Skip to main content

ExerPy: Exergy Analysis in Python

ExerPy is a Python library and framework for automated exergy analysis of energy-conversion systems. It integrates seamlessly with simulation tools such as Ebsilon Professional, Aspen Plus and TESPy. 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.

This release has been tested with TESPy 0.11.2, Ebsilon Professional 17 and Aspen Plus 14.5.

ExerPy is part of the Open Energy Modelling Framework oemof.

https://raw.githubusercontent.com/oemof/exerpy/refs/heads/main/docs/_static/images/logo_exerpy_big.svg

Key Features

  • Comprehensive Exergy Analysis: Calculate physical and chemical exergy, perform component-level and system-wide analysis.

  • Flexible Integration: Compatible with multiple simulation tools (Ebsilon Professional, Aspen Plus, TESPy).

  • 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:

pip install exerpy

Quick Start Example

Here’s a simple example how to perform an exergy analysis using ExerPy:

Custom json

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

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

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 online documentation.

Citation

If you use ExerPy in your scientific work, please consider citing it to support ongoing development. You can cite ExerPy using the following BibTeX entry:

@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 = {0.1.0},
     year = {2026}
}

License

MIT License

Copyright (c) Sergio Tomasinelli

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Metadata

Release files for exerpy 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for exerpy 0.1.0
File Size Uploaded
exerpy-0.1.0.tar.gz 4.2 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for exerpy 0.1.0
File Interpreter ABI Platform
exerpy-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 4.4 MB

Release files / exerpy-0.1.0.tar.gz

Download URL exerpy-0.1.0.tar.gz
Size 4.2 MB
Tags Source
SHA-256 checksum
How to use checksums
c71460803df9c123fb884baf7fb349a7053e42aaae5ab8c5d0dbd03c1d74b5c1
BLAKE2b-256 checksum
How to use checksums
669ec66daa5f9ab5fb5169d1447e406f92596531ed43bc6e960d7dd032376acf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.4

Release files / exerpy-0.1.0-py3-none-any.whl

Download URL exerpy-0.1.0-py3-none-any.whl
Size 189.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
34f2084901e83c2af6a72a3689a32248589561d785827945fd7266b0dfdb49b0
BLAKE2b-256 checksum
How to use checksums
ccdb61792b03de2605f0234ae8f6abfc1c565f78ed1830a65e4221db80fb0db1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.4

Release history Release notifications | RSS feed

This release

0.1.0 This release

2 release files

0.0.11

2 release files

0.0.10

1 release file

0.0.9

1 release file

0.0.8

1 release file

0.0.7

2 release files

0.0.6

1 release file

0.0.3

1 release file

0.0.2

1 release file

0.0.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page