Abstract
While process simulations often are either very rigid and accurate or very flexible and unprecise, informed decision making can only be maintained by establishing a detailed process model as early as possible within the project lifecycle while keeping relevant aspects of the process flexible enough. In this work, we present the development of a framework based on a dynamic interface between AspenPlus® process simulations and Python, enabling enhanced flexibility and automation for process modeling and optimization. This integration leverages the powerful simulation capabilities of AspenPlus® with the versatility of Py-thon for data analysis and optimization, delivering significant improvements in workflow efficiency and process control. By utilizing the dynamic simulation data exchange with Python, extensive parameter studies can be conducted.
In this provided dataset, the necessary input data, as well as the output files for each parameter run are provided. Furthermore, a .runtime and logging file a attached. As case study the freely available process simulation of James Scott and Thomas A. Adams (https://psecommunity.org/materials/calculationfiles) is chosen. For demonstration purpose two variation runs are executed with different record options. As variation parameter the compression of the oxygen led into the SOFC is varied from 1.91 MPa to 2.5 MPa.
The execution time on an Intel(R) Xeon(R) E-2186G CPU @ 3.80 GHz, with 65'536 MB working memory was for a full data retrievel 7:46 min, of which 6:49 min were communication with AspenPlus(R). In a second variation run, three specific blocks and three specific streams were chosen for data extraction. The execution time for the second run was 1:27 min and 1:25 min communicating with AspenPlus(R).