Title

Ammonia Decomposition by Catalysis for Application In Hydrogen Fuel Cells

School Name

South Carolina Governor's School for Science & Mathematics

Grade Level

12th Grade

Presentation Topic

Chemistry

Presentation Type

Mentored

Abstract

The goal of the experiment was to find a catalyst that is less expensive, has a high activity, and functions at low temperatures for ammonia decomposition for application in hydrogen fuel cells. In this experiment catalysts were defined, made, and tested for ammonia decomposition. The catalysts, made via incipient wetness impregnation, were ruthenium-based on a gamma-alumina support, that contained a combination of different promoters and metals at different weight percentages. A high-throughput reactor was used in conjunction with machine learning to test a select few of the defined catalysts. With the conversion data yielded from the high throughput testing, the machine learning model selected catalysts with high predicted conversions to test in real-world conditions in a continuous flow reactor. This experiment shows with machine learning that taking into account many aspects of the catalyst allows a model to select higher conversion catalysts. This technique found the 3,1,10 RuHfK catalyst, which is both cheaper and have a higher conversion (35% at 350°C) compared to the control 4% Ru catalyst (5.5% at 350°C). This technique could be applied with more metals and promoters to discover higher activity catalysts and make ammonia a viable storage method for hydrogen fuel cells.

Location

Furman Hall 108

Start Date

3-28-2020 10:45 AM

Presentation Format

Oral Only

Group Project

No

COinS
 
Mar 28th, 10:45 AM

Ammonia Decomposition by Catalysis for Application In Hydrogen Fuel Cells

Furman Hall 108

The goal of the experiment was to find a catalyst that is less expensive, has a high activity, and functions at low temperatures for ammonia decomposition for application in hydrogen fuel cells. In this experiment catalysts were defined, made, and tested for ammonia decomposition. The catalysts, made via incipient wetness impregnation, were ruthenium-based on a gamma-alumina support, that contained a combination of different promoters and metals at different weight percentages. A high-throughput reactor was used in conjunction with machine learning to test a select few of the defined catalysts. With the conversion data yielded from the high throughput testing, the machine learning model selected catalysts with high predicted conversions to test in real-world conditions in a continuous flow reactor. This experiment shows with machine learning that taking into account many aspects of the catalyst allows a model to select higher conversion catalysts. This technique found the 3,1,10 RuHfK catalyst, which is both cheaper and have a higher conversion (35% at 350°C) compared to the control 4% Ru catalyst (5.5% at 350°C). This technique could be applied with more metals and promoters to discover higher activity catalysts and make ammonia a viable storage method for hydrogen fuel cells.