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• W4283449418 endingPage "124597" @default.
• W4283449418 startingPage "124597" @default.
• W4283449418 abstract "The transient response of a particle-to-sCO 2 moving bed heat exchanger (MBHE) is investigated by a continuum model when perturbations are applied to the particle inlet temperature, sCO 2 inlet temperature, and the sCO 2 mass flow rate. Upon disturbance, the outlet temperatures of the sCO 2 and particle flows deviate from the designed values and gradually approach a new steady state. The transient process can be described by exponential functions when the disturbance is applied to one of the input parameters while the transition becomes unpredictable when multiple input parameters are simