Fuel adulteration detection using electrodepositated polymer sensors and artificial neural networks.

Author(s)

Sérgio Tonzar Ristori Ozaki

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2010-06-11

Examining board members

Fernando Josepetti Fonseca; Emilio Del Moral Hernandez; Luiz Henrique Capparelli Mattoso

• Advisor: Fernando Josepetti Fonseca

Abstract

Fuel adulteration is a major concern in Brazil. The local governmental agency detects from 1 to 3% of problematic samples yearly, which is a lot considering Brazils market size. The myriad of adulteration possibilities is vast and it is very dynamic, thus array of sensors based on global selectivity concept seems to be more suitable methodology to detect problems in fuel. The global selectivity concept encompasses the cross-sensitivity of non-specific chemical sensors and the use of multivariated data analysis methods as a way to provide fingerprints for samples of different chemical composition. The chemical sensors can employ different types of sensoactive materials, whose electrical responses are dependent on the physicochemical characteristics of the media they get in contact with. Conducting polymers (CP) are per excellence suitable sensoactive materials, since their electrical conductivity is highly influenced by the environmental conditions and they can be easily processed in the thin film form by different techniques. In the present work films of poly(3-methylthiophene) (PMTh) and poly(3-hexylthiophene) (PHTh) are deposited by chronopotenciometry and chronoamperometry onto interdigitated microelectrodes and characterized through Impedance Spectroscopy. This data was analyzed with Multilayer perceptron neural networks and a very good performance is found in gasoline adulteration detection. A less great performance was also achieved in the investigation vehicular ethanol adulteration. (AU)

• FAPESP's process: 07/06027-7 - Fuel adulteration detection using electrodepositated film sensors and artificial neural networks
• Grantee: Sérgio Tonzar Ristori Ozaki
• Support Opportunities: Scholarships in Brazil - Master