Microturbine Decarbonization with Machine-Learning Regression Modeling
Session Number
Project ID: CMPS 03
Advisor(s)
Dr. Chandrachur Bhattacharya; Argonne National Laboratory
Dr. Prasanna Balaprakash; Argonne National Laboratory
Discipline
Computer Science
Start Date
19-4-2023 10:05 AM
End Date
19-4-2023 10:20 AM
Abstract
Authorities all across the world are trying to minimize carbon dioxide (CO2) emissions, and this decarbonization step is necessary if the global climate issue is to be resolved. Researchers at Argonne have modified a natural gas-burning microturbine to burn natural gas-hydrogen fuel blends with the aim to reduce CO2 emissions. Emissions and efficiency data are obtained via experiments performed at Argonne. This research project demonstrates the use of machine learning regression approaches to build a data-driven model of the microturbine system. The collected experimental data is used to train the machine learning models. This is then used to determine the optimal operational parameters of the microturbine that minimize harmful emissions while maximizing efficiency. This study determines how best to model the trends in the data, and it evaluates the effectiveness of diverse design strategies for a microturbine. This project also determines the extent to which CO2, CO, and NOx emissions are altered when hydrogen is used as a substitute for natural gas and will lead to future decarbonization prospects.
Microturbine Decarbonization with Machine-Learning Regression Modeling
Authorities all across the world are trying to minimize carbon dioxide (CO2) emissions, and this decarbonization step is necessary if the global climate issue is to be resolved. Researchers at Argonne have modified a natural gas-burning microturbine to burn natural gas-hydrogen fuel blends with the aim to reduce CO2 emissions. Emissions and efficiency data are obtained via experiments performed at Argonne. This research project demonstrates the use of machine learning regression approaches to build a data-driven model of the microturbine system. The collected experimental data is used to train the machine learning models. This is then used to determine the optimal operational parameters of the microturbine that minimize harmful emissions while maximizing efficiency. This study determines how best to model the trends in the data, and it evaluates the effectiveness of diverse design strategies for a microturbine. This project also determines the extent to which CO2, CO, and NOx emissions are altered when hydrogen is used as a substitute for natural gas and will lead to future decarbonization prospects.