Evaluation of the conductor-like screening model for real solvents for the prediction of the water activity coefficient at infinite dilution in ionic liquids

Ionic liquids (ILs) have attracted great attention, from both industry and academia, as alternative fluids for very different types of applications. The large number of cations and anions allow a wide range of physical and chemical characteristics to be designed. However, the exhaustive measurement of all these systems is impractical, thus requiring the use of a predictive model for their study. In this work, the predictive capability of the conductor-like screening model for real solvents (COSMO-RS), a model based on unimolecular quantum chemistry calculations, was evaluated for the prediction water activity coefficient at infinite dilution, gamma(infinity)(w), in several classes of ILs. A critical evaluation of the experimental and predicted data using COSMO-RS was carried out. The global average relative deviation was found to be 27.2%, indicating that the model presents a satisfactory prediction ability to estimate gamma(infinity)(w) in a broad range of ILs. The results also showed that the basicity of the ILs anions plays an important role in their interaction with water, and it considerably determines the enthalpic behavior of the binary mixtures composed by Its and water. Concerning the cation effect, it is possible to state that generally gamma(infinity)(w) increases with the cation size, but it is shown that the cation-anion interaction strength is also important and is strongly correlated to the anion ability to interact with water. The results here reported are relevant in the understanding of ILs-water interactions and the impact of the various structural features of its on the gamma(infinity)(w) as these allow the development of guidelines for the choice of the most suitable lLs with enhanced interaction with water.

Model predictive control applied to a supply chain management problem

Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017