Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Lize M. S. L. Oliveira is active.

Publication


Featured researches published by Lize M. S. L. Oliveira.


Talanta | 2015

Prediction of the distillation temperatures of crude oils using 1H NMR and support vector regression with estimated confidence intervals

Paulo R. Filgueiras; Luciana A. Terra; Eustáquio V.R. Castro; Lize M. S. L. Oliveira; Júlio C.M. Dias; Ronei J. Poppi

This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS.


Animal Reproduction Science | 2012

Spermatogenesis in goats with and without scrotum bipartition

A.A.N. Machado Júnior; Lize M. S. L. Oliveira; A.C. Assis Neto; Flávio Ribeiro Alves; Maria Angélica Miglino; Maria Acelina Martins de Carvalho

The objective of the present research was to quantify the seminiferous epithelium cells, spermatogenesis efficiency and characterize the ultrastrucure of Sertoli cells in goats. Eighteen goats were used and divided into three groups: Group I - goats without bipartition of the scrotum; Group II - animals with bipartition of the scrotum in up to 50% of the testicular length; Group III - goats with bipartition of the scrotum in more than 50% of the testicular length. The goat testes in Group III had a greater number of primary spermatocytes (25.37 ± 4.55 cells per cross sections), spermatids (112 ± 15.12 cells per cross sections), and Sertoli cells (9.46 ± 1.74 cells per cross sections) than the animals in Groups I and II (P<0.05). The spermatogenic mitotic, meiotic, and general efficiency were greater in animals in Group III (1.25 ± 0.28; 5.12 ± 1.63; 6.44 ± 1.96) when compared to those in Groups I and II. Sheet-like processes originated from the Sertoli cell body as simple and smooth structures which involved almost all the surface of germ cells. Slender cord-like processes originated from Sertoli cells and also from the sheet-like processes. The relative frequency of the cycle stages showed differences among the groups of goats studied, and the highest frequency was in Stage 3 (20.68% for goats in Group I, 21.15% for those in Group II, and 16.89% for the animals in Group III). In conclusion, goats with bipartition of the scrotum have a greater number of germ and Sertoli cells per cross section of seminiferous tubule, that indicated a greater sperm production when compared to the other groups, and the ultrastructure of the Sertoli cell process did not present any relationship with bipartition of the scrotum.


Energy & Fuels | 2016

Determination of Saturates, Aromatics, and Polars in Crude Oil by 13C NMR and Support Vector Regression with Variable Selection by Genetic Algorithm

Paulo R. Filgueiras; Natália A. Portela; Samantha R.C. Silva; Eustáquio V.R. Castro; Lize M. S. L. Oliveira; Júlio C.M. Dias; Álvaro Cunha Neto; Wanderson Romão; Ronei J. Poppi


Fuel | 2016

Determination of some physicochemical properties in Brazilian crude oil by 1H NMR spectroscopy associated to chemometric approach

Lucas M. Duarte; Paulo R. Filgueiras; Samantha R.C. Silva; Júlio C.M. Dias; Lize M. S. L. Oliveira; Eustáquio V.R. Castro; Marcone Augusto Leal de Oliveira


Energy & Fuels | 2016

Sulfur Determination in Brazilian Petroleum Fractions by Mid-infrared and Near-infrared Spectroscopy and Partial Least Squares Associated with Variable Selection Methods

Julia Rocha; Lize M. S. L. Oliveira; Júlio C.M. Dias; Ulysses B. Pinto; Maria de Lourdes S. P. Marques; Betina P. Oliveira; Paulo R. Filgueiras; Eustáquio V.R. Castro; Marcone A. L. de Oliveira


Fuel | 2015

Laser desorption ionization FT-ICR mass spectrometry and CARSPLS for predicting basic nitrogen and aromatics contents in crude oils

Luciana A. Terra; Paulo R. Filgueiras; Lilian V. Tose; Wanderson Romão; Eustáquio V.R. Castro; Lize M. S. L. Oliveira; Júlio C.M. Dias; Boniek G. Vaz; Ronei J. Poppi


Fuel Processing Technology | 2016

Evaluation of the correlation between wax type and structure/behavior of the pour point depressant

Lize M. S. L. Oliveira; Rita de Cassia P. Nunes; Isis C. Melo; Ygor L. L. Ribeiro; Leidiane G. Reis; Júlio C.M. Dias; Regina C. L. Guimarães; Elizabete F. Lucas


Microchemical Journal | 2018

Analytical advanced techniques in the molecular-level characterization of Brazilian crude oils

Gabriela Vanini; Vinícius B. Pereira; Wanderson Romão; Alexandre O. Gomes; Lize M. S. L. Oliveira; Júlio C.M. Dias; Débora A. Azevedo


Energy & Fuels | 2017

Study of Asphaltene Precipitation in Crude Oils at Desalter Conditions by Near-Infrared Spectroscopy

Denisson Santos; Elvio Barreto de Melo Filho; Raul S. Dourado; Monique Amaral; Sofia Filipakis; Lize M. S. L. Oliveira; Regina C. L. Guimarães; Alexandre F. Santos; Gustavo R. Borges; Elton Franceschi; Cláudio Dariva


Fuel | 2018

Comprehensive and multidimensional tools for crude oil property prediction and petrochemical industry refinery inferences

Daniella L. Vale; Paula Fernandes de Aguiar; Lize M. S. L. Oliveira; Gabriela Vanini; Vinícius B. Pereira; Larissa O. Alexandre; Giovani S.C. da Silva; Luiz André N. Mendes; Alexandre O. Gomes; Débora A. Azevedo

Collaboration


Dive into the Lize M. S. L. Oliveira's collaboration.

Top Co-Authors

Avatar

Eustáquio V.R. Castro

Universidade Federal do Espírito Santo

View shared research outputs
Top Co-Authors

Avatar

Paulo R. Filgueiras

Universidade Federal do Espírito Santo

View shared research outputs
Top Co-Authors

Avatar

Ronei J. Poppi

State University of Campinas

View shared research outputs
Top Co-Authors

Avatar

Wanderson Romão

Universidade Federal do Espírito Santo

View shared research outputs
Top Co-Authors

Avatar

Débora A. Azevedo

Federal University of Rio de Janeiro

View shared research outputs
Top Co-Authors

Avatar

Gabriela Vanini

Federal University of Rio de Janeiro

View shared research outputs
Top Co-Authors

Avatar

Lucas M. Duarte

Universidade Federal de Juiz de Fora

View shared research outputs
Top Co-Authors

Avatar

Luciana A. Terra

State University of Campinas

View shared research outputs
Researchain Logo
Decentralizing Knowledge