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Integrating CO2 capture with biomass/waste fired combined heat and power (CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass/waste and the dynamic operation of CHP plants result in bigger fluctuations in the properties of flue gas (FG), e.g. CO2 concentration (CO2vol%) and flowrates, and the heat that can be used for CO2 capture, when comparing with coal fired power plants. To address such a challenge, dynamic modelling is essential to accurately estimate the amount of captured CO2 and optimize the operation of CO2 capture. This paper compares three dynamic approaches commonly used in literature, namely using the ideal static model (IST) and using dynamic models without control (Dw/oC) and with control (DwC), for MEA based chemical absorption CO2 capture. The performance of approaches is assessed under the variations of key factors, including the flowrate and CO2vol% of FG, and the available heat for CO2 capture. Simulation results show clear differences. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, DwC gives the highest amount of captured CO2, which is 7.3 % and 22.3 % higher than IST and Dw/oC, respectively. It is also found that the time step size has a clear impact on the CO2 capture amount, especially for DwC. Based on the results, suggestions are also provided regarding the selection of dynamic modelling approaches for different purposes of simulations.

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 and energy consumption with the mean absolute percentage error of 6.2% and 2.7% respectively.

Accurate dynamic modelling of CO2 capture is essential for real time process integration, control and optimization. This work aims to investigate the feasibility of using machine learning (ML) methods for different purposes of dynamic modelling of CO2 capture. Since the development of ML models relies on the selection of input features, the key input parameters are first reviewed and determined based on requirements of various dynamic model applications. Four cases are covered in this work: system identification for control development, system monitoring and diagnosis, operation optimization and system performance assessment. Three ML methods, Informer, Long ShortTerm Memory (LSTM) and Backpropagation Neural Network (BPNN), are used. The data needed for ML model development are generated by using a validated physical dynamic model developed in Aspen HYSYS Dynamics, based on real data of flue gas obtained from a waste fired combined heat and power plant. It was found that with selected input parameters, ML models can achieve high accuracy for all cases, with mean absolute percentage errors (MAPEs) less than 5%. No single model outperforms the others across all cases.

The integration of CO2 capture with biomass-fired power plants has attracted much attention due to its ability to achieve negative emissions. Waste-fired combined heat and power (CHP) plants with CO2 capture, on the other hand, has received little attention, and their potential remains unclear. This study aims to identify the possible range of the amount of captured CO2 and investigate the impact of CO2 capture on the performance of waste-fired CHP plants. Since heat is the primary product of CHP plants, it is important to maintain heat production unchanged when CO2 capture is integrated. Based on this prerequisite, two operating strategies (OS) were investigated, which correspond to the upper and lower boundaries of CO2 capture: OS1 was to maximize the amount of captured CO2 while keeping the heat supplied to the district heating (DH) network unchanged; and OS2 was to maximize CO2 capture while keeping both supplied heat and generated electricity unchanged. To obtain more accurate results regarding the CO2 capture, a dynamic model developed in Aspen Hysys™ was utilized to simulate monoethanolamine (MEA) based chemical absorption for CO2 capture. By using real dynamic data from a waste-fired CHP plant, dynamic simulation results showed that the highest amount of captured CO2, which was achieved in OS1, was 401 kton/year, corresponding to a CO2 capture ratio of 82%; while the lowest amount of captured CO2, which was achieved in OS2, was 99 kton/year, corresponding to a CO2 capture ratio of 20%. For OS1, the electricity generation was substantially decreased by 61%. When determining the negative emission, the emission resulted from the share of fossil fuel in the waste needs to be excluded. For the studied CHP plant, the fossil share was around 45%. As a result, only OS1 can achieve the negative emission, which was 181 kton/year; while OS2 still led to positive emissions. Compared to the plant without CO2 capture, the carbon intensity of heat was reduced from 0.405 ton/MWh to 0.091 ton/MWh in OS1 and 0.351 ton/MWh in OS2, while the carbon intensity of electricity was reduced from 0.409 ton/MWh to 0.072 ton/MWh in OS1 and 0.343 ton/MWh in OS2. 

Integrating carbon dioxide (CO2) capture in biomass or waste-fired combined heat and power (CHP) plants has been considered a key measure to achieve negative emissions. To support decision-making, an accurate assessment of the potential contribution and the associated cost from the national perspective is urgently needed. This paper proposed a bottom-up approach based on a dynamic modelling to evaluate the potental of nationwide negative emissions. As heat supply is often prioritized by CHP plants, unchanged heat generation is a prerequisite of this study. Two operating modes (OMs) for the integration of CO2 capture are investigated, which can represent the upper and lower boundaries of CO2 capture: OM1 aims to maximize the amount of captured CO2, while electricity generation can be sacrificed; OM2 aims to maximize the amount of captured CO2, while the electricity generation is maintained unchanged. Sweden is employed as a case study. Results show that operating CO2 capture in OM1 can achieve 8.7 million ton CO2 nationwide negative emissions a year, while operating CO2 capture in OM2 can generate 4.3 million ton CO2 positive emissions a year, which represents a reduction of 6.3 million tonCO2 a year compared with the reference plant without CO2 capture. The levelized costs of CO2 avoided are 36.9 USD/tonCO2 and 52.0 USD/tonCO2 for OM1 and OM2, respectively. The biogenic fraction of waste has a significant influence on negative emissions. According to the Swedish climate goal about bioenergy with CO2 capture and storage (BECCS), to achieve 3 million ton negative CO2 emissions a year, the minimum biogenic fractions should be 32.8% and 84.3% for operating CO2 capture in OM1 and OM2, respectively; in contrast, to achieve 10 million ton negative emissions a year, biomass and waste-fired CHP plants have to operate CO2 capture in OM1 and the biogenic fraction needs to be over 59.9%.