• Macromolecular Research
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• 제17권11호
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• pp.829-841
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• 2009

This study introduces a specified group-contribution method for predicting the phase equilibria in polymer solutions. The method is based on a modified double lattice model developed previously. The proposed model includes a combinatorial energy contribution that is responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, a polar force and specific energy contribution. Using the group-interaction parameters obtained from data reduction, the solvent activities for a large variety of mixtures of polymers and solvents over a wide range of temperatures can be predicted with good accuracy. This method is simple but provides improved predictions compared to those of the other group contribution methods.

• Macromolecular Research
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• 제16권4호
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• pp.320-328
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• 2008

We established a thermodynamic framework of group contribution method based on modified double lattice (MDL) model. The proposed model included the long-range interaction contribution caused by the Coulomb electrostatic forces, the middle-range interaction contribution from the indirect effects of the charge interactions and the short-range interaction from modified double lattice model. The group contribution method explained the combinatorial energy contribution responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, the polar force, and the specific energy contribution from hydrogen bonding. We showed the solvent activities of various polymer solution systems in comparison with theoretical predictions based on experimental data. The proposed model gave a very good agreement with the experimental data.

• 응용통계연구
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• 제25권4호
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• pp.589-603
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• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• 설비공학논문집
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• 제11권1호
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• pp.125-132
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• 1999

Studies are being done to replace conventional refrigerants with alternatives that have low or no ozone depletion and greenhouse warming Potentials, yet possess appropriate pro perties for a refrigeration cycle. To achieve this goal, a consistent set of thermodynamic properties of the working fluid is required. A common problem with the possible alternative refrigerants is that sufficient experimental data do not exist, thus making it difficult to develp complete equations of state that can predict properties in all regions including the vapor-liquid equilibrium. One solution is the use of the generalized equation of state correlations that can predict thermodynamic properties with a minimum number of characteristic parameters. Characteristic parameters required for the generalized equation of state are, in general, critical temperature, critical pressure, critical volume and normal boiling temperature. In this study, estimation of these characteristic parameters of refrigerants by group contribution method is developed.

• 천문학회보
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• 제42권2호
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• pp.61.4-62
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• 2017

In this study we have made a statistical investigation on the kinematic classification of coronal mass ejections (CMEs) using about 4,000 SOHO/LASCO CMEs from 1996 to 2015. For this we use their SOHO/LASCO C3 data and exclude all poor events. Using the constant acceleration model, we classify these CMEs into three groups: Acceleration group, Constant Velocity group, and Deceleration group. For classification we adopt four different methods: Acceleration method, Velocity Variation method, Height Contribution method, and Visual Inspection method. Our major results are as follows. First, the fractions of three groups depend on the method used. Second, the results of the Height Contribution method are most consistent with those of the Visual Inspection method, which is thought to be most promising. Third, the fractions of different kinematic groups for the Height contribution method are: Acceleration (35%), Constant speed (47%), and Deceleration (18%). Fourth, the fraction strongly depend on CME speed; the fraction of Acceleration decreases from 0.6 to 0.05 with CME speed; the fraction of Constant increases from 0.3 to 0.7; the fraction of Deceleration increases from 0.1 to 0.3. Finally we present dozens of CMEs with non-constant accelerations. It is found that about 40 % of these CMEs show quasi-periodic oscillations.

• 한국안전학회지
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• 제36권1호
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• pp.1-8
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• 2021

To simulate a process model in the field of chemical engineering, it is very important to identify the physical properties of novel materials as well as existing materials. However, it is difficult to measure the physical properties throughout a set of experiments due to the potential risk and cost. To address this, this study aims to develop a property prediction model based on the group contribution method for aromatic chemical compounds including benzene rings. The benzene rings of aromatic materials have a significant impact on their physical properties. To establish the prediction model, 42 important functional groups that determine the physical properties are considered, and the total numbers of functional groups on 147 aromatic chemical compounds are counted to prepare a dataset. Support vector regression is employed to prepare a prediction model to handle sparse and high-dimensional data. To verify the efficacy of this study, the results of this study are compared with those of previous studies. Despite the different datasets in the previous studies, the comparison indicated the enhanced performance in this study. Moreover, there are few reports on predicting the physical properties of aromatic compounds. This study can provide an effective method to estimate the physical properties of unknown chemical compounds and contribute toward reducing the experimental efforts for measuring physical properties.

