Activity coefficients in nearly athermal mixtures predicted from equations of state: Don't blame the cubic when it is the lack of a third parameter!

The idea that cubic EoS's are very primitive and limited models, quite extended at present among researchers working on fluid properties and phase equilibria, has different roots, including some limitations observed specifically for classic and popular equations like Peng-Robinson (PR) or Soave-Redlich-Kwong (SRK). These are two-parameter models, i.e. they have only an attractive and a repulsive parameter to characterize each molecule, while other models like SAFT but also cubic –and still for non-associating molecules-introduce also a third parameter related somehow to the molecular structure or shape. One of the alluded limitations, actually a very clear one, is the complete failure in describing the non-ideality in nearly athermal mixtures, like those composed of n-alkanes with different chain lengths: SRK and PR predict positive deviations from ideality, which increase with the system asymmetry, while experimental measurements show exactly the opposite, i.e. increasing negative deviations from ideality. This provides an excellent opportunity to try to clarify whether such failure is due to the cubic nature of these classic models or to their two-parameter character and/or to the classic van der Waals one-fluid (vdW1f) mixing rules typically used. With that motivation, in this work we used models representing three different categories, in a completely predictive way: a two-parameter cubic EoS (PR), a three-parameter cubic EoS (RKPR) and a three-parameter SAFT EoS (PC-SAFT). Their predictions of infinite dilution activity coefficients were analyzed and compared, in contrast to available data for different mixtures of n-butane to n-octane as the lighter compound and paraffins ranging from C16 to C36 as the heavier, in both extremes of dilution. The obtained results, and their analysis, allowed us to extract very clear conclusions which were not present in the literature so far, regarding the importance of a third parameter in any type of EoS.Fil: Tassin, Natalia Giselle. Universidad Nacional de Córdoba. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada; ArgentinaFil: Rodriguez Reartes, Sabrina Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; ArgentinaFil: Cismondi Duarte, Martín. Universidad Nacional de Córdoba. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada; Argentina. YPF - Tecnología; Argentin

Comparison between multi-linear- and radial-basis-function-neural-network-based QSPR Models for the prediction of the critical temperature, critical pressure and acentric factor of organic compounds

Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92–2.0% for training and 1.7–4.8% for validation sets) than MLR models (an AAD% of 3.2–8.7% for training and 6.2–12.2% for validation sets). The RMSE of the RBFNN models was 20–30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance

Prediction of properties of new halogenated olefins using two group contribution approaches

AbstractThe increasingly restrictive regulations for substances with high ozone depletion and global warming potentials are driving the search for new sustainable fluids with low environmental impact. Recent research works have pointed out the great potential of fluorine- and chlorine-based olefins as refrigerants and solvents, due to their environmentally-friendly features. However there is a lack of experimental data of their thermophysical properties. In this work we present two models based on a group contribution method, using a classical approach and neural networks, to predict the critical temperature, critical pressure, normal boiling temperature, acentric factor, and ideal gas heat capacity of organic fluids containing chlorine and/or fluorine. The accuracy of the prediction capacity of the two models is analyzed, and compared with equivalent methods in the literature. The models showed an average reduction of the absolute relative deviation for all the studied properties of more than 50%, compared to other methods. In addition, it was observed that the neural-network-based model yielded a better accuracy than the classical approach in the prediction of all the properties, except for the acentric factor, due to the lack of experimental data for this property

Protein-salt binding data from potentiometric titrations of lysozyme in aqueous solutions containing KCl

An existing method for potentiometric titrations of proteins was improved, tested and applied to titrations of the enzyme hen-egg-white lysozyme in aqueous solutions containing KCl at ionic strengths from 0.1 M to 2.0 M at 25 C. Information about the protein`s net charge dependence on pH and ionic strength were obtained and salt binding numbers for the system were calculated using a linkage concept. For the pH range 2.5--11.5, the net charge slightly but distinctly increases with increasing ionic strength between 0.1 M and 2.0 M. The differences are most distinct in the pH region below 5. Above pH 11.35, the net charge decreases with increasing ionic strength. Preliminary calculation of binding numbers from titration curves at 0.1 M and 1.0 M showed selective association of chloride anions and expulsion of potassium ions at low pH. Ion-binding numbers from this work will be used to evaluate thermodynamic properties and to correlate crystallization or precipitation phase-equilibrium data in terms of a model based on the integral-equation theory of fluids which is currently under development

Hazardous materials database

Handling and disposal of a retired object can be a major component of its Life Cycle Cost. Often, during dismantling of a retired object many hazardous materials are released. Disposal of hazardous materials also need to comply with various federal regulations. Agencies like Occupational Safety and Health Administration (OSHA) and the Environmental Protection Agency (EPA) set these safety regulations. It is possible to apply generic exposure and release controls to protect workers from hazardous materials. For the most cost effective hazard controls it is necessary first to identify the materials and their properties of primary concern. There is a need to have an integrated database for properties of Hazardous Materials.;This project developed a database for properties of hazardous materials. The database was implemented in Microsoft Access. Thirty-four chemicals and their categories were identified. These chemicals are encountered during dismantling of a retired object. The database currently contains 60 main fields, which also contain subfields. Information such as its physical properties, chemical properties, health hazards, releases from demolition or various other industrial processes and references to safety, health and environmental regulations can be obtained from this database. A decision support system was developed as a front end to Access. The decision support system was implemented in Visual Basic.;In the future, this database can be expanded to include non-hazardous materials. The database capabilities were demonstrated on the hazardous materials occurring in the ship dismantling industry. It is expected that the database will save significant time and cost in data retrieval. Information retrieval from the database is through an intuitive graphical interface, and suitable for use by a non-computer person