Identifying and Resolving Physical Property Issues in Chemical Process Simulation Using the Dortmund Data Bank
3 day course
Chemical process simulation using software packages like Aspen Plus, Pro/II etc. is widely applied throughout the chemical and petrochemical industry world-wide. It is generally acknowledged, that the reliability of the physical property data used in process simulation is of great importance for the reliability of the simulation results.
All process simulation software packages provide a variety of thermodynamic models and data banks with the required model parameters. The applicability of these models and parameters to the process in consideration needs to be verified by the user of the software.
Due to past experiences with unreliable physical property description, many companies are investing serious effort and resources into the generation and verification of the so-called property packages for process simulation. These efforts run from providing thermophysical data banks and software like DDB and DDBSP throughout the company to centralizing the generation and verification of the models and parameters.
While in case of simple mixtures (e.g. hydrocarbons), the default values from the simulator database are often sufficiently reliable, this cannot be expected in case of more non-ideal chemical systems containing e.g. oxygenated compounds like alcohols, ketones, carboxylic acids etc.. In this case, important parameters (e.g. binary interaction parameters) may even not be available. The problem is even more severe in case of electrolyte systems.
Aim of the course is to provide information about the different physical property parameters supplied by the Aspen Plus simulator, especially concerning their generation and reliability.
Using different examples, a critical sensitivity concerning these parameters should be introduced.
The course features an introduction to the Dortmund Data Bank (DDB) and the DDB software package with special focus on the verification of physical property packages and the generation of missing parameters either by regression to experimental data or the responsible and careful application of estimation methods (UNIFAC, mod. UNIFAC, PSRK, …). Similar functionality in the process simulator will be critically evaluated.
Thermodynamic basics and the implementation in via methods, models and routes in the simulator will be added as required.
The course will equally divided into lectures and workshops.
Day 1 Introduction of the instructors and their background
Motivational examples and course overview
Physical property models and parameters in Aspen Plus – an overview
- Pure component property constants and equation parameters by DIPPR
- Binary interaction parameters for Wilson, NRTL, UNIQUAC
- Parameters of predictive methods (UNIFAC, mod. UNIFAC, PSRK, ..)
The UNIFAC consortium.
- Note on electrolyte models and parameters
Introduction to the Dortmund Data Bank and the DDB software package (Part 1)
- Parts of DDB/DDBSP
- Components and Component Lists in DDB
- Pure Component Data Estimation (Artist)
- Pure Component and Mixture Data Retrieval and Output
- Pure Component Data Regression
Day 2 Introduction to the Dortmund Data Bank and the DDB software package (Part 2)
- Mixture Data Regression
- Simultaneous Mixture Data Regression
- Mixture Data Estimation
- DDB Parameter Data Bank
Check parameters prior to process simulation (workshop)
Generate missing parameters by data regression or estimation
Data regression and physical property estimation in Aspen Plus (DRS, NIST-TDE, PES)
Day 3 Workshop parameter and model verification
On the last day, the participants should verify physical property models and parameters in several simulation projects that will be supplied by the instructors.
Examples include simple one-column separations, extractive and azeotropic distillation and selected further examples.
In between the workshops, short lectures will discuss the findings of the participants as well as additional topics.
Internal structure of the Aspen Property Plus thermo engine will be discussed (Models, Methods, Routes)