A Framework Utilizing a Seamless Integration of Python with AspenPlus(R) for a Multi-Criteria Process Evaluation

Kurzfassung

Detailed assessment of fuel production processes at an early stage of a project is crucial to identify potential technical challenges, optimize efficiency and minimize costs and environmental impact. 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 Python 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 and post-processed for techno-economic and environmental analyses. Furthermore, the interface allows the implementation of complex kinetic models or optimization routines for single process units. An additional extension for heat integration ensures the technical viability of the process route for reliable comparisons of different routes and process configurations. The functionalities are applied to a biomass- and power-based methanol production process including various process designs and operating conditions. To keep the level of detail at a high level, additional Python scripts are implemented securing a proper scaling of process units such as the methanol synthesis reactor system. The process configurations are assessed technically, economically and environmentally.