AI assisted CO2 capture in biomass CHP plants
Bioenergy with CO2 capture and storage (BECCS) is essential to achieve Sweden’s goal of net-zero CO2 emissions by 2045. The performance of chemical absorption for capturing CO2 from biomass fired CHP plants is clearly influenced by the big fluctuation of flue gas (FG) flowrates and compositions, resulted from versatile biomass and dynamic heat supply.
This project aims to develop artificial intelligence (AI) assisted solutions to optimize the dynamic operation of chemical absorption and its integration in CHP. AI will be used for the prediction of FG flowrates and compositions from the online measurement of input fuel properties, based on which the operation of chemical absorption can be optimized. Through investigating the dynamic interactions between heat supply of district heating and heat demand of CO2 capture, AI will also be used to optimize the global performance of CHP. Such solutions are expected to increase CO2 capture rate, reduce the capture cost, and therefore, promote the application of BECCS.
Start
2021-02-01
Planned completion
2024-12-31
Main financing
Collaboration partners
Research area
Project manager at MDU
Primary purpose of the project:
The overall purpose of this project is to develop artificial intelligence (AI) assisted solutions to optimize and control the dynamic operation of chemical absorption for capturing CO2 from a biomass fired CHP plant. Such solutions are expected to increase CO2 emission reduction and reduce the energy penalty and cost of CO2 capture.
What are the project objectives?
- AI assisted prediction of FG composition and flowrates (led by Dr. Jan Skvaril)
- AI assisted optimization for chemical absorption CO2 capture and bio-CHP (led by Dr. Haoran Zhang)
- Performance evaluation of AI assisted chemical absorption and BECCS (led by Eva Thorin)
What are the project objectives?
- To develop an approach by using deep machine learning techniques for the prediction of FG composition and flowrate based on the online measurement of feedstock and operation parameters of combustion and flue gas cleaning system.
- To optimize the dynamic operation of chemical absorption and develop an advanced control strategy based on AI for the operation of chemical absorption.
- To investigate the interaction between heat supply and CO2 capture in the CHP plant due to the dynamic variation of fuel and heat demand.
- To optimize the integration of CO2 capture in CHP plants by considering different objective functions, such as maximizing the CO2 capture and minimizing the cost of CO2 capture.
In which locations will the project be conducted?
Mälardalen University
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