################################# ExerPy: Exergy Analysis in Python ################################# ExerPy is a Python library and framework for automated exergy analysis of energy-conversion systems. It builds on the exergy analysis methodology implemented in TESPy and extends its capabilities to seamlessly integrate with simulation tools such as Ebsilon Professional, Aspen Plus, and TESPy itself. ExerPy enables engineers and researchers to identify inefficiencies and optimize the performance of thermodynamic systems through automated workflows and consistent data handling. With its advanced features, ExerPy accounts for both physical and chemical exergy, automates exergy balances, and allows users to perform automated analysis of individual components and entire processes. This helps identify where and why exergy losses occur, facilitating strategies to improve efficiency, reduce costs, and support sustainable energy use. ExerPy is part of the Open Energy Modelling Framework `oemof `_. .. image:: /_static/images/logo_exerpy_big.svg :align: center :class: only-light .. image:: /_static/images/logo_exerpy_big_dark.svg :align: center :class: only-dark ************ Key Features ************ - **Automated, User-friendly Analysis**: Minimizes user input and enables automated exergy analysis with minimal human error. - **Comprehensive Exergy Analysis**: Calculates physical and chemical exergies for both component- and system-level analyses. - **Flexible Integration**: Compatible with multiple simulation tools (Ebsilon Professional, Aspen Plus, TESPy, and user-defined input; other software will be integrated in the future based on user feedback). - **Advanced Component Library**: Pre-built components for common energy system elements (turbines, heat exchangers, etc.). - **Extensible Architecture**: Easy-to-use framework for implementing custom components and analysis methods. - **Robust Data Handling**: Consistent fluid property models and automated data extraction from simulation tools. - **Open Source**: MIT licensed, free for academic and commercial use. ********************* Getting Started ********************* ============ Installation ============ You can install the latest version of ExerPy using pip: .. code:: bash pip install exerpy For complete installation instructions go to :ref:`this page ` =================== Quick Start Example =================== Here are simple examples how to perform an exergy analysis using ExerPy: Custom json ----------- .. code:: python from exerpy import ExergyAnalysis model_path = 'my_model.json' ean = ExergyAnalysis.from_json(model_path, chemExLib='Ahrendts') fuel = {"inputs": ['Fuel'], "outputs": []} product = {"inputs": ['Power'], "outputs": []} ean.analyse(E_F=fuel, E_P=product) ean.exergy_results() Exported tespy model -------------------- .. code:: python from exerpy import ExergyAnalysis model_path = 'tespy_model_export.json' ean = ExergyAnalysis.from_tespy(model_path, chemExLib='Ahrendts') fuel = {"inputs": ['Fuel'], "outputs": []} product = {"inputs": ['Power'], "outputs": []} ean.analyse(E_F=fuel, E_P=product) ean.exergy_results() You can also use a tespy network object instead! Ebsilon model ------------- .. code:: python from exerpy import ExergyAnalysis model_path = 'my_model.ebs' ean = ExergyAnalysis.from_ebsilon(model_path, chemExLib='Ahrendts') fuel = {"inputs": ['Fuel'], "outputs": []} product = {"inputs": ['Power'], "outputs": []} ean.analyse(E_F=fuel, E_P=product) ean.exergy_results() For more detailed tutorials and examples, see the :ref:`Example applications `. ******** Citation ******** If you use ExerPy in your scientific work, please cite it to support ongoing development. Use the following BibTeX entry: .. code:: @software{ExerPy, author = {Tomasinelli, Sergio and Witte, Francesco and Müller, Robert}, title = {{ExerPy}: Exergy Analysis in Python}, note = {Supervision: Prof. Dr.-Ing. Fontina Petrakopoulou} url = {https://github.com/oemof/exerpy}, version = {0.0.8}, year = {2026} } ******* License ******* .. include:: ../LICENSE