Viscosity of aqueous ionic liquids analogues as a function of water content and temperature

Ionic liquids analogues known as Deep Eutectic Solvents （DESs） are gaining a surge of interest by the scientific community, and many applications involving DESs have been realized. Moisture content is one of the important factors that affects the physical and chemical characteristics of these fluids. In this work, the effect of mixing water with three common type III DESs on their viscosity was investigated within the water tool fraction range of （0-1） and at the temperature range （298.15-353.15 K）. Similar trends of viscosity variation with respect to molar composition and temperature were observed for the three studied systems, Due to the asymmetric geometry of the constituting molecules in these fluids, their viscosity could not be modeled effectively by the conventional Grunberg and Nissan model, and the Fang-He model was used to address this issue with excellent performance. All studied aqueous DES mixtures showed negative deviation in viscosity as compared to ideal mixtures, The degree of intermolecular interactions with water reaches a maximum at a composition of 30% aqueous DES solution. Reline, the most studied DES in the literature, showed the highest deviation. The informa- tion presented in this work on the viscosity of aqueous DES solutions may serve in tuning this important property for diverse industrial applications involving these novel fluids in fluid flow, chemical reactions, liquid-liquid separation and many more.

Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging

For accurately forecasting the liquid steel temperature in ladle furnace （LF）, a novel temperature predic tion model based on optimally pruned Bagging combined with modified extreme learning machine （ELM） is pro posed. By analyzing the mechanism of LF thermal system, a thermal model with partial linear structure is obtained. Subsequently, modified ELM, named as partial linear extreme learning machine （PLELM）, is developed to estimate the unknown coefficients and undefined function of the thermal model. Finally, a pruning Bagging method is pro- posed to establish the aggregated prediction model for the sake of overcoming the limitation of individual predictor and further improving the prediction performance. In the pruning procedure, AdaBoost is adopted to modify the ag- gregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggre- gation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging＇s ro- bustness against highly influential points, reduce the storage needs as well as speed up the computing time. The pro- posed prediction model is examined by practical data, and comparisons with other methods demonstrate that the new ensemble predictor can improve prediction accuracy, and is usually consisted compactly.