Heat Integration Model from the PDD Class at ETHZ

This repository contains a Python-based implementation of a heat integration model. The model is derived from the work originally published in:

Yee, T. F., and Grossmann, I. E.,
Simultaneous Optimization of Models for Heat Integration - Heat Exchanger Network Synthesis.
Computers and Chemical Engineering, 14(10), 1151-1184, 1990.

The code in this repository is adapted from the example code (synheat) available in the GAMS model library:
https://www.gams.com/latest/gamslib_ml/libhtml/

Key Features

• Original GAMS Code Compatibility: The base model was originally written in GAMS for mathematical programming.
• Python Integration: This implementation uses the GAMSPy Python package, interacting with the GAMS framework through a container-based structure.
• Flexibility: Easily modify the problem’s mathematical structure and run multiple scenarios in loops.
• Enhanced Data Handling: Store, manipulate, and visualize resulting data seamlessly using Python libraries.

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Getting Started

Modify the settings.py file as needed. Below is a complete example of how to use the SynheatModel class, from instantiation to data saving and plotting.

# Import the SynheatModel class
from synheat import SynheatModel

# Initialize the model
your_model_name = SynheatModel(minimum_heat_flux=False, number_of_stages=4)

# Run the model without integer cuts
your_model_name.run_with_integer_cuts(max_cuts=0)

# Run the model with integer cuts (e.g., 5 cuts)
your_model_name.run_with_integer_cuts(5)

# Print the resulting data
your_model_name.print_data(toLatex=False)

# Retrieve the best result
index_of_best_value, best_result_objective_value, best_z_tuple_table = your_model_name.return_best_result(print_output=True)

# Retrieve the worst result
index_of_worst_value, worst_result_objective_value, worst_z_tuple_table = your_model_name.return_worst_result(print_output=True)

# Save results to a JSON file
your_model_name.save_results_to_json("ENTER YOUR FILEPATH")

# Load results from a JSON file (if file doesn't exist, run with specified cuts and save results)
your_model_name.load_results_from_json("ENTER YOUR FILEPATH")
if not your_model_name.results:
    your_model_name.run_with_integer_cuts(max_cuts=100)
    your_model_name.save_results_to_json("ENTER YOUR FILEPATH")

# Plot the obtained results (optional: save the plot to a file)
your_model_name.plot_results(save_path="ENTER YOUR SAVE PATH")