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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Phase Equilibrium Involving Xylitol, Water, and Ethylene Glycol or 1,2-Propylene Glycol: Experimental Data, Activity Coefficient Modeling, and Prediction with Artificial Neural Network-Molecular Descriptors

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Author(s):
Machado, Patricia G. [1] ; Galvao, Alessandro C. [1] ; Robazza, Weber S. [1] ; Arce, Pedro F. [2] ; Hochscheidt, Bruna E. [1]
Total Authors: 5
Affiliation:
[1] Santa Catarina State Univ UDESC, Lab ApTher Appl Thermophys, Dept Food & Chem Engn, BR-89870000 Pinhalzinho, SC - Brazil
[2] Univ Sao Paulo, Engn Sch Lorena, Dept Chem Engn, BR-12600970 Lorena, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Industrial & Engineering Chemistry Research; v. 57, n. 31, p. 10675-10683, AUG 8 2018.
Web of Science Citations: 3
Abstract

The xylitol molecule is an important building block that can be used in the production of such interesting chemicals as ethylene glycol and 1,2-propylene glycol. The development of productive processes that enable this transformation depends on various experimental and theoretical information. In order to supply part of this demand, this work sought to study the solubility of xylitol in binary liquid solutions formed by water, ethylene glycol, and 1,2-propylene glycol in the temperature range between 293.15 and 323.15 K, covering the entire molar composition range of the solution. The Jouyban-Acree, NRTL, and UNIQUAC models were used in the correlation of experimental data, and the mUNIFAC model was applied in the prediction of experimental data. In addition, an artificial neural network associated with molecular descriptors was developed to simulate the data. Xylitol showed solubility in the pure components with decreasing values in the following order: water, ethylene glycol, and 1,2-propylene glycol. The solubility in binary solutions had intermediate values according to the intermediate concentration values. The models used proved capable of correlating or predicting the experimental data. The artificial neural networks had a satisfactory performance in the data simulation, and the best observed architecture used four layers of the type 7-3-3-1. (AU)

FAPESP's process: 15/05155-8 - Modeling of the thermophysical properties of pure fluids (ionic liquids and components present in natural products) and thermodynamic behaviour of the phase equilibrium of mixtures
Grantee:Pedro Felipe Arce Castillo
Support Opportunities: Regular Research Grants