Pyomo.DoE

Pyomo.DoE (Pyomo Design of Experiments) is a Python library for model-based design of experiments using science-based models.

Pyomo.DoE was originally created by Jialu Wang and Alexander W. Dowling at the University of Notre Dame as part of the Carbon Capture Simulation for Industry Impact (CCSI2) project, funded through the U.S. Department Of Energy Office of Fossil Energy with assistance from John Siirola, Bethany Nicholson, Miranda Mundt, and Hailey Lynch. Significant improvements and extensions were contributed by Dan Laky, and Shuvashish Mondal with funding from the Process Optimization & Modeling for Minerals Sustainability (PrOMMiS) project and the University of Notre Dame.

If you use Pyomo.DoE, please cite the [PyomoDOE-paper]:

Wang, Jialu, and Alexander W. Dowling. “Pyomo.DOE: An open‐source package for model‐based design of experiments in Python”, AIChE Journal, 68(12), e17813. 2022. DOI 10.1002/aic.17813

Index of Pyomo.DoE documentation

• Overview
• Quick Start Guide
  • Step 0: Import Pyomo and the Pyomo.DoE module and create an Experiment class
  • Step 1: Define the Pyomo process model
  • Step 2: Finalize the Pyomo process model
  • Step 3: Label the information needed for DoE analysis
  • Step 4: Implement the get_labeled_model method
  • Step 5: Exploratory analysis (Enumeration)
  • Step 6: Performing an optimal experimental design
• Experiment Abstraction
• Objective Options
• Multiple Experiments
• Parameter Uncertainty