Date of Award

December 2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Energy Engineering

First Advisor

Olusegun S. Tomomewo

Abstract

Natural gas flaring is a persistent challenge which undermines global sustainability and decarbonization efforts as it leads to the waste of valuable energy resources alongside the release of harmful greenhouse gases into the environment. Even when natural gas is utilized through commonly adopted pathways, significant emission of carbon dioxide tends to be realized. Methane pyrolysis offers a promising route in which methane can be decomposed into solid carbon and hydrogen gas with no direct CO2 emissions. However, despite extensive studies of the methane pyrolysis process, a fundamental thermodynamic rationale of the process behavior remains insufficiently explored. This study 1) develops a fundamental thermodynamic framework to explain fully the mechanisms driving the thermal methane pyrolysis (TMP) process, 2) isolates and quantifies the enthalpy(∆H) and entropy (T∆S) contributions to process spontaneity and conversion, and 3) thermodynamically rationalizes prevalent process optimization strategies. Results confirm the TMP process to be endothermic and entropy-driven, with temperature as the dominant driver of the process through amplification of the T∆S term, while pressure suppresses spontaneity via entropy reduction. The optimal operating regime was found to be at 900 °C, 1 bar, where methane conversion reaches ~94%, after which diminishing energy returns were observed. The optimal operating regime was observed to change to 1200 °C, 1 bar with the implementation of heat recovery. The thermodynamic analysis validated the adoption of current process configurations as preheating and heat recovery were found to minimize external heat duty, and reactor insulation maintained the entropy-driven conditions that favor conversion. This work bridges the gap between fundamental thermodynamics and practical engineering, providing a foundational baseline for more complex models and analysis.

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