Date of Award
2024
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Chemical Engineering (MS)
Administrative Home Department
Department of Chemical Engineering
Advisor 1
Lei Pan
Committee Member 1
Kaiwu Huang
Committee Member 2
Robert Handler
Abstract
The climate-altering effects of atmospheric CO2 make it necessary to develop innovative technologies that can remove it from the atmosphere. Mineral carbonation is a scalable technology to sequestrate and store CO2 in a stable carbonate mineral form. The efficiency of the mineral carbonation process depends on both feed mineralogy and process parameters. Comprehensive modeling and prediction of mineral carbonation efficiency for different CO2 – reactive rocks has not been achieved in the past. In this thesis work, analytical and machine learning models are developed to predict the carbonation efficiency of dunite reactions. It is found that random forest can accurately predict the carbonation efficiency of these reactions with a RMSE of 4.05. These models are then applied to predict the carbonation efficiency of mine-tailings reactions. This analysis shows that an ensemble of gradient boost and decision tree accurately predicts carbonation efficiency with a RMSE of 2.45.
Recommended Citation
Hanson, William, "ANALYTICAL AND MACHINE LEARNING MODELING OF DIRECT CARBONATION OF NATURAL SILICATE MINERALS", Open Access Master's Thesis, Michigan Technological University, 2024.