• 한국물환경학회지
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• 제35권6호
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• pp.497-509
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• 2019

After the Total Maximum Daily Loads(TMDLs) was applied, it became beyond the limit of concentration management. However, it does not adequately reflect the characteristics of various watersheds, and causes problems with local governments because of the standard flow set. Thus, in this study, the Han River system is organized into four groups in estimating the Pollution Contribution by applying the Flow Duration Curve(FDC) created by the daily flow of data from the HSPF. And the method of this study is expected to be valuable as basic data for the TMDLs. As a result, Group I contains the main watersheds with no large hydraulic structures and tributary watersheds. There is no specificity in the FDC and the Pollution Contribution is estimated as rainfall runoff. Group II contains watersheds near the city where the FDC is maintained above a certain level during the Low Flow Conditions and the Pollution Contribution is estimated as the discharge flow of large scale point pollution facilities. Group III contains the main watersheds in which the large hydraulic structures are installed and FDC is curved in the Low Flow Conditions. So the Pollution Contribution is estimated as the water quality of the large hydraulic structures. Group IV contains the upstream in mainstream watersheds in which the large hydraulic structures are installed and the FDC is disabled before the Low Flow Conditions. As the flow is concentrated in the High Flow Conditions, the non-point pollution sources are estimated as the Pollution Contribution.

• Asia pacific journal of information systems
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• 제22권3호
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• pp.53-74
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• 2012

A challenge in fostering virtual communities is the continuous supply of knowledge, namely members' willingness to contribute knowledge to their communities. Previous research argues that giving away knowledge eventually causes the possessors of that knowledge to lose their unique value to others, benefiting all except the contributor. Furthermore, communication within virtual communities involves a large number of participants with different social backgrounds and perspectives. The establishment of mutual understanding to comprehend conversations and foster knowledge contribution in virtual communities is inevitably more difficult than face-to-face communication in a small group. In spite of these arguments, evidence suggests that individuals in virtual communities do engage in social behaviors such as knowledge contribution. It is important to understand why individuals provide their valuable knowledge to other community members without a guarantee of returns. In virtual communities, knowledge is inherently rooted in individual members' experiences and expertise. This personal nature of knowledge requires social interactions between virtual community members for knowledge transfer. This study employs the social capital theory in order to account for interpersonal relationship factors and identity theory for individual and group factors that may affect knowledge contribution. First, social capital is the relationship capital which is embedded within the relationships among the participants in a network and available for use when it is needed. Social capital is a productive resource, facilitating individuals' actions for attainment. Nahapiet and Ghoshal (1997) identify three dimensions of social capital and explain theoretically how these dimensions affect the exchange of knowledge. Thus, social capital would be relevant to knowledge contribution in virtual communities. Second, existing research has addressed the importance of identity in facilitating knowledge contribution in a virtual context. Identity in virtual communities has been described as playing a vital role in the establishment of personal reputations and in the recognition of others. For instance, reputation systems that rate participants in terms of the quality of their contributions provide a readily available inventory of experts to knowledge seekers. Despite the growing interest in identities, however, there is little empirical research about how identities in the communities influence knowledge contribution. Therefore, the goal of this study is to better understand knowledge contribution by examining the roles of social capital and identity in virtual communities. Based on a theoretical framework of social capital and identity theory, we develop and test a theoretical model and evaluate our hypotheses. Specifically, we propose three variables such as cohesiveness, reciprocity, and commitment, referring to the social capital theory, as antecedents of knowledge contribution in virtual communities. We further posit that members with a strong identity (self-presentation and group identification) contribute more knowledge to virtual communities. We conducted a field study in order to validate our research model. We collected data from 192 members of virtual communities and used the PLS method to analyse the data. The tests of the measurement model confirm that our data set has appropriate discriminant and convergent validity. The results of testing the structural model show that cohesion, reciprocity, and self-presentation significantly influence knowledge contribution, while commitment and group identification do not significantly influence knowledge contribution. Our findings on cohesion and reciprocity are consistent with the previous literature. Contrary to our expectations, commitment did not significantly affect knowledge contribution in virtual communities. This result may be due to the fact that knowledge contribution was voluntary in the virtual communities in our sample. Another plausible explanation for this result may be the self-selection bias for the survey respondents, who are more likely to contribute their knowledge to virtual communities. The relationship between self-presentation and knowledge contribution was found to be significant in virtual communities, supporting the results of prior literature. Group identification did not significantly affect knowledge contribution in this study, inconsistent with the wealth of research that identifies group identification as an important factor for knowledge sharing. This conflicting result calls for future research that examines the role of group identification in knowledge contribution in virtual communities. This study makes a contribution to theory development in the area of knowledge management in general and virtual communities in particular. For practice, the results of this study identify the circumstances under which individual factors would be effective for motivating knowledge contribution to virtual communities.

