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To detect adulteration in gasoline, an automatic distillation apparatus was set up to measure the recovered volume and temperature simultaneously. The level metering was performed by online image processing instead of the conventional visual operator‐based measurement. To investigate the effect of additives in super gasoline, regular gasoline and diesel were added and the distillation curves were analyzed. The principal component analysis model was employed to reduce the obtained data. Finally, an artificial neural network was applied to predict the volume percentage of adulterants in super gasoline. Statistical analysis showed that the proposed model has a mean relative error and correlation coefficient of 4.6 % and 0.995, respectively.

The adsorption of anticancer drug daunorubicin from aqueous solution by carbonaceous adsorbents was investigated. The adsorption performances of graphene oxide (GO), activated carbon (AC), and multiwalled carbon nanotubes (MWCNTs) were compared. The effects of solution pH, contact time, and initial daunorubicin concentration on the removal of daunorubicin were explored. Isotherm and kinetic models were applied to predict the efficiency of the adsorbents. The major functional groups of the adsorbents were identified by FTIR spectroscopy. In alkaline medium, adsorption increases with increasing pH value. With increasing initial concentration, adsorption capacity increases and removal efficiency decreases. Kinetic studies showed that the pseudo‐second‐order model describes adsorption onto GO well, whereas the intraparticle diffusion model gives the best fits for AC and MWCNTs. The adsorption isotherm fits the Langmuir model very well.