex but are dense in the original space and the resulting optimization problem becomes well-posed. With different choices of criteria and Sieves, the method of Sieves is very flexible in estimating complicated semi-nonparametric models with (or without) endogeneity and latent heterogeneity. It can easily incorporate prior information and constraints, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. It can simultaneously estimate the parametric and nonparametric parts in semi-nonparametric models, typically with optimal convergence rates for both parts. This chapter describes estimation of semi-nonparametric econometric models via the method of Sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve M-estimates, pointwise normality of series estimates of regression functions, root- n asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite-dimensional parameters. Examples are used to illustrate the general results.
Maohong Fan - One of the best experts on this subject based on the ideXlab platform.
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Extraction of lithium with functionalized lithium ion-Sieves
Progress in Materials Science, 2016Co-Authors: Xin Xu, Pingyu Wan, Hertanto Adidharma, Khaled A. M. Gasem, Kaiying Wang, Yongmei Chen, Ting He, Maohong FanAbstract:Abstract Due to the technology advancement and the large-scale application of lithium-ion batteries in recent years, the market demand for lithium is growing rapidly and the availability of land lithium resources is decreasing significantly. As such, the focus of lithium extraction technologies has shifted to water lithium resources involving salt-lake brines and sea water. Among various aqueous recovery technologies, the lithium ion-sieve (LIS) technology is considered the most promising one. This is because LISs are excellent adsorbents with high lithium uptake capacity, superior lithium selectivity and good cycle performance. These attributes have enabled LISs to separate lithium effectively from aqueous solutions containing different ions. The present work reviews the latest development in LIS technology, including the chemical structures of ion-Sieves, the corresponding lithium adsorption/desorption mechanisms, the ion-Sieves preparation methods, and the challenges associated with the lithium recovery from aqueous solutions by the LIS batteries. Besides, some common LIS composite materials forming technologies, including granulation, foaming, membrane and fiber formation, and magnetization, which are used to overcome the shortcomings in industrial column operations, are also explored.
