Prediction of CO2 uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence

Mohd Azfar Shaida, Saad Shamim Ansari*, Raeesh Muhammad, Syed Muhammad Ibrahim, Izharul Haq Farooqi, Abdulkarem Amhamed

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study introduces comprehensive research on the prediction of the carbon dioxide (CO2) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. It aims to identify the main characteristics, and trends that are specific to this domain, and to establish, compare and analyse the four different black box machine learning (ML) models for CO2 uptake prediction. For this study, through model evaluation parameters, and scatter plots, statistical analysis supports the fact that the Extreme Gradient Boosting (XGBoost) model is found to be the best performing model for CO2 uptake prediction with low errors and high coefficient of correlation for both training (MSE: 0.157, RMSE: 0.397, MAE: 0.294, MAPE: 0.112, R2: 0.931) and testing phases (MSE: 0.345, RMSE: 0.588, MAE: 0.461, MAPE: 0.121, R2: 0.860). Now, with the best performing black box ML model as XGBoost model, it serves as the basis for the multi-layered XAI analysis. Using multi-layered XAI techniques to interpret the black box ML model and covert it to a white box model, it makes clearer insights into the significant key features that affect the CO2 uptake both at the global and local level. The study demonstrates that using multi-layered XAI analysis helps in improving the trust of the predictive model and provides a way forward for the application of white box models in CO2 uptake.

Original languageEnglish
Article number133183
JournalFuel
Volume380
DOIs
Publication statusPublished - 15 Jan 2025

Keywords

  • Biomass waste
  • CO2 uptake
  • Explainable artificial intelligence
  • Machine learning
  • Scientometrics

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