000			02348nab a2200253 4500
005			20260520003105.0
008			260224s2013 xxu ing
041			_aInglés
245	0	0	_aGraph Machine Based-QSAR Approach for Modeling Thermodynamic Properties of Amines: Application to CO2 Capture in Postcombustion
260			_a _b _cene./feb. 2013
270			_a21/10/2013 ; 21/10/2013
300			_a17 p. ; 469-486
520			_aTranscripción del resumen del autor: Amine scrubbing is usually considered as the most efficient technology for CO2 mitigation through postcombustion Carbon Capture and Storage (CCS). However, optimization of the amine structure to improve the solvent properties requires to sample a large number of possible candidates and hence to gather a large amount of experimental data. In this context, the use of QSAR (Quantitative Structure Activity Relationship) statistical modeling is a powerful tool as it performs a mapping of a set of input vectors (i.e. the characteristics or the properties of the molecules under consideration) to a set of output vectors (i.e. their targeted properties). In this work, we used a high throughput screening experimental device to measure CO2 solubility data on a set of 46 amine aqueous solutions. Absorption isotherms are represented using a thermodynamic model based on two thermodynamic constants, pKa* and pKc* , accounting for the main chemical reactions occurring in the liquid phase between amine and CO2. Then, we used a statistical approach named Graph Machines at the same time to cluster the molecules and to model the variation of the acidity constant pKa* as a function of the molecular structure. The originality of our approach is the use of graphs to represent molecules in multidimensional spaces and simultaneously construct predictive models of their physicochemical properties based on these graphs. This approach is applied in this paper to predict the thermodynamic properties of a set of 5 new molecules.
581			_a1
773	0		_tOil and Gas Science and Technology _g68
942			_cARTICULO
100	1		_aPorcheron, F. _953530
100	1		_aJacquin, M. _953531
100	1		_aEl Hadri, N. _953532
100	1		_aSaldana, D.A. _953533
100	1		_aGoulon, A. _953534
100	1		_aFaraj, A. _912310
999			_c187933 _d187933