Generalized Superstructure for recovering rare earth elements from an end-of-life product.

[ChrisLaliwala]

Addresses Issue:

Summary/Motivation:

The PR introduces a generalized superstructure function that can be used to find the optimal processing pathway for recovering critical minerals from different end-of-life (EOL) products based on user-specified inputs. The user has the option to maximize the net present value (NPV), or minimize the cost of recovery (COR). Additionally, the user can also consider the valorization of byproducts, and the ability to perform multi-objective optimization considering the LCA emission metric of Global Warming Potential.

Changes proposed in this PR:

• Generalized superstructure function

Reviewer's checklist / merge requirements:

• The head branch (i.e. the "source" of the changes) is not the main branch on the PR author's fork
• Documentation
• Tests
• Diagnostic tests for models

Legal Acknowledgement

By contributing to this software project, I agree to the following terms and conditions for my contribution:

1. I agree my contributions are submitted under the license terms described in the LICENSE.md file
   at the top level of this directory.

2. I represent I am authorized to make the contributions and grant the license. If my employer has
   rights to intellectual property that includes these contributions, I represent that I have
   received permission to make contributions and grant the required license on behalf of that
   employer.

[codecov]

Codecov Report

❌ Patch coverage is 99.67923% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 85.46%. Comparing base (5be8de8) to head (af8f97b).
⚠️ Report is 21 commits behind head on main.

Files with missing lines	Patch %	Lines
...is/superstructure/report_superstructure_results.py	98.88%	0 Missing and 3 partials ⚠️
...mmis/superstructure/check_superstructure_inputs.py	99.75%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #136      +/-   ##
==========================================
+ Coverage   83.07%   85.46%   +2.38%     
==========================================
  Files          62       67       +5     
  Lines        7422     8669    +1247     
  Branches      818     1150     +332     
==========================================
+ Hits         6166     7409    +1243     
  Misses       1078     1078              
- Partials      178      182       +4     

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[agarciadiego]

Couple of typo fixing but other than that looks good to me

[agarciadiego]

Also please check if you can increase coverage since the code coverage overall is low. @OOAmusat can you help review coverage?

[ChrisLaliwala]

Also please check if you can increase coverage since the code coverage overall is low. @OOAmusat can you help review coverage?

@agarciadiego This has to do with Gurobi solver not being available on GitHub repository (so the tests are skipped). When I run the tests locally, the code has 100% coverage.

[agarciadiego]

Also please check if you can increase coverage since the code coverage overall is low. @OOAmusat can you help review coverage?

@agarciadiego This has to do with Gurobi solver not being available on GitHub repository (so the tests are skipped). When I run the tests locally, the code has 100% coverage.

But we could test a lot of lines of code (i.e., the existence and values of the parameters) without testing the solve.

[ChrisLaliwala]

Also please check if you can increase coverage since the code coverage overall is low. @OOAmusat can you help review coverage?

@agarciadiego This has to do with Gurobi solver not being available on GitHub repository (so the tests are skipped). When I run the tests locally, the code has 100% coverage.

But we could test a lot of lines of code (i.e., the existence and values of the parameters) without testing the solve.

The uncovered code was all from the file "report_superstructure_results.py". This function takes in a solved model and outputs the results to the console in a way that is easy for the user to interpret. Since Gurobi is not available on the GitHub repository, all the tests were skipped for this file, leading to poor code coverage. I have added the test file - "test_report_superstructure_results_no_model_solve.py", which tests "report_superstructure_results.py" by making a mock model. This allows full code coverage of "report_superstructure_results.py" without the need for solving the model.

The Codecov report now shows that tests fully cover all code. All remaining missing coverage is in the test files (which I don't think Codecov should be including when calculating code coverage).

[agarciadiego]

Also please check if you can increase coverage since the code coverage overall is low. @OOAmusat can you help review coverage?

@agarciadiego This has to do with Gurobi solver not being available on GitHub repository (so the tests are skipped). When I run the tests locally, the code has 100% coverage.

But we could test a lot of lines of code (i.e., the existence and values of the parameters) without testing the solve.

The uncovered code was all from the file "report_superstructure_results.py". This function takes in a solved model and outputs the results to the console in a way that is easy for the user to interpret. Since Gurobi is not available on the GitHub repository, all the tests were skipped for this file, leading to poor code coverage. I have added the test file - "test_report_superstructure_results_no_model_solve.py", which tests "report_superstructure_results.py" by making a mock model. This allows full code coverage of "report_superstructure_results.py" without the need for solving the model.

The Codecov report now shows that tests fully cover all code. All remaining missing coverage is in the test files (which I don't think Codecov should be including when calculating code coverage).

Thank you, the coverage looks a lot better