# Pgraph : Process Graphs for Process Network Synthesis (PNS)
[](https://zenodo.org/badge/latestdoi/507569838)
<!-- TABLE OF CONTENTS -->
## Table of Contents
* [About the Project](#about-the-project)
* [Getting Started](#getting-started)
* [Usage Examples](#usage-examples)
* [Contributing](#contributing)
* [License](#license)
* [Contact](#contact)
* [References](#references)
<!-- ABOUT THE PROJECT -->
## About The Project
This project aims at enabling the classical P-graph Framework (www.p-graph.org) to interface with modern Python programming ecosystems. The backend solver is the original executable from P-graph, staying true to the original implementation of P-graph. For manual network manipulation, the P-graph studio can be downloaded from this link: https://p-graph.org/downloads/.
# Changelog
14/11/2024: Due to update of P-graph studio, mutual exclusion is not properly processed. This is now fixed. Custom solvers can now also be selected for advanced users.
18/10/2024: Due to update of networkx draw, older versions of Pgraph crashes when it draws the graph. This has now been fixed. Users now need to adjust axis size themselves.
<!-- GETTING STARTED -->
## Getting Started
Install this library either from the official PyPI or from this Github repository:
## Install a Stable Version (PyPI)
```bat
pip install ProcessGraph
```
## Install most updated version from Github
In a environment terminal or CMD:
```bat
pip install git+https://github.com/tsyet12/Pgraph
```
<!-- USAGE EXAMPLES -->
## Usage Examples
See [`examples`](https://github.com/tsyet12/Pgraph/tree/main/examples) for all code examples.
### Simple Example
```python
from Pgraph.Pgraph import Pgraph #This is our Pgraph library
import networkx as nx
import matplotlib.pyplot as plt
##### STEP 1 : Problem Specification ######
G = nx.DiGraph()
G.add_node("M1",names="Product D",type='product',flow_rate_lower_bound=100, flow_rate_upper_bound=100)
G.add_node("M2",names="Chemical A",type='raw_material',price=200,flow_rate_lower_bound=0)
G.add_node("M3",names="Chemical B", type='raw_material',price=100,flow_rate_lower_bound=0)
G.add_node("M4",names="Chemical C", type='raw_material',price=10,flow_rate_lower_bound=0)
G.add_node("O1",names="Reactor 1",fix_cost=2000, proportional_cost=400)
G.add_node("O2", names="Reactor 2",fix_cost=1000, proportional_cost=400)
G.add_edge("M2","O1", weight = 1)
G.add_edge("M3","O2", weight = 1)
G.add_edge("M4","O2", weight = 2)
G.add_edge("O1","M1", weight = 0.7)
G.add_edge("O2","M1", weight = 0.9)
ME=[["O1","O2"]] #Reactor 1 and Reactor 2 are mutually excluded. Only one can be chosen as solution.
#### Step 2: Setup Solver ####
P=Pgraph(problem_network=G, mutual_exclusion=ME, solver="INSIDEOUT",max_sol=100)
#### Step 2.1: Plot Problem #####
ax1=P.plot_problem(figsize=(5,5))
ax1.set_xlim(0,200)
plt.show()
##################################
```
```python
#### Step 3: Run ####
P.run()
#### Step 3.1: Plot Solution########
total_sol_num=P.get_sol_num()
for i in range(total_sol_num): # Here we loop through all the solutions to plot everything
ax=P.plot_solution(sol_num=i) #Plot Solution Function
ax.set_xlim(0,200)
plt.show()
```
```python
#### Step 3.2: Export to P-graph Studio ####
from google.colab import files #This is only for google colab
string = P.to_studio(path='./',verbose=False) #export to p-graph studio
files.download("./studio_file.pgsx") #download for google colab
#Note: Please be reminded to press "Generate Layout" button in P-graph Studio after opening
```
Press "Generate Layout" Button:
<!-- CONTRIBUTING -->
## Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b testbranch/prep`)
3. Commit your Changes (`git commit -m 'Improve testbranch/prep'`)
4. Push to the Branch (`git push origin testbranch/prep`)
5. Open a Pull Request
<!-- LICENSE -->
## License
Distributed under the Open Sourced BSD-2-Clause License. See [`LICENSE`](https://github.com/tsyet12/Pgraph/blob/main/LICENSE) for more information.