• 청정기술
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• 제11권3호
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• pp.117-122
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• 2005

N-vinyl caprolactam (NVCL)은 초임계 분산 중합에 사용될 수 있는 비닐 아마이드 계열의 단량체이다. 이때 NVCL을 초임계 이산화탄소 상에서 분산 중합하기 위해서는 중합 초기 단계에 단량체가 $CO_2$에 모두 용해되어야 한다. 또한 최종 고분자로부터 미반응 단량체를 제거하기 위해서 중합 용매인 $CO_2$와 단량체 NVCL의 상거동 자료가 필수적이다. 하지만 단량체의 순수 물성은 측정 시 중합의 가능성이 있기 때문에 실험적인 제약이 있다. 본 연구에서는 이러한 단량체인 NVCL과 이와 유사한 작용기를 갖고 있는 N-methyl caprolactam (NMCL)의 순수 물성을 보다 정확하게 얻기 위하여 기존에 알려져 있는 그룹기여 방법을 수정하였다. 이때 유사한 구조를 갖는 ${\varepsilon}$-caprolactam을 바탕으로 새롭게 그룹 기여값을 얻고, 이를 NVCL, NMCL에 적용하였다. 또한 새롭게 수정된 순수 물성을 바탕으로 $CO_2$ + N-vinyl caprolactam과 N-methyl caprolactam 계에 대해 Peng-Robinson EOS와 van der Waals 1-fluid mixing rule을 이용하여 계산하고 실험값과 비교하였다.

• 청정기술
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• 제11권3호
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• pp.147-152
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• 2005

초임계 이산화탄소에서의 이부프로펜의 용해도를 측정하였다. 이부프로펜의 용해도는 variable volume view cell이 장착되어있는 고압장치를 사용하여 cloud point를 관찰하는 방법으로 실험하였다. 온도의 범위는 35, 40, $45^{\circ}C$로 하였고 측정된 실험값은 Peng-Robinson equation of state로 모델링 하였다. 이부프로펜의 물리적 특성은 Lydersen, Ambrose 그리고 Joback의 group contribution method를 사용하여 구한 값을 사용하였고, 각각의 결과를 비교하였다. 이산화탄소는 이부프로펜에 대해 큰 용해력을 가진다는 것을 확인하였고, 150bar 부근에서 retrograde 현상이 일어남을 알 수 있었다. 또한 Lydersen의 group contribution method를 사용하여 얻어진 이부프로펜의 물리적 특성으로 PR-EOS와 함께 모델링 한 결과가 다른 method보다 실험값과 더 잘 일치함을 확인하였다. 결과적으로 이 실험을 통해 초임계 공정으로 이부프로펜의 미세입자화 시킬 경우 용질이 용매에 용해되어야 하는 rapid expansion of supercritical solution (RESS)방법이 적용될 수 있음을 확인하였다.