Predicting Methanol Space-Time Yield from CO? Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques

Harun Al Azies (Universitas Dian Nuswantoro)
Muhamad Akrom (Universitas Dian Nuswantoro)
Setyo Budi (Universitas Dian Nuswantoro)
Gustina Alfa Trisnapradika (Universitas Dian Nuswantoro)
Aprilyani Nur Safitri (Universitas Dian Nuswantoro)

International Journal of Advances in Data and Information Systems
Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO? hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.