Estimation of the Contaminant Risk Level of Petroleum Residues Applying FDA Techniques

  • Miguel Flores Escuela Politécnica Nacional
  • Ana Escobar Escuela Politécnica Nacional
  • Luis Horna Escuela Politécnica Nacional
  • Lucia Carrión University of Technology, Sydney
Keywords: Quality Control, Generalized Linear Functional Model, Linear Regression, Classification


In the process of oil extraction, specifically in therefinement and industrialization of hydrocarbons, as is known,multiple wastes are highly polluting for the soil, water and air.In this work, the risk level of these wastes in affected areasis estimated thanks to the application of statistical models inthe field of functional data analysis. These models have beenimplemented in a statistical software called RStudio that allowsan early measurement and evaluation of the level of risk by usingsemiquantitative and quantitative methods. This measurement iscarried out by the staff of PETROECUADOR close to the affectedplace. It was used the laser-induced fluorescence technique (LIF).The data obtained using this technique was used to adjust thefollowing models: Generalized Functional Linear Model (MLFG),which makes it possible to classify the spectrum generated intwo pollution levels: Low and High. Functional linear regressionmodel with scalar response and functional explanatory variablewith the aim of directly estimating the percentage of contaminationlevel. With these results it is verified that the shape ofthe laser fluorescence spectrum is highly related to the gasolinecontent in the sample.



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Author Biography

Miguel Flores, Escuela Politécnica Nacional




Celander, K., Freddricsson, B. Galle, S. y Svanberg. (1988). Investigation of Laser-Induced Fluorescence with application to remote sensing ofenvironmental parameters, Goteborg Institute of Physics Reports GIPR-149.

González-Manteiga, W. y Vieu, P. (2007). Statistics for functional data. Computational Statistics and Data Analysis, 51, 4788-4792.

Li, J., Cuesta-Albertos, J. A., & Liu, R. Y. (2012). DD-classifier: Nonparametric classification procedure based on DD-plot. Journal ofthe American Statistical Association. Vol. 107, 737-753.

López Miranda Claudio y Cesar Augusto Romero Ramos, (2014).Propuesta de proyecto de estadística: un modelo de regresión lineal simple para pronosticar la concentración de co2 del volcán Mauna Loa.EPISTEMUS, 17:63-69.

Febrero-Bande, M. and Oviedo de la Fuente, M. (2012). Statistical computing in functional data analysis: The R package fda.usc. Journal of Statistical Software, 51(4):1-28.

Miguel Flores, Guido Saltos and Sergio Castillo-Paéz, (2016), Setting ageneralized functional liner model (GFLM) for classification of differenttypes of cancer, Latin American Journal Computing, 3 (2):41-48.

O’Neill, R.A., Buja-Bijunos, L., Rayner, D.M. (1980). Field Perfor-mance of laser flourosensor for detection of oil spills. Appl. Opt. 19,863.

Ramsay, J. O. and Silverman, B. W.2005. “Functional Data Analysis”,2nd ed., Springer-Verlag, New York, Pp. 147-325.

Ramírez John. Matemática. (2014), Regresión funcional mediante bases obtenidas por descomposición espectral del operador covarianza, Matemáticas, 12 (2):15-27.

R.H. Anderson, D.B. Farrar, S.R. Thoms, (2009). Application of discriminant analysis with clustered data to determine anthropogenic metals contamination. Elsevier, 408:50-56.

Muñoz Dania, Silva Francisco, Hernández Noslen, Bustamante Talavera.(2014). Functional Data Analysis as an Alternative for the Automatic Biometric Image Recognition: Iris Application. Computación y Sistemas, 18 (1):111-121

How to Cite
M. Flores, A. Escobar, L. Horna, and L. Carrión, “Estimation of the Contaminant Risk Level of Petroleum Residues Applying FDA Techniques”, LAJC, vol. 4, no. 2, pp. 13 - 18, Nov. 2017.
Research Articles for the Regular Issue