Oily sludge produced in the process of petroleum exploitation and utilization is a kind of hazardous waste that needs to be urgently dealt with in the petrochemical industry. The oil content of oily sludge is generally between 15–50% and has a great potential for oil resource utilization. However, its composition is complex, in which asphaltene is of high viscosity and difficult to separate. In this study, The oily sludge was extracted with toluene as solvent, supplemented by three kinds of ionic liquids (1-ethyl-3-methylimidazole tetrafluoroborate ([EMIM] [BF4]), 1-ethyl-3-methylimidazole trifluoro-acetate ([EMIM] [TA]), 1-ethyl-3-methylimidazole Dicyandiamide ([EMIM] [N(CN)2])) and three kinds of deep eutectic solutions (choline chloride/urea (ChCl/U), choline chloride / ethylene glycol (ChCl/EG), and choline chloride/malonic acid (ChCl/MA)). This experiment investigates the effect of physicochemical properties of the solvents on oil recovery and three machine learning methods (ridge regression, multilayer perceptron, and support vector regression) are used to predict the association between them. Depending on the linear correlation of variables, it is found that the conductivity of ionic liquid is the key characteristic affecting the extraction treatment in this system.
Evaluation and Prediction on the Effect of Ionic Properties of Solvent Extraction Performance of Oily Sludge Using Machine Learning