Project Details
Projekt
Machine Learning and Optimal Experimental Design for Thermodynamic Property Modeling
Subject Area
Technical Thermodynamics
Mathematics
Mathematics
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 466528284
For many tasks in chemical and energy engineering, the accurate knowledge of thermodynamic properties (e.g., pressure and temperature with density and speed of sound) and the phase behavior of the involved fluids plays a key role. In science, such properties are required for the basic understanding of chemical-physical behavior and for the development of predictive models. For industry, thermodynamic properties are the basis for the design of safe and sustainable processes and machinery. However, the quality of property calculations using equations of state (EOS) depends largely on the availability and accuracy of experimental data. Measurements of such data are often carried out within the frame of a dense grid of measurement points, which delivers a comprehensive data set. Nevertheless, with the aim to develop an accurate EOS, this approach is time-consuming, while it is unclear whether all data are ultimately substantial to the model development. As a result, the required time and financial expenditure makes the generation of reliable models rather limited. Considering this, it is highly desirable to significantly reduce the model development time by limiting the amount of experimental data to the required extent and to involve functional forms, which enable short computing times for the application in process simulation. Therefore, the major goal of the research project is to tackle the aforementioned issues by realizing a specific interplay between (1) interpretable machine learning (ML) to find the ideal functional form of the EOS, (2) optimal experimental design to find the most appropriate measurement points and (3) the actual experiment. A potential workflow can be imagined as follows: Starting from initial thermodynamic property measurements, ML-based EOS modeling is used to create a first functional form. This form is used to predict the next most informative measurements, which can then be used as input for further EOS modeling. When to terminate this workflow is inherent part of the project's research schedule. One important output of the project is an in-situ software tool for thermodynamic measurement planning and model development, which considers the measurement effort, model accuracy and interpretability.
DFG Programme
Priority Programmes
