Machine learning applications to predict two-phase flow patterns

Recent advances in artificial intelligence with traditional machine learning algorithms and deep learning architectures solve complex classification problems. This work presents the performance of different artificial intelligence models to classify two-phase flow patterns, showing the best alternatives for this specific classification problem using two-phase flow regimes (liquid and gas) in pipes. Flow patterns are affected by physical variables such as superficial velocity, viscosity, density, and superficial tension. They also depend on the construction characteristics of the pipe, such as the angle of inclination and the diameter. We selected 12 databases (9,029 samples) to train and test machine learning models, considering these variables that influence the flow patterns. The primary dataset is Shoham (1982), containing 5,675 samples with six different flow patterns. An extensive set of metrics validated the results obtained. The most relevant characteristics for training the models using Shoham (1982) dataset are gas and liquid superficial velocities, angle of inclination, and diameter. Regarding the algorithms, the Extra Trees model classifies the flow patterns with the highest degree of fidelity, achieving an accuracy of 98.8%.

Citing

If you use our project for your research or if you find this paper and repository helpful, please consider citing the work:

Arteaga-Arteaga HB, Mora-Rubio A, Florez F, Murcia-Orjuela N, Diaz-Ortega CE, Orozco-Arias S, delaPava M, Bravo-Ortíz MA, Robinson M, Guillen-Rondon P, Tabares-Soto R. 2021. Machine learning applications to predict two-phase flow patterns. PeerJ Computer Science 7:e798 https://doi.org/10.7717/peerj-cs.798

@article{Arteaga2021MPF,
 title = {Machine learning applications to predict two-phase flow patterns},
 author = {Arteaga-Arteaga, Harold Brayan and Mora-Rubio, Alejandro and Florez, Frank and Murcia-Orjuela, Nicolas and Diaz-Ortega, Cristhian Eduardo and Orozco-Arias, Simon and delaPava, Melissa and Bravo-Ortíz, Mario Alejandro and Robinson, Melvin and Guillen-Rondon, Pablo and Tabares-Soto, Reinel},
 year = 2021,
 month = nov,
 keywords = {Flow patterns classification, Machine learning, Deep learning, Extra trees, Feature extraction},
 volume = 7,
 pages = {e798},
 journal = {PeerJ Computer Science},
 issn = {2376-5992},
 url = {https://doi.org/10.7717/peerj-cs.798},
 doi = {10.7717/peerj-cs.798}
}

This paper was published as a journal paper in PeerJ Computer Science. (Webpage)

Folders

• 12 DB In this folder, you will find all algorithms and implementations with the '12 DB' dataset. Please see the associated paper.
• Databases This folder contain the databases files for Shoham and '12 DB' for 6, 5, and 3 classes.
• FeatureSelection-DataDistribution-ROC_AUC_CI This folder shows the feature selection process, data distribution, and ROC curves with the AUC, including the CI.
• Shoham In this folder, you will find all algorithms and implementations with the Shoham (1982) dataset. Also, please see the associated paper.

Requirements

This repository requires the following libraries and frameworks:

• TensorFlow
• Matplotlib
• Seaborn
• Scikit-learn
• Yellowbrick
• Bokeh
• NumPy
• Time
• Random
• OS
• XGBoost

This repository was developed in the Python 3 (3.8) programming language.

Package installation

If you don't use google colab, We highly recommend to use and install Python packages within an Anaconda enviroment. To create, execute the command below:

conda create --name MPF python=3.8

So, activate it

conda activate MPF 

Packages installation

pip install ipykernel

and display of enviroment in jupyther

python -m ipykernel install --user --name MPF --display-name "MPF"

installed the framework

conda install tensorflow-gpu

Now, install the libraries.

conda install -c conda-forge matplotlib

conda install -c anaconda seaborn

conda install -c anaconda scikit-learn

conda install -c districtdatalabs yellowbrick

pip install imbalanced-learn

conda install -c bokeh bokeh

conda install -c conda-forge xgboost

Execution

After installing all the Requirements, you must clone the repository using.

git clone https://github.com/BioAITeam/Machine-learning-applications-to-predict-two-phase-flow-patterns.git

If you will use colab, upload the cloned folder to drive, then open the folder and run the notebook of your choice.

if you are going to use your computer, install:

conda install jupyter 

Enter the cloned folder, then enter the folder and run the notebook of your choice.

Note

Before running the notebook, please verify that the file paths are correct.