Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band

Abstract

The accurate monitoring of oil spills is crucial for effective oil spill recovery, volume determination, and cleanup. Oil slicks become emulsified under the effects of wind and waves, which increases the consistency of the oil spills. This phenomenon makes oil spills more challenging to handle and exacerbates environmental pollution. In this study, the variation of the solar-blind ultraviolet (UV) fluorescence spectra obtained from simulated oil spills with different oil types and oil–water ratios was investigated. By designing and constructing a multi-angle excitation and detection system, an apparent fluorescence peak of the oil emulsions was observed at around 290 nm under 220 nm excitation. By utilizing competitive adaptive reweighted sampling (CARS) and multi-output neural network algorithms, both the types and concentrations of the emulsified oils were obtained simultaneously. The classification accuracy for identifying the oil type exceeds 98%, and the mean absolute percentage error (MAPE) for concentration regression is around 2%. The results indicate that active solar-blind UV fluorescence could become a supplementary method for on-site oil spill detection to achieve comprehensive monitoring of oil spills. This study provides potential applications for UV-induced fluorescence spectrometry in oil spill on-site monitoring during the daytime.