Since 1998 Brigham Young University has served as the primary researcher for the DIPPR project 801. BYU also handled the public licensing for this project under the direction of the American Institute of Chemical Engineers (AIChE). However, effective April 1, 2009, AIChE began directly servicing all public licenses to allow BYU to focus on research in order to better serve your physical property needs. If you have any questions, please email us at dippr@aiche.org. Thank you!
dippr project 801 full version
Comparison of literature data for CO2 + ethyl acetate (blue) [54], + 1,4-dioxane (purple) [53], and + 1,2-dimethoxyethane (gray) [22] binary systems at T = 343.15 K and predictions by GEOS (full lines) and PR EoS (dashed lines).
Provides source of evaluated standard thermophysical property values in Knovel's interactive format. Nineteen fully searchable and filterable interactive tables enhanced with the equation plotter applet for graphical representation of the data provide a convenient access to a wealth of physical, thermodynamic, and transport properties for industrially important chemicals used in chemical process and equipment design
International Tables for Crystallography is the definitive resource and reference work for crystallography and structural science. The online version of International Tables for Crystallography provides access to the content of the full set of all eight volumes in the series in pdf and richly linked html format.
The authors gratefully acknowledge support from the Office of Naval Research under contract N0001415WX01414. We thank J.G. Champlain for helpful discussions in understanding the behavior of the FET devices.
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Most thermophysical-property databases (TPD) provide low-level quality control checks. This manuscript focuses on additional, higher-level data evaluations made possible by the breadth of data stored in the database. For example, thermodynamic equations relate the critical point, vapor-pressure curve, enthalpy of vaporization, liquid density, and liquid and vapor heat capacities to each other. Thermodynamic consistency among these properties can be used to guide selection of the best data sets. Even more broadly, molecular structure-based trends in properties can be identified within the database, and the properties of structurally related compounds can be effectively used to discriminate among available datasets. Automated property predictions can be used in conjunction with the TPD to guide the selection of the most accurate data. These and other high-level consistency tools will be illustrated based on evaluation and quality control work associated with the DIPPR 801 TPD project for pure chemicals.
The volume of the liquid (V) was calculated by computing the volume of the solid of revolution (see insert in Fig. 1), which is a generalization of an earlier method utilizing projections (photography in visible light) of phase interfaces in glass tubes without using the reconstruction of the central plane59. The partial molar volume of methane (\(\overlineV_\textA = \partial V/\partial n_\textA\)) and its uncertainty due to random errors (ur, cover factor 2) were calculated based on the mole amount of absorbed methane (\(n_\textA\)) in the entire liquid body and its volume at fixed T, p, and mole amount of the perdeuterated xylene (\(n_\textB\)), see Fig. 2A and Table S4 in SI. The molar volume (\(V_\textm\)) of the liquid and its density (\(\rho\)) depend on the mole fractions [\(x_\textA = n_\textA /\left( n_\textA + n_\textB \right)\), \(x_\textB = 1 - x_\textA\)] and molar masses (M) such that
This work is based on experiments performed at the Swiss spallation neutron source SINQ, Paul Scherrer Institute, Villigen, Switzerland69. Experiments were conducted within beamtime proposal 20200129 at NEUTRA thermal neutron imaging beamline at the Paul Scherrer Institute. Financial support obtained from the Ministry of Education, Youth and Sports of the Czech Republic, specific university research grant A1_FCHI_2020_002, is gratefully acknowledged.
The Industry and Agro-resource (IAR) Cluster changed his name to Bioeconomy for Change. It promotes exchanges, decompartmentalisation and project launches. It assembles players from the whole value chain around a shared innovation problem. The Cluster and its members are involved in developing technology and products to replace petroleum-based raw materials with agricultural, forestry and algal plant production.
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N2 - Process safety studies and assessments rely on accurate property data. Flammability data like the lower and upper flammability limit (LFL and UFL) play an important role in quantifying the risk of fire and explosion. If experimental values are not available for the safety analysis due to cost or time constraints, property prediction models like group contribution (GC) models can estimate flammability data. The estimation needs to be accurate, reliable and as less time consuming as possible. However, GC property prediction methods frequently lack rigorous uncertainty analysis. Hence, there is no information about the reliability of the data. Furthermore, the global optimality of the GC parameters estimation is often not ensured.In this research project flammability-related property data, like LFL and UFL, are estimated using the Marrero and Gani group contribution method (MG method). In addition to the parameter estimation an uncertainty analysis of the estimated data and a comparison to other methods is performed. A thorough uncertainty analysis provides information about the prediction error, which is important for the use of the data in process safety studies and assessments. The method considers the group contribution in three levels: The contributions from a specific functional group (1st order parameters), from polyfunctional (2nd order parameters) as well as from structural groups (3rd order parameters). The latter two classes of GC factors provide additional structural information beside the functional group. The contributions of all three factors are then summed upThe method is simple and easy to apply. Taking into account higher order groups increases the accuracy. Furthermore, the application range is high due to the high number of considered functional and structural contributions.In this study, the MG-GC-factors are estimated using a systematic data and model evaluation methodology in the following way:1) Data. Experimental flammability data is used from AIChE DIPPR 801 Database.2) Initialization and sequential parameter estimation. An approximation using linear algebra provides the first guess. Then the 1st, 2nd and 3rd order parameter estimations are performed separately. 3) Simultaneous parameter estimation. The result of the sequential estimation serves then as initial guess for the simultaneous parameter estimation algorithm. Different minimization/search algorithms ensure global optimality.4) Uncertainty. A rigorous uncertainty analysis that includes asymptotic approximation of covariance matrix for parameter estimators is performed in order to provide information of the model prediction quality (95% confidence interval). 2ff7e9595c
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