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idaes_examples/notebooks/index.md

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### Prerequisites
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Examples in this documentation are rigorously *tested* to ensure that they work with the *latest* version of the IDAES
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**Install the latest version of IDAES.** Examples in this documentation are rigorously *tested* to ensure that they work with the *latest* version of the IDAES
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software.
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For more information on installing IDAES on your platform,
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please refer to the [IDAES documentation](https://idaes-pse.readthedocs.io/en/latest/index.html).
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**Learn about mathematical optimization and Pyomo.** IDAES is a state-of-the-art equation-oriented modeling and optimization environment. Below are recommended topics and references:
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* *Mathematical optimization* especially nonlinear programs (optimization problems) and chemical engineering applications. {cite}`Postek2025`, along with the [companion website](https://mobook.github.io/MO-book/intro.html) and [overview video](https://www.youtube.com/watch?v=DPv-7TeSTNs), are the best resources for a user new to mathematical optimization. {cite}`biegler1997systematic`, {cite}`biegler2010nonlinear`, and {cite}`grossmann2021advanced` are excellent references for advanced users. The [Prof. Dowling's course website](https://ndcbe.github.io/optimization/intro.html) includes Jupyter notebooks and Pyomo examples inspired by these texts.
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* *Pyomo*. IDAES is built upon Pyomo, which is an open-source algebraic modeling environment. New users will likely find {cite}`Postek2025` along its [companion website](https://mobook.github.io/MO-book/intro.html) and the [ND Pyomo Cookbook](https://ndcbe.github.io/ND-Pyomo-Cookbook/README.html) as the easiest introduction to Pyomo. Other excellent resources include {cite}`bynum2021pyomo` and the [Pyomo documentation](https://pyomo.readthedocs.io/en/stable/).
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### Getting the source code
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The full source code for these examples is available from the
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[IDAES examples repository](https://github.com/IDAES/examples) on GitHub.

idaes_examples/notebooks/references.bib

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Bethany Nicholson and Carl Laird and Lorenz T. Biegler and Debangsu Bhattacharyya and Nikolaos V. Sahinidis and
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Ignacio E. Grossmann and Chrysanthos E. Gounaris and Dan Gunter},
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keywords = {Multiscale modeling, conceptual design, process optimization, dynamic optimization, parameter estimation}
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}
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@book{Postek2025,
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title = {Hands-On Mathematical Optimization with Python},
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author = { Krzysztof Postek and Alessandro Zocca and Vrije Universiteit Amsterdam and Joaquim A. S. Gromicho},
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publisher = {Cambridge University Press},
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year = {2025},
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isbn = {9781009493505},
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url = {https://www.cambridge.org/us/universitypress/subjects/mathematics/optimization-or-and-risk-analysis/hands-mathematical-optimization-python}
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}
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@book{biegler1997systematic,
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title={Systematic methods of chemical process design},
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author={Biegler, Lorenz T and Grossmann, Ignacio E and Westerberg, Arthur W},
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year={1997},
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publisher={Prentice Hall},
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isbn={9780134924229}
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}

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