• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.257-265
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• 2020

Densities (ρ) for binary mixtures of ethanol (1) + diisopropyl ether (DIPE) or cyclohexane or alkane (C₆-C₉) (2) were measured at 298.15 K, 308.15 K and 318.15 K. The excess molar volume (V^E_m) of binary mixtures was calculated using ρ data and correlated with Redlich-Kister polynomial equation. The V^E_m values for binary mixtures of ethanol (1) + cyclohexane or n-alkane (C₆-C₉) (2) were positive, whereas for ethanol (1) + DIPE (2) these were negative. The magnitude of V^E_m values follows the order: cyclohexane > n-nonane > n-octane > n-heptane > n-hexane > DIPE. The V^E_m values have been interpreted qualitatively and also quantitatively in terms of Flory-Treszczanowicz-Benson (FTB) model and Prigogine-Flory-Patterson (PFP) theory. The values V^E_m predicted using FTB model agree well with experimental V^E_m values at all mole fractions. But the PFP theory describes well V^E_m data in ethanol-rich region (x₁ > 0.5) for all binary mixtures and is able to predict the sign of V^E_m vs x₁ curve for ethanol-lean region (x₁ < 0.5) except for ethanol (1) + nonane (2) mixtures.

• Journal of the Korean Chemical Society
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• v.37 no.6
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• pp.562-569
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• 1993

The pseudophase separation model is used to describe the effects of pH and n-propanol on the mixed micellar formation of protonated and unprotonated dimethyldodecylamine oxides. Dimethyl-dodecylamine oxide surfactant molecules may exist in aqueous solution in either nonionic (unprotonated) or cationic (protonated) form, and they can be modeled thermodynamically as a binary mixture of cationic and nonionic surfactants. The composition of the binary mixture is varied by adjusting the solution pH. And activities, micellar compositions, and monomeric compositions of two surfactant species can be calculated directly from the experimental titration data by applying pseudophase separation model to the micellar system of DDAO in water/n-propanol. The critical micellar concentrations and the p$K_a$ values of the binary mixture systems are dependent on the micellar composition of the protonated cationic surfactant (X); especially they show the minimum phenomena when they are plotted against the micellar composition of the protonated cationic surfactant (X). The critical micellar concentration of the binary mixed DDAO system is generally decreased when n-propanol is added to the binary mixture system, and the degree of decrease is dependent on the concentration of n-propanol.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.34-37
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• 2002

This paper presents a new stochastic binary neural network based on the Hopfield model. We apply the proposed network to TSP and compare it with other methods by computer simulations. Furthermore, we apply 2-opt to the proposed network to improve the performance.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.63-78
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• 1994

For discrimination with binary variables, reformulated full and first order Bahadur model with incomplete observations are presented. This allows prior probabilities associated with multiple population to be estimated for the sample-based classification rule. The EM algorithm is adopted to provided the maximum likelihood estimates of the parameters of interest. Some experiences with the models are evaluated and discussed.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1543-1550
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• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05b
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• pp.274-279
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• 1997

Breakthrough curves for separation of cesium and strontium binary components from aqueous solutions in fixed bed by chabazite and 13X were experimentally obtained and simulated the breakthrough curves for binary component adsorption. It is shown that agreements between model predictions and experimental data are relatively good although some deviations are observed. From engineering point of view, the simple model used here can be applied to simulate adsorption breakthrough curves satisfactorily.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.141-153
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• 2017

A binary integer programming model is proposed for a complex timetabling problem in a university faculty which conducts various degree programs. The decision variables are defined with fewer dimensions to economize the model size of large scale problems and to improve modeling efficiency. Binary matrices are used to incorporate the relationships between the courses and students, and the courses and teachers. The model includes generally applicable constraints such as completeness, uniqueness, and consecutiveness; and case specific constraints. The model was coded and solved using Open Solver which is an open-source optimizer available as an Excel add-in. The results indicate that complicated timetabling problems with large numbers of courses and student groups can be formulated more efficiently with fewer numbers of variables and constraints using the proposed modeling framework. The model could effectively generate timetables with a significantly lower number of work hours per week compared to currently used timetables. The model results indicate that the particular timetabling problem is bounded by the student overlaps, and both human and physical resource constraints are insignificant.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.4
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• pp.1437-1448
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• 1996

This paper presents a simplified model for approximate analysis of the solute redistribution in coarsening dendrite arms during solidification of binary metal alloys. By introducing a quadratic concentration profile with a time-dependent coefficient, the integral equation for diffusion in the solid phase is reduced to a simple differential relation between the coefficient and the solid-liquid interface position. The solid fraction corresponding to the system temperature is readily determined from the relation, phase equilibrium and the overall solute balance in which the liquid phase is assumed to be completely mixed. In order to validate the developed model, calculations are performed for the directional solidification of Al-4.9 mass Cu alloy. The predicted eutectic fractions for a wide range of the cooling rate reasonably agree with data from the well-known experiment as well as sophisticated numerical analyses. Also, the results for the back diffusion limits are consistent with available references. Additional calculations show that the characteristic parameters such as the coarsening, density variation and nonlinarity in the phase diagram significantly affect the microsegregation. Owing to the simplicity, efficiency and compatibility, the present model may be suitable for the micro-macroscopic solidification model as a microscopic component.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.270-275
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• 2006

The cultivated pearls collected for the study were pretreated through the removal of contaminants and the surface bleaching for easy dyeing. Coloring of pearls are necessary after selecting dyes adsorbable to the Conchiolin layer, a kind of hard protein formed in the seawater, covering the surface of the pretreated pearls. Dyes adsorbable to the Conchiolin layers are mostly basic dyes such as Rhodamine 6G(R6G), Rhodamine B(RB), Methylene Blue(MB) etc. and the binary and ternary competitive adsorption were performed by mixing two or three dyes together. The multi-dye adsorption data were compared with the predictions from the ideal adsorbed solution theory(IASI) combined with the single-dye adsorption model, the Langmuir or the Redlich-Peterson(RP) model. The quality of prediction was compared by using determination coefficient($R^2$) and standard deviation(SSE) values. Predictions from the IAST were found to be in good agreement with the data for the R6G/RB binary adsorption to the pearl layers not fractionated with their size, except for the adsorption data for RB at high concentrations. Among the three binary adsorption systems, R6G/RB, R6G/MB, and MB/RB, only the RB sorption data in the R6G/RB binary system was in poor agreement with the IAST prediction. Competitive adsorption data in ternay systems were in good agreement with the predictions from the IAST except for the RB data.