Optimization of parameters for models based in group contribution concept applied to calculation of viscosity for non-ideal mixtures

• Grant number: 15/16781-7
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): April 01, 2016
• Effective date (End): September 30, 2018
• Field of knowledge: Agronomical Sciences - Food Science and Technology - Food Engineering
• Principal researcher: Cintia Bernardo Gonçalves
• Grantee: Camila Nardi Pinto
• Home Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil

Abstract

The determination of viscosity transporting property of essential oils, vegetable oils and biodiesel is mandatory for the chemical, pharmaceutical, petroleum and food industries. Over the past decades many mathematical models have been developed to predict the viscosity of mixtures, among them we can highlight the UNIFAC-VISCO and GC-UNIMOD models. These two models based on the theory of group contribution rely on the determination of group interaction parameters to have a viable application. Studies conducted in SLE (Separations Engineering Laboratory - FZEA / USP) has shown that the interaction parameters already determined, for the two models, predict unsatisfactorily viscosity non-ideal mixtures. Two of the factors for the low predictive ability of existing interaction parameters are: low numbers of data used in the optimization and the limited number of components. In order to address this deficiency proposes to build a comprehensive database, large and robust, for new optimization of interaction parameters for UNIFAC-VISCO and GC-UNIMOD models using genetic algorithm. Additionaly, it is proposed to use an artificial neural network for determining the viscosity of non-ideal mixtures based on the concept of molecular composition. The database will be developed in SQL, using as database manager MySQL. MATLAB is used as the programming language due to its ease of handling arrays and provide graphical user interface. The effectiveness of process optimization will be assessed by the average deviation between the calculated data and experimental data. The results of this project should contribute to the development of processes and projecting equipment in industries that use as raw materials essential oils, vegetable oils and biodiesel.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PINTO, CAMILA N.; FRUGOLI, GABRIEL Z.; FLORIDO, PRISCILA M.; ATZINGEN, GUSTAVO V.; RODRIGUES, CHRISTIANNE E. C.; GONCALVES, CINTIA B. Viscosities and Densities of Fatty Alcohol Mixtures from 298.15 to 338.15 K: Estimation by Kay's Rule and Prediction by the UNIFAC-VISCO and GC-UNIMOD Group Contribution Methods. JOURNAL OF CHEMICAL AND ENGINEERING DATA, v. 64, n. 5, SI, p. 1937-1947, MAY 2019. Web of Science Citations: 0.

FLORIDO, PRISCILA M.; LOBO, DEBORAH P. S.; PINTO, CAMILA N.; RODRIGUES, CHRISTIANNE E. C.; GONCALVES, CINTIA B. Physical properties of systems of interest to the edible oil industry: Viscosities and densities of model systems formed by (triacylglycerol plus fatty acid plus solvent). JOURNAL OF CHEMICAL THERMODYNAMICS, v. 113, p. 198-212, OCT 2017. Web of Science Citations: 0.

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

PINTO, Camila Nardi. Optimization of parameters for models based on the concept of group contribution applied to the calculation of viscosity of non-ideal mixtures. 2019. Doctoral Thesis - Universidade de São Paulo (USP). Faculdade de Zootecnica e Engenharia de Alimentos (FZE/BT) Pirassununga.