Thermophysical and rheological properties of sorbitol + ([mmim](MeO)2PO2) ionic liquid solutions: Solubility, density and viscosity

Application of machine learning techniques to the modeling of solubility of sugar alcohols in ionic liquids

The current trend of chemical industries demands green processing, in particular with employing natural substances such as sugar-derived compounds. This matter has encouraged academic and industrial sections to seek new alternatives for extracting these materials. Ionic liquids (ILs) are currently paving the way for efficient extraction processes. To this end, accurate estimation of solubility data is of great importance. This study relies on machine learning methods for modeling the solubility data of sugar alcohols (SAs) in ILs. An initial relevancy analysis approved that the SA-IL equilibrium governs by the temperature, density and molecular weight of ILs, as well as the molecular weight, fusion temperature, and fusion enthalpy of SAs. Also, temperature and fusion temperature have the strongest influence on the SAs solubility in ILs. The performance of artificial neural networks (ANNs), least-squares support vector regression (LSSVR), and adaptive neuro-fuzzy inference systems (ANFIS) to predict SA solubility in ILs were compared utilizing a large databank (647 data points of 19 SAs and 21 ILs). Among the investigated models, ANFIS offered the best accuracy with an average absolute relative deviation (AARD%) of 7.43% and a coefficient of determination (R²) of 0.98359. The best performance of the ANFIS model was obtained with a cluster center radius of 0.435 when trained with 85% of the databank. Further analyses of the ANFIS model based on the leverage method revealed that this model is reliable enough due to its high level of coverage and wide range of applicability. Accordingly, this model can be effectively utilized in modeling the solubilities of SAs in ILs.

Key Challenges of Microbial Degradation of Keratinous Wastes

Feather is the main waste of poultry industries and constitutes of 90% keratin. Wastes composed of keratin are insoluble and recalcitrant to degradation using conventional decomposing methods. Microbial degradation for keratinous wastes is a promising approach for being eco-friendly and economically. However, due to insolubility of keratinous waste, it has several challenges in upstream and downstream processes such as culture medium optimization, designing of bioreactor, bioreaction/flow type, bioreactor configurations considering mass and heat transfer limitations, rheology derived problems, monitoring of microbial activity, choosing of the right scale-up parameter and purification. In this study, the challenges for keratin degradation processes were discussed with the aim of opening new opportunities for keratinous waste treatments in industrial level.