Online Monitoring of Oils in Wastewater Using Combined Ultraviolet Fluorescence and Light Scattering with an Artificial Neural Network

Abstract

SB EXECUTIVE SUMMARYUltraviolet (UV) fluorescence and light scattering are two analytical methods commonly used in instrumentation for online measurement of oils in water. UV fluorescence-based instruments detect both dissolved and emulsified aromatic constituents of oils. Light-scattering-based sensors measure optical scattering induced by emulsified oil droplets. A major technical challenge for each method is to maintain quantitative accuracy in the presence of chemical and physical interferences, including fluorescent organic compounds (e.g., detergents and natural organic matter), suspended solid particles, dissolved salts, etc. To address this issue, we have been developing a new monitoring system that simultaneously combines both UV fluorescence and light scattering spectroscopy. Four major types of oils (lube oils 2190 and 9250, diesel fuel marine [DFM], and JP5), each of which had a dozen subtypes of oil samples, were examined to obtain the intensity of both fluorescence and scattering as a function of oil, detergent (Mil-D and Tide®), and seawater concentrations. Both fluorescence and light scattering intensities varied significantly with oil types and subtypes. Both Mil-D and Tide greatly influenced the fluorescence and scattering of oil samples.The tremendous variations in fluorescence and scattering intensity with oil types and subtypes, detergents, and seawater make it difficult to calibrate the analytical instrument using traditional ' methods; hence, we have implemented a multivariate, nonlinear calibration of instrumental response through an artificial neural network. We have demonstrated that the simultaneous, combined use of fluorescence and scattering data significantly improves quantitative prediction accuracy. The trained backpropagation neural network was used successfully to predict concentrations of single oils and their mixtures, even in the presence of detergents and seawater, and appears well suited for calibrations of an online oil content monitor. The trained network processes information very quickly and is appropriate for real-time applications. The newly developed technique permits the online monitoring of oil spills, the accurate determination of oil concentrations in wastewater discharged from ships.

Adsorption of BTX and PAHs by Polymeric Resins Packed in a Fixed Bed: Semi-Industrial Evaluation

“…The organic phase was added to a quartz cuvette with four polished faces and analyzed under an excitation wavelength of 350 nm and emission wavelength of 440 nm in a Varian Cary Eclipse fluorescence spectrophotometer. The values were determined by using a calibration curve obtained previously [18].…”

Application of neural networks in online oil content monitors

“…Laser light scattering measurements were performed for each sample using a LISST-100 particle size analyzer (Sequoia Scientific, Inc., Mercer Island, WA). The laboratory setup and measurement procedures have been detailed in a previous report [3].…”