<!-- CONTACT -->
## Contact
Main Developer:
Sin Yong Teng sinyong.teng@ru.nl or tsyet12@gmail.com
Radboud University Nijmegen
<!-- References -->
## References
Teng, S.Y., Orosz, Á., How, B.S., Pimentel, J., Friedler, F. and Jansen, J.J., 2022. Framework to Embed Machine Learning Algorithms in P-graph: Communication from the Chemical Process Perspectives. Chemical Engineering Research and Design. (Paper explaining the software)
Friedler, F., Tarjan, K., Huang, Y.W. and Fan, L.T., 1992. Graph-theoretic approach to process synthesis: axioms and theorems. Chemical Engineering Science, 47(8), pp.1973-1988.
Friedler, F., Tarjan, K., Huang, Y.W. and Fan, L.T., 1992. Combinatorial algorithms for process synthesis. Computers & chemical engineering, 16, pp.S313-S320.
Friedler, F., Tarjan, K., Huang, Y.W. and Fan, L.T., 1993. Graph-theoretic approach to process synthesis: polynomial algorithm for maximal structure generation. Computers & Chemical Engineering, 17(9), pp.929-942.
## Acknowledgements
The research contribution from S.Y. Teng is supported by the European Union's Horizon Europe Research and Innovation Program, under Marie Skłodowska-Curie Actions grant agreement no. 101064585 (MoCEGS).
## How to cite this software
S.Y. Teng (2022). tsyet12/Pgraph: Process Graphs for Process Network Synthesis (PNS), Zenodo Release 2 (v1.0-zenodo-2). Zenodo. https://doi.org/10.5281/zenodo.6778354
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"description": "# Pgraph : Process Graphs for Process Network Synthesis (PNS)\n\n\n[](https://zenodo.org/badge/latestdoi/507569838)\n\n<!-- TABLE OF CONTENTS -->\n## Table of Contents\n\n* [About the Project](#about-the-project)\n* [Getting Started](#getting-started)\n* [Usage Examples](#usage-examples)\n* [Contributing](#contributing)\n* [License](#license)\n* [Contact](#contact)\n* [References](#references)\n\n\n<!-- ABOUT THE PROJECT -->\n## About The Project\nThis project aims at enabling the classical P-graph Framework (www.p-graph.org) to interface with modern Python programming ecosystems. The backend solver is the original executable from P-graph, staying true to the original implementation of P-graph. For manual network manipulation, the P-graph studio can be downloaded from this link: https://p-graph.org/downloads/. \n\n# Changelog\n14/11/2024: Due to update of P-graph studio, mutual exclusion is not properly processed. This is now fixed. Custom solvers can now also be selected for advanced users.\n\n18/10/2024: Due to update of networkx draw, older versions of Pgraph crashes when it draws the graph. This has now been fixed. Users now need to adjust axis size themselves.\n\n\n<!-- GETTING STARTED -->\n## Getting Started\n\nInstall this library either from the official PyPI or from this Github repository:\n\n## Install a Stable Version (PyPI)\n```bat\npip install ProcessGraph\n```\n## Install most updated version from Github\n\nIn a environment terminal or CMD:\n```bat\npip install git+https://github.com/tsyet12/Pgraph\n```\n\n\n<!-- USAGE EXAMPLES -->\n## Usage Examples\n\nSee [`examples`](https://github.com/tsyet12/Pgraph/tree/main/examples) for all code examples.\n\n### Simple Example\n```python\nfrom Pgraph.Pgraph import Pgraph #This is our Pgraph library\nimport networkx as nx\nimport matplotlib.pyplot as plt\n##### STEP 1 : Problem Specification ######\nG = nx.DiGraph()\nG.add_node(\"M1\",names=\"Product D\",type='product',flow_rate_lower_bound=100, flow_rate_upper_bound=100)\nG.add_node(\"M2\",names=\"Chemical A\",type='raw_material',price=200,flow_rate_lower_bound=0)\nG.add_node(\"M3\",names=\"Chemical B\", type='raw_material',price=100,flow_rate_lower_bound=0)\nG.add_node(\"M4\",names=\"Chemical C\", type='raw_material',price=10,flow_rate_lower_bound=0)\nG.add_node(\"O1\",names=\"Reactor 1\",fix_cost=2000, proportional_cost=400)\nG.add_node(\"O2\", names=\"Reactor 2\",fix_cost=1000, proportional_cost=400)\nG.add_edge(\"M2\",\"O1\", weight = 1)\nG.add_edge(\"M3\",\"O2\", weight = 1)\nG.add_edge(\"M4\",\"O2\", weight = 2)\nG.add_edge(\"O1\",\"M1\", weight = 0.7) \nG.add_edge(\"O2\",\"M1\", weight = 0.9) \nME=[[\"O1\",\"O2\"]] #Reactor 1 and Reactor 2 are mutually excluded. Only one can be chosen as solution.\n\n#### Step 2: Setup Solver ####\nP=Pgraph(problem_network=G, mutual_exclusion=ME, solver=\"INSIDEOUT\",max_sol=100)\n\n#### Step 2.1: Plot Problem #####\nax1=P.plot_problem(figsize=(5,5))\nax1.set_xlim(0,200)\nplt.show()\n##################################\n```\n\n\n\n```python\n#### Step 3: Run ####\nP.run()\n#### Step 3.1: Plot Solution########\ntotal_sol_num=P.get_sol_num() \nfor i in range(total_sol_num): # Here we loop through all the solutions to plot everything\n ax=P.plot_solution(sol_num=i) #Plot Solution Function\n ax.set_xlim(0,200)\n plt.show()\n```\n\n\n\n\n```python\n#### Step 3.2: Export to P-graph Studio ####\nfrom google.colab import files #This is only for google colab\nstring = P.to_studio(path='./',verbose=False) #export to p-graph studio\nfiles.download(\"./studio_file.pgsx\") #download for google colab\n#Note: Please be reminded to press \"Generate Layout\" button in P-graph Studio after opening\n```\n\nPress \"Generate Layout\" Button:\n\n\n\n\n<!-- CONTRIBUTING -->\n## Contributing\n\nContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b testbranch/prep`)\n3. Commit your Changes (`git commit -m 'Improve testbranch/prep'`)\n4. Push to the Branch (`git push origin testbranch/prep`)\n5. Open a Pull Request\n\n\n<!-- LICENSE -->\n## License\n\nDistributed under the Open Sourced BSD-2-Clause License. See [`LICENSE`](https://github.com/tsyet12/Pgraph/blob/main/LICENSE) for more information.\n\n\n<!-- CONTACT -->\n## Contact\nMain Developer:\n\nSin Yong Teng sinyong.teng@ru.nl or tsyet12@gmail.com\nRadboud University Nijmegen\n\n<!-- References -->\n## References\n\nTeng, S.Y., Orosz, \u00c1., How, B.S., Pimentel, J., Friedler, F. and Jansen, J.J., 2022. Framework to Embed Machine Learning Algorithms in P-graph: Communication from the Chemical Process Perspectives. Chemical Engineering Research and Design. (Paper explaining the software)\n\nFriedler, F., Tarjan, K., Huang, Y.W. and Fan, L.T., 1992. Graph-theoretic approach to process synthesis: axioms and theorems. Chemical Engineering Science, 47(8), pp.1973-1988.\n\nFriedler, F., Tarjan, K., Huang, Y.W. and Fan, L.T., 1992. Combinatorial algorithms for process synthesis. Computers & chemical engineering, 16, pp.S313-S320.\n\nFriedler, F., Tarjan, K., Huang, Y.W. and Fan, L.T., 1993. Graph-theoretic approach to process synthesis: polynomial algorithm for maximal structure generation. Computers & Chemical Engineering, 17(9), pp.929-942.\n\n\n## Acknowledgements\nThe research contribution from S.Y. Teng is supported by the European Union's Horizon Europe Research and Innovation Program, under Marie Sk\u0142odowska-Curie Actions grant agreement no. 101064585 (MoCEGS).\n\n\n## How to cite this software\n\nS.Y. Teng (2022). tsyet12/Pgraph: Process Graphs for Process Network Synthesis (PNS), Zenodo Release 2 (v1.0-zenodo-2). Zenodo. https://doi.org/10.5281/zenodo.6778354\n",
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