Author Type

Graduate Student

Date of Award

Fall 11-20-2025

Document Type

Thesis

Publication Status

Version of Record

Submission Date

December 2025

Department

Ocean and Mechanical Engineering

Degree Name

Master of Science (MS)

Thesis/Dissertation Advisor [Chair]

Myeongsub Kim

Abstract

Climate change remains one of the most pressing global challenges, with carbon dioxide (CO2) emissions playing a crucial role in exacerbating global warming and ocean acidification. Among emerging mitigation strategies, carbon capture and storage (CCS) stands out as a promising solution. This dissertation explores a novel carbon capture approach utilizing seawater and nickel nanoparticles (NiNPs) stabilized by carboxymethylcellulose (CMC) to enhance CO2 dissolution. Unlike conventional amine-based methods, which are resource-intensive and reliant on freshwater, this approach offers a more sustainable and environmentally friendly alternative. To address the high cost and complexity of experimental CO2 capture studies, a predictive artificial intelligence (AI) framework was developed to estimate CO2 dissolution efficiency as a function of NiNP and CMC concentrations. Because the experimental dataset includes only 24 measurements used for AI algorithm development, a data augmentation technique - monotone spline interpolation - was employed to generate a synthetic dataset of 24,000 points, enabling robust AI model training and improved generalization. Two AI models, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process Regression (GPR), specialized for the limited input datasets, were trained and validated using performance indicators such as the Mean Absolute Percentage Error (𝑀𝐴𝑃𝐸) and the coefficient of determination (𝑅2). ANFIS achieved a 𝑀𝐴𝑃𝐸 of 0.0212 and an 𝑅2 of 0.9880, while GPR achieved a 𝑀𝐴𝑃𝐸 of 0.0015 and an 𝑅2 of 0.9999. A comparative analysis identified the GPR model as the most reliable, with an accuracy of 95.7 %. This work introduces a scalable, data-efficient methodology for predicting CO2 capture performance, supporting the development of more adaptive and sustainable carbon capture technologies.

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