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Global warming is a significant threat to our planet. Adopting the Paris Agreement is a global action that aims to reduce greenhouse gas emissions. An extensive deployment of negative emission technologies (NETs) is required to achieve the targets set by the Paris Agreement. Bioenergy with carbon capture and storage (BECCS) is emerging as one of the most promising NETs. Among different biomass utilization processes, integrating BECCS with biomass-fired and waste-fired combined heat and power (bio-CHP and w-CHP) plants has been considered the most feasible solution. Bio/w-CHP plants are characterized by high fluctuations in operation, which can result in more dynamic variations of flue gas (FG) flowrates and compositions and available heat for CO₂ capture. Such changes can clearly affect the performance of CO₂ capture; therefore, doing dynamic simulations becomes crucial.

This thesis aims to investigate the performance of different dynamic physical model-based approaches and provide suggestions for approach selection. In addition, the data-driven modeling approach, which is an emerging technology, has also been tested.

Three physical model-based approaches include the ideal static model (IST), the dynamic approach without control (Dw/oC), and the dynamic approach with control (DwC). To compare their performance, the operating data from an actual waste CHP plant is employed. Various cases have been defined considering different critical operating parameters, including the FG flowrate, the CO₂ concentration (CO₂vol%), and the available heat for CO₂ capture. Apparent differences can be observed in the results from different approaches. For example, when the CO₂vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, the difference in the captured CO₂ can be up to 22% between DwC and Dw/oC. It is worth noting that when there are both increases and decreases in the variations of parameters, the differences become smaller. 

Based on the comparison, the recommendations on approaches have been summarized. Dw/oC is recommended for checking the boundary of safety operation by the response analysis. DwC is recommended for designing the control system, observing the flexible dynamic operation, estimating the short-term CO₂ capture potential, and optimizing the hourly dynamic operation. IST is recommended for estimating the long-term CO₂ capture potential, and optimizing the long-term dynamic operation when the input parameters vary not as often as hourly.

A data-driven model, Informer, is developed to model CO₂ capture dynamically. The dataset is generated by using a physical model. The FG flowrate, the CO₂vol%, the lean solvent flowrate, and the available heat for CO₂ capture are employed as input parameters, and the CO₂ capture rate and the energy penalty are chosen as outputs. The results show that Informer can accurately predict dynamic CO₂ capture. The mean absolute percentage error (MAPE) was found to be 6.2% and 2.7% for predicting the CO₂ capture rate and energy penalty, respectively.