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.

Available for download on Thursday, November 27, 2025

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