Rapid in situ determination of total oil concentration in water using ultraviolet fluorescence and light scattering coupled with artificial neural networks Original Research Article

Real-time measurement of total oil concentration in complex samples is required in wastewater discharge streams from ships and processing industries. A novel technology has been developed for the accurate quantification of a variety of single oils and their mixtures. Four major types of oils (lube oils 2190 and 9250, diesel fuel marine (DFM), and jet fuel (JP5)), each of which consisted of a dozen subtypes of oil samples, were examined to obtain both fluorescence and light scattering spectra as a function of concentration of single oils and mixtures. Tremendous variations in both fluorescence and scattering were observed among oil types, subtypes, and mixtures. The spectral response of an oil mixture was not the simple summation of respective single oils. To account for all these variations, a multivariate, nonlinear calibration method is applied to associate instrumental responses with oil concentrations using artificial neural networks (ANNs). The neural network architecture has been established by optimizing network parameters such as epochs, the number of neurons in the hidden layer, and learning rates in order to achieve the maximum accuracy of oil concentration measurements. It is demonstrated that the simultaneous, combined use of fluorescence and light scattering significantly improves the accuracy of measurement for oil samples. The newly developed technique permits the reliable, real-time determination of the total concentration of various oils and mixtures in water.