Brightcon 2025, hackathon & courses in Grenoble and online

Using primary data for life cycle assessments is the common and preferred way to conduct life cycle assessments. But collecting data is time consuming, can be incomplete and only reflects a static and specific result. In order to generate reliable inventory data, the additional use of external software, like flowsheet simulation is necessary. Flowsheet simulation software is based on thermodynamic models, fluid property databases, specialised numerical solvers and more tools for design and optimization of processes. Such rigorous simulations open up new perspectives for life cycle assessment: They enable transparent scenario analyses, the quantification of uncertainties and the investigation of technological alternatives.
Since there is currently no available interface between LCA software and process simulation tools, data exchange is mainly carried out manually in practice. Although approaches to semi-automated data transfer have already been published (TEPET), a generic open interface is still lacking. Such an interface is necessary for the consistent, efficient and reusable implementation of simulation models into the workflow of LCA studies.
In this contribution the work on the python package SiModIn is presented, offering an open interface of external simulation models to brightway25. The package includes a data schema for an interface to brightway25, and offers abstract classes for linking external simulation models. Built-in unit transformation and validation ensure a straightforward implementation of simulation models as data source in LCA. This enables a linking of multiple modelling frameworks with brightway25 and promotes the possibility to make models available for others in a transparent, consistent and reusable way.
The functionality of SiModIn is demonstrated using a process heat model created with the python package TESPy (Thermal Engineering Systems in Python). The example model depicts the specific process heat demand linked to steam generation and distribution and enables the creation of detailed, site-specific LCA datasets. Process relevant parameters such as the required steam temperature and other plant specific parameters are taken into account.