Author ORCID Identifier

https://orcid.org/0000-0001-7817-1270

Semester

Spring

Date of Graduation

2025

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Chemical and Biomedical Engineering

Committee Chair

Tian Yuhe

Committee Co-Chair

Lima Fernando

Committee Member

Palanki Srinivas

Committee Member

Pistikopoulos Efstratios

Committee Member

Li Wenyuan

Abstract

There has been an ongoing trend toward cleaner fuels for more environmentally benign forms of energy generation. Recently, hydrogen has been identified as a clean fuel for energy generation. However, the most established, efficient, and widely utilized technique for producing hydrogen remains the steam methane reforming (SMR) in the presence of catalyst. Unlike other fossil fuels, utilizing methane from natural gas in an SMR process to produce hydrogen emits less, but yet a notable sum of carbon dioxide. Furthermore, the endothermic nature of SMR reactions imply that there is a high energy requirement to obtain a substantial amount of hydrogen from the conversion of methane during the process. Lastly, conventional SMR process utilizes a series of unit operations to ensure that the purity of hydrogen meets the set standards. Owing to this, research continues to investigate how to improve the SMR process. Membrane reactors (MRs) have been developed based on the principle of process intensification to improve the performance of SMR. Specifically, MRs are noted for their ability to improve the equilibrium conversion of methane as well as the production of highly purified hydrogen, especially with the use of Palladium-based membranes, such as Pd-Ag membrane.

Owing to this, a one-dimensional pseudo-homogeneous model of a Pd-Ag MR is developed in this dissertation for the SMR process. The developed model is first validated using data from open literature. Simulation is then performed to evaluate the performance of the process. Specifically, the considered performance metrics are methane conversion, hydrogen yield, carbon dioxide purity, and hydrogen recovery. Based on a literature survey on existing SMR MR modeling studies, a research gap lies in how the interaction of the operating variables impacts the performance of the process, especially with the incorporation of environmental goals in term of carbon dioxide purity. Given this, a sensitivity study is carried out to offer a robust understanding of the impact of the interaction of operating temperature, pressure, and steam-to-carbon ratio on the process performance.

Optimization studies are efficient for realizing the optimal performance of a reactor. Nonetheless, there are limited studies on the multi-objective optimization (MOO) of an SMR in a Pd-Ag-MR. Besides, to the best of our knowledge, no MOO study has considered the sizing of an industrial-scale MR as a decision variable. This study contributes to existing knowledge in the field by incorporating system performance goals (methane conversion, hydrogen permeation rate, and hydrogen recovery), environmental goal (carbon dioxide purity) and economic goal (membrane cost) during the MOO study. Specifically, the MOO scenarios considered are: (i) the maximization of methane conversion and minimization of membrane cost (ii) the maximization of hydrogen permeation rate and minimization of membrane cost (iii) the maximization of carbon dioxide purity and minimization of membrane cost (iv) the maximization of hydrogen recovery and minimization of membrane cost.

The results obtained from modeling, simulation and sensitivity studies show that MRs are efficient for improving the performance of an SMR process. When compared to equilibrium-governed simulations, a Pd-Ag-MR improved the conversion of methane by 51%, hydrogen yield by 54%, and carbon dioxide purity by 163%. Lastly, a Pareto front that consists of a set of Pareto-optimal solutions for each of the MOO scenarios is obtained. In conclusion, this work contributes to the existing field of knowledge in this domain by carrying out the modeling, simulation, sensitivity analysis, and multi-objective optimization of SMR using a Pd-Ag-MR, together with the integration of environmental and economic goals, towards achieving sustainable hydrogen production.

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