Azeotropic mixture
New extractive configuration separating azeotropic mixture in semi-batch way
... the mixture to be separated ( ...the mixture to be separated is charged to the still, and the entrainer is fed continuously to the column ( ...the azeotropic composition and the entrainer in the ...
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Thermodynamic Insights on the Feasibility of Homogeneous Batch Extractive Distillation. 3. Azeotropic Mixtures with Light Boiling Entrainer
... azeotropic mixtures and low-relative-volatility mixtures, respec- tively, using a heavy entrainer in either batch rectifying or stripp- ing column ...an azeotropic mixture in which the boiling ...
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Thermodynamic Insights on the Feasibility of Homogeneous Batch Extractive Distillation. 3. Azeotropic Mixtures with Light Boiling Entrainer
... azeotropic mixtures and low-relative-volatility mixtures, respec- tively, using a heavy entrainer in either batch rectifying or stripp- ing column ...an azeotropic mixture in which the boiling ...
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A review of ternary azeotropic mixtures advanced separation strategies
... separating azeotropic and close boiling mixtures in pharmaceutical and chemical ...an azeotropic mixture A–B (with A having a lower boiling temperature than B), an entrainer E is added to interact ...
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A review of ternary azeotropic mixtures advanced separation strategies
... separating azeotropic and close boiling mixtures in pharmaceutical and chemical ...an azeotropic mixture A–B (with A having a lower boiling temperature than B), an entrainer E is added to interact ...
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Separation of n-hexane - ethyl acetate mixture by azeotropic batch distillation with heterogeneous entrainers
... the mixture n-hexane – ethyl acetate. This azeotropic mixture is often used in pharmaceutical industries for separating bioactive substances by liquid chromatographic ...this mixture by batch ...
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Adaptive Importance Sampling in General Mixture Classes
... To illustrate the results of the algorithm presented above, we consider a toy example in which the target density consists of a mixture of two multivariate Gaussian densities. The appeal of this example is that it ...
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Finite mixture models for exponential repeated data
... proposed mixture of exponential mixed models, the use of the EM algorithm which allows to take into account the incomplete structure of the data is considered (McLachlan and Krishnan ...a mixture of linear ...
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Asymptotic properties of mixture-of-experts models
... are linearly independent in the Hilbert space L 2 (µ). The key assumption (H-2) guarantees that the score functions are well defined. This assumption failed in the example of divergence of the LRTS. In the general case, ...
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Modal Trajectory Estimation using Maximum Gaussian Mixture
... Gaussian mixture filters, first devoted to minimum variance filtering, occurs in the MTE issue provided by replacing the Sum operator by a Max operator (section ...
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Approximating the marginal likelihood in mixture models
... April 15, 2008 Abstract In Chib (1995), a method for approximating marginal densities in a Bayesian setting is pro- posed, with one proeminent application being the estimation of the number of components in a normal ...
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Corrosion monitoring in mixture cement paste - Bentonite
... bentonite mixture but also (ii) the curing process of the ...the mixture contained 16% CEM III/C Rombas and 4% Bentonite with 80% Evian mineral ...bentonite mixture ran both in fully closed ...
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Segregation effects in processes handling powder mixture
... For storage or feeding applications two kinds of systems can be considered: lean phase flow or dense particle flows, corresponding respectively to industrial pneumatic conveying of par[r] ...
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On the relation between azeotropic behavior and minimum / maximum flash point occurrences in binary mixtures of flammable compounds
... In this article we explore that concomitance more systemati- cally. The paper is organized as follows: after recalling the flash- point prediction model (section 2 ), two T-dependent functions are derived relating both ...
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Adaptive Importance Sampling in General Mixture Classes
... 5 Conclusions The M-PMC algorithm provides a flexible and robust framework for adapting general importance sampling densities represented as mixtures. The extension to mixtures of t distribution broadens the scope of the ...
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Relabelling MCMC Algorithms in Bayesian Mixture Learning
... [3] H. Haario, E. Saksman, and J. Tamminen. An adaptive Metropolis algorithm. Bernoulli, 7:223– 242, 2001. [4] A. Jasra, C. C. Holmes, and D. A. Stephens. Markov Chain Monte Carlo methods and the label switching problem ...
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Model Selection for Mixture Models-Perspectives and Strategies
... the fundamental detailed balance property that ensures the correct stationary (posterior) distribution. The intuition behind the RJMCMC method is to create bijections between pairs of parameter spaces by creating ...
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A Mixture Approach to Bayesian Goodness of Fit
... as Instead of using a functional basis as in Verdinelli and Wasserman 1998, we represent as the infinite mixture of Beta distributions, Estimation within both parametric and nonparametri[r] ...
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Mixture models, latent variables and partitioned importance sampling
... the mixture model greatly reduces computational ...in mixture models, one based on a Rao-Blackwellization argument applied to a latent-variable-based ...
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Computational Solutions for Bayesian Inference in Mixture Models
... Gaussian mixture against G − 1 + dG + (d(d + 1)/2)G for a d-dimensional Gaussian mixture, 2 numerical integration cannot be considered as a viable ...
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