The ROLE of Physical Property Databases IN CH. E. EDUCATION

 

       Numerical problem solving in Chemical Engineering typically requires a mathematical model of the problem along with physical and thermodynamic data and correlations (equations) of the chemical substances involved. The required pure compound properties are usually divided into constant properties and temperature and/or pressure dependent properties. In most ChE textbooks, the preparation of the mathematical model associated with a particular problem is typically emphasized. The property data required for solution of the problems are usually provided in tabular or graphical form in the appendix of the textbook and/or in a CD associated with the book (see for example, Felder and Rousseau, 2000 and Himmelblau and Riggs, 2004).  

       Recently, various databases that contain extensive physical property data for large number of compounds have become available. Typical examples are the DIPPR (Rowley et al., 2010) and the NIST (http://webbook.nist.gov/chemistry/) databases. The use of the data available in the databases for problem solving has significant advantages over the use of the data provided in the textbooks. Some of the more important advantages are:

1.    In engineering practice, the databases are used as a principal source of property data and correlations. Thus it is important that students become experienced with the application of these sources during their educational programs.

2.    The databases provide consistent sets of correlations for temperature-dependent properties enabling the use of solution techniques independent of the format in which the property data is provided. If temperature-dependent data are provided in tabular or graphical forms, for example, this prevents the use of standard numerical methods for problem solution, and data format dependent ad hoc solution techniques have to be used. 

3.    The data provided in databases are continuously updated with the new data as they become available. Property data in textbooks are often taken "as is" from references that may be as much as half a century old. The textbook data may be incorrect or even contradictory. It is important that students become accustomed to using reliable data.

4.    The data available in the databases are evaluated and in cases when multiple values are available for the same property, one recommended value is selected by the database professionals. There may be very substantial differences between property values reported by different investigators. Brauner et al., 2005, for example mention the case of the melting point of 4-methyloctane for which the recommended value is 159.95 K while one reported experimental value is 219.62 K. The students must be made aware of the fact that several property values may be available and learn how to find and utilize the value with the highest confidence in its correctness.

5.    The databases usually provide uncertainty values (upper limit on experimental error) which enable estimation of the uncertainty of the problem solution using error propagation analysis. An important goal of chemical engineering education is to impart to students that the numerical "solution" of a problem almost always has some associated uncertainty.

 

       A convenient option for chemical engineering educators to incorporate the use of databases into their teaching has been integrated into the Polymath package (www.polymath-software.com ). This widely-used computational tool now contains a "sample database" subset of the DIPPR database.  The "sample database" contains 34 constant properties and 16 temperature dependent property correlations for ~120 compounds that are most frequently used in chemical engineering textbooks. A special interface within Polymath now enables a convenient search of the sample database for selected compounds. The desired properties can be selected, and the data and correlations outputted in a format that can be copied and pasted directly into a computer code with all the significant figures given in the database. The formats that are currently supported are for Polymath, MATLAB and Excel.

        In the extended abstract and the presentation, several examples will be provided to demonstrate the incorporation of physical property databases in numerical problem solving.  Our experience with the in-class use of the Polymath and the associated DIPPR sample database will also be described.  

       

References

1.       Brauner, N., Shacham, M., Cholakov, G. St. and Stateva, R. P., “Property Prediction by Similarity of Molecular Structures – Practical Application and Consistency Analysis”, Chem. Eng. Sci. 60, 5458 – 5471 (2005)

2.       Felder, R. M. and Rousseau, R. W., Elementary Principles of Chemical Processes,3^rd Ed, John Wiley & Sons, Inc, Hoboken, New-Jersey, 2000.

3.       Himmelblau D. M., and Riggs, J. B., Basic principles and Calculations in Chemical Engineering, 7^th Ed., Prentice-Hall, Upper Saddle River, New-Jersey, 2004.

4.       Rowley, R. L.; Wilding, W. V.; Oscarson, J. L.; Yang, Y.; Zundel, N. A., DIPPR Data Compilation of Pure Chemical Properties, Design Institute for Physical Properties, (http//dippr.byu.edu), Brigham Young University, Provo, Utah, 2010.