Prediction of CO₂ adsorption on different activated carbons by hybrid group method of data-handling networks and LSSVM

ABSTRACT

CO₂ is a ubiquitous species that has received much attention recently. The adsorption of CO₂ by means of activated carbons is a well-tried technology that can be used on a large scale. Improvements of the prediction models with more accurate results and lower error are necessary for future development of the projects and the economic dispatch sector. The least square support vector machine, a relatively unexplored neural network known as group method of data handling (GMDH), were implemented to forecast the CO₂ adsorption on different activated carbons. This work aims to provide new methods to predict the adsorption equilibrium of pure CO₂ on a set of commercial activated carbons and to express it regarding textural properties such as Brunauer–Emmett–Teller (BET) surface area, total pore volume, and micropore volume. Results indicated that the utilized models are very accurate in predicting CO₂ adsorption on different activated carbons. Comparison of the outcomes of the two models shows that the GMDH model is more accurate with R² and mean squared error values of 0.8915 and 0.0001425, respectively.

KEYWORDS:

• Activated carbon
• CO2 adsorption
• greenhouse gases reduction
• neural network
• predictive modeling

Acknowledgments

The author is pleased to acknowledge the financial support of this startup project for high-level talents of Guizhou Institute of Technology (XJGC20150102).

Additional information

Funding

This work was supported by the Guizhou Institute of Technology; [XJGC20150102]; Natural Science Research Project of Guizhou Provincial Education Office; Guizhou Province Science and Technology.