Evaluation of physicochemical parameters as indicators of diesel adulteration.

Diesel adulteration not only reduces engine performance and lifespan but also has a stiffening effect on the economy. Therefore, regulatory agencies and petroleum laboratories are constantly adopting various methods to ensure that commercial diesel is pure and of good quality. Despite the introduction of solvent tracer analysis as a reliable means of detecting adulteration, most laboratories still depend on the physicochemical parameters of diesel as an indicator of adulteration. This research aimed to document the feasibility of using quality parameters to detect diesel adulteration. Neat diesel samples were mixed with some common adulterants (kerosene, premix, and condensate) at varying concentrations. The quality of each admixture was analysed using the ERASPEC fuel analyser and physicochemical parameters including density, kinematic viscosity, cetane index, and flashpoint were recorded. A negative correlation was observed between adulteration and all quality parameters. At low levels of adulteration, physicochemical parameters were within the required range. However, diesel with adulterants above 20 % v/v had cetane index, density, and flashpoint values not conforming with quality standards. Kinematic viscosity of diesel remained within the required limits despite the levels of adulteration. Physicochemical parameters, though generally accepted as good indicators of fuel quality, were not reliable indicators of diesel adulteration, especially at low levels. At higher levels of adulteration, the type of adulterant present must be considered if physicochemical parameters are to be used to predict adulteration. However, it is recommended that physicochemical parameters be used in combination with other techniques to detect diesel adulteration.

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.