Add Hyperplane Tree #20
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* Change terminal storage amounts to input parameters Move terminal storage amounts (days) for compressed and liquid hydrogen to dictionary of input parameters for the "calcs" function. Update "input_params" Excel file. * Update s2a_systems_functions.py Remove unused parameter. * Update s2a_systems_functions.py Set energy, cost, and emissions for CO2 recycling and downstream gas separation (precooling for PSA) to zero if no CO2 is used or produced for a given LOHC. * Update s2a_systems_functions.py Remove list() from argument to 2D interpolation function so that return value is array of number (instead of array of list). * Fix electrolyzer fixed costs and energy Use terminal LOHC mass flowrate (instead of molar flowrate) as the target flowrate for scaling LOHC electrolyzer area and energy. Fix both code and input Excel file. (Note: Previously mixed up molar and mass flowrates. Formic acid results were not impacted because of a similar mismatch in input Excel file.) --------- Co-authored-by: Mengyao Yuan <[email protected]> Co-authored-by: Mengyao Yuan <[email protected]>
* Change terminal storage amounts to input parameters Move terminal storage amounts (days) for compressed and liquid hydrogen to dictionary of input parameters for the "calcs" function. Update "input_params" Excel file. * Update s2a_systems_functions.py Remove unused parameter. * Update s2a_systems_functions.py Set energy, cost, and emissions for CO2 recycling and downstream gas separation (precooling for PSA) to zero if no CO2 is used or produced for a given LOHC. * Update s2a_systems_functions.py Remove list() from argument to 2D interpolation function so that return value is array of number (instead of array of list). * Fix electrolyzer fixed costs and energy Use terminal LOHC mass flowrate (instead of molar flowrate) as the target flowrate for scaling LOHC electrolyzer area and energy. Fix both code and input Excel file. (Note: Previously mixed up molar and mass flowrates. Formic acid results were not impacted because of a similar mismatch in input Excel file.) --------- Co-authored-by: Mengyao Yuan <[email protected]> Co-authored-by: Mengyao Yuan <[email protected]>
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This PR introduces Hyperplane Trees, a powerful tool for building piecewise linear surrogate models from data. This was initially a fork of Marco Cerliani's Linear-Tree, but it has since grown to include a new implementation of the training algorithm in pytorch, as well as feature engineering tools for linear and quadratic combinations. For more details about this code, please see the Readme for the appropriate module.
This code is the main work of the S2A Surrogates team for Year 2 of the project. We are pushing this into the main branch now as we plan to submit this work for publication in the coming days.