Simulation and machine learning assisted discovery of performance enhancement for CO2 reduction electrolyser and fuel cell

Lei, Hanhui (2024) Simulation and machine learning assisted discovery of performance enhancement for CO2 reduction electrolyser and fuel cell. Doctoral thesis, Northumbria University.

Text (Doctoral thesis) lei.hanhui_phd(19035122).pdf - Submitted Version Restricted to Repository staff only until 22 August 2024. Download (7MB) | Request a copy

Abstract

The escalation of carbon emissions poses a significant challenge for societies across the globe. As the levels of CO2 continue to surge, they create a greenhouse effect that traps heat in the Earth's atmosphere, leading to a gradual rise in the planet's temperature. reducing carbon emissions and developing new energy sources such as hydrogen are the solutions with visible benefits and considerable potential.

This study explores the simulation application in the electrochemical catalysis of CO2RR and Porton Exchange membrane Fuel Cell (PEMFC). CO2RR is a process that generates low-carbon compounds, including CO and formic acid, which have considerable economic potential. Meanwhile, PEMFCs are known for their high efficiency, quick start-up, and relative ease of operation, making them a commercially viable option. Despite these advantages, these technologies face several challenges, including low electrocatalyst activity, mass transfer limitations, and low reaction rates, which have impeded their large-scale adoption. To overcome these hurdles and make these technologies viable for industrial use, eCO2RR must address issues such as poor catalyst selectivity, durability, and resistance to hydrogen evolution reactions.

In Chapter 3, we studied the impact of different GDL structures on eCO2RR. Three cell types were designed: a carbon paper (CP) GDL type cell, a graphene aerogels (GA) GDL type cell, and a combined CP and GA GACP type cell. The study showed that GDL structural variations significantly influenced CO2RR efficiency. The FE increased from 60% to 94% (CP cell to GACP cell). The FE reduction relative to the Reversible Hydrogen Electrode (RHE) was approximately 65 times less. Highly selective catalysts can achieve 100% FE and high current density.

Chapter 4 presented the impact of different catalyst structures on eCO2RR. Morphological changes in the catalyst effectively increased the porosity of all three interfaces and the total contact area, significantly enhancing CO2 mass transfer. In 1M KOH, at a voltage of -1.0 V relative to the RHE, the highest Faradaic efficiency for CO reached 93.20%.

Chapter 5 examined the industrial aspects of Chapters 3 and 4. Simulation results showed that the thickness of the catalyst layer had the most significant impact on CO2 concentration during the cell scaling-up process. Optimal parameters achieved a CO2 consumption rate of over 98%.

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