DRM-ML-Project

This Repository contains the Juypter Notebooks and Spreadsheet developed for the article "Impacts of Catalyst and Process Parameters on Ni-Catalyzed Methane Dry Reforming via Interpretable Machine Learning"

Notebooks for model interpretation

The main notebooks used for machine learning models are "CatalysisML_Final_Syngas.ipynb" and "CatalysisML_Final_Conversion.ipynb". These notebooks are used to generate the predictions and PDP plots The "CatalysisML_Trajectory.ipynb" Notebook contains codes to plot the trajectory plots

Notebooks for Data Analysis

Other notebooks used are "CatalysisML_Final_DataAnalysis.ipynb" and "CatalysisML_Final_Apsenplt.ipynb" They are used to generate other plots for data analysis and the surface plots from the Aspen Simulations

Dataset: SpreadSheets and CSV files

The spreadsheets for the Final Data are presents in both .xlsx and .csv formats The "Simulation" spreadsheets contains results from Aspen Simulations. The "trend.csv" file contains the number of past literature papers published on DRM

Use of resources

Please cite our article as:

K. Vellayappan, Y. Yue, K.H. Lim, K. Cao, J.Y. Tan, S. Cheng, T. Wang, T.Z.H. Gani, I.A. Karimi, S. Kawi.
"Impacts of Catalyst and Process Parameters on Ni-Catalyzed Methane Dry Reforming via Interpretable Machine Learning", Applied Catalysis B: Environmental , 2023 ,https://doi.org/10.1016/j.apcatb.2023.122593.