/foqus

FOQUS: Framework for Optimization and Quantification of Uncertainty and Surrogates

Primary LanguagePythonOtherNOASSERTION

Documentation Status Tests Nightlies

FOQUS: Framework for Optimization, Quantification of Uncertainty, and Surrogates

Package includes: FOQUS GUI, Optimization Engine, Turbine Client. Requires access to a Turbine Gateway installation either locally or on a separate cluster/server. #GAMS is required for heat integration option.

Getting Started

Install

To get started right away, start with the installation instructions for the most recent stable release.

We have several videos playlists on how to install FOQUS:

Documentation and User's Manual

Read the full documentation for FOQUS (including the installation manual). Documentation for past releases or the latest (unreleased) development version are available.

A complete set of usage and installation instruction videos for FOQUS are available on our YouTube channel.

FAQ

See our FAQ for frequently asked questions and answers

Authors

See also the list of contributors who participated in this project.

Development Practices

  • Code development will be peformed in a forked copy of the repo. Commits will not be made directly to the repo. Developers will submit a pull request that is then merged by another team member, if another team member is available.
  • Each pull request should contain only related modifications to a feature or bug fix.
  • Sensitive information (secret keys, usernames etc) and configuration data (e.g database host port) should not be checked in to the repo.
  • A practice of rebasing with the main repo should be used rather that merge commmits.

Versioning

We use SemVer for versioning. For the versions available, releases or tags on this repository.

License & Copyright

See LICENSE.md file for details.

Reference

If you are using FOQUS for your work, please reference the following paper:

Miller, D.C., Agarwal, D., Bhattacharyya, D., Boverhof, J., Chen, Y., Eslick, J., Leek, J., Ma, J., Mahapatra, P., Ng, B., Sahinidis, N.V., Tong, C., Zitney, S.E., 2017. Innovative computational tools and models for the design, optimization and control of carbon capture processes, in: Papadopoulos, A.I., Seferlis, P. (Eds.), Process Systems and Materials for CO2 Capture: Modelling, Design, Control and Integration. John Wiley & Sons Ltd, Chichester, UK, pp. 311–342.

Technical Support

If you require assistance, or have questions regarding FOQUS, please send an e-mail to: ccsi-support@acceleratecarboncapture.org or open an issue in GitHub