Automatic Creation of Missing Groups through Connectivity Index for Pure-Component Property Prediction

Gani, Rafiqul; Harper, Peter Mathias; Hostrup, Martin

doi:10.1021/ie0501881

Cited by 85 publications

(72 citation statements)

References 7 publications

(22 reference statements)

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“…In this case, the polymer repeat-unit structures are represented by atoms and atom-connectivity indices, as proposed by Gani et al (2005):…”

Section: The Atom-connectivity Index Methodsmentioning

confidence: 99%

“…At times there can also be a polymer repeat-unit structure that cannot be totally represented by groups when predicting its properties with a specific group contribution method. This is overcome by a mesoscale approach, where an atom-connectivity index method (Gani et al, 2005) is applied to determine the missing groups and predict their contributions automatically without the need for additional experimental data. This integration of the group and atom-connectivity index based models is called the group contribution plus (GC + ) model, as proposed by Satyanarayana et al (2007).…”

Section: Brazilian Journal Of Chemical Engineeringmentioning

confidence: 99%

“…This approach for property prediction resulted from the integration of the group contribution and the atom-connectivity index methods developed by Gani et al (2005), with emphasis on properties of organic chemicals. They have been further extended to predict homo-polymer repeat unit properties using the Marrero-Gani group contribution method and the atom-connectivity index method (Sataynarayana et al (2007)).…”

Section: Background the Group Contribution Plus (Gc + ) Modelmentioning

confidence: 99%

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Computer aided polymer design using multi-scale modelling

Satyanarayana¹,

Abildskov²,

Gani³

et al. 2010

Braz. J. Chem. Eng.

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-The ability to predict the key physical and chemical properties of polymeric materials from their repeat-unit structure and chain-length architecture prior to synthesis is of great value for the design of polymerbased chemical products, with new functionalities and improved performance. Computer aided molecular design (CAMD) methods can expedite the design process by establishing input-output relations between the type and number of functional groups in a polymer repeat unit and the desired macroscopic properties. A multi-scale model-based approach that combines a CAMD technique based on group contribution plus models for predicting polymer repeat unit properties with atomistic simulations for providing first-principles arrangements of the repeat units and for predictions of physical properties of the chosen candidate polymer structures, has been developed and tested for design of polymers with desired properties. A case study is used to highlight the main features of this multi-scale model-based approach for the design of a polymer-based product.

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“…In this case, the polymer repeat-unit structures are represented by atoms and atom-connectivity indices, as proposed by Gani et al (2005):…”

Section: The Atom-connectivity Index Methodsmentioning

confidence: 99%

Section: Brazilian Journal Of Chemical Engineeringmentioning

confidence: 99%

Section: Background the Group Contribution Plus (Gc + ) Modelmentioning

confidence: 99%

See 1 more Smart Citation

Computer aided polymer design using multi-scale modelling

Satyanarayana¹,

Abildskov²,

Gani³

et al. 2010

Braz. J. Chem. Eng.

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show abstract

“…The group-contribution (GC) approach is a relatively recent concept [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] which originally aimed at predicting the physical properties of pure molecules starting from their chemical structures. The application of the GC concept to mixtures is actually an extension of the GC concept for single molecules [3,18].…”

Section: General Aspectsmentioning

confidence: 99%

The thermodynamics of alcohols-hydrocarbons mixtures

Privat

Jaubert

Molière³

2013

MATEC Web of Conferences

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Alcohols/hydrocarbons blends represent important products in the industry and remarkable systems from the thermodynamic standpoint, especially in presence of traces of water. In automotive applications, ethanol is incorporated in increasing proportions into car fuel formulations for environmental and economic reasons.From a thermodynamic viewpoint, the strongly polar nature of ethanol versus the rather apolar character of hydrocarbons makes the study of such blends particularly interesting in terms of vapor pressure and miscibility behavior.A long term collaboration between GE EnergyEurope (Belfort, France) and the Thermodynamics Team of the LRGP laboratory (Nancy, France) has been conducted to improve the knowledge of these systems, using the UNIFAC thermodynamic theory. First, blends of anhydrous ethanol and naphtha class hydrocarbons have been studied in terms of volatility: a strong liquid/vapor non-ideality effect has been put in evidence and numerically characterized. In a further step, blends of hydrated ethanol and hydrocarbons featuring diverse compositions have been the matter of a thermodynamic modeling that confirmed the paramount role played by the moisture content of ethanol on the miscibility, using the Maximum Miscibility Temperature ("TMM") concept; this study also pointed out the non-negligible influence of the PONA data and sulfur species of the hydrocarbon cut. Later on, other alcohols namely methanol, isopropanol and n-butanol, that may play an important role in future "green fuel" formulations, were included in this study that showed an interesting chain length effect.Recently, the team has started a study of the effect of biodiesel additions on the TMM of hydrated alcoholhydrocarbon mixtures.This paper summarizes the methodology and the results of the multi-step, collaborative modeling study developed in the field of ethanol/hydrocarbon thermodynamics.

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“…Each group contribution is typically assumed independent of the contributions of the other groups. Group Contribution Methods (GCMs) can account for the relative position of the groups in a molecule by introducing second and third order groups (cross -effects), use of different topological indexes (Gani et al, 2005;Gonzàlez et al, 2007;Wakeham et al, 2002) or by applying proximity parameters within the group contribution summation (Vijande et al, 2010). The methods by Marrero and Gani and Nannoolal et al (Marrero and Gani, 2001;Nannoolal et al, 2004) are recently published examples of GCMs for estimation of pure compound physical properties.…”

mentioning

confidence: 99%