Volume 37: New Energy, New Ecology and New Environment

AI-based Dynamic Modelling for CO2 Capture Beibei Dong, Jinyu Chen, Xiaodan Shi, Hailong Li



Integrating CO2 capture with biomass/waste fired combined heat and power plants (CHPs) is a promising method to achieve negative emission. However, the use of versatile biomass/waste and dynamic operation of CHPs result in big fluctuations in the flue gas (FG) and heat input to CO2 capture. Dynamic modelling is essential to investigate the interactions between key process parameters in producing the dynamic response of the CO2 capture process. In order to facilitate developing robust control strategies for flexible operation in CO2 capture plants and optimizing the operation of CO2 capture plants, artificial intelligence (AI) models are superior to mechanical models due to the easy implementation into the control and optimization. This paper aims to develop an AI model, Informer, to predict the dynamic responses of MEA based CO2 capture performance from waste-fired CHP plants. Dynamic modelling was first developed in Aspen HYSYS software and validated against the reference. The operation data from the simulated CO2 capture process was then used to develop and verify Informer. The following variables were employed as inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, lean solvent flow rate, heat input to CO2 capture. It was found that Informer could predict CO2 capture rate, reboiler temperature and energy consumption with the mean absolute percentage error of 6.2%, 0.08% and 2.7% respectively.

Keywords artificial intelligence (AI); dynamic modelling; bioenergy with carbon capture and storage (BECCS); combined heat and power (CHP) plants; energy consumption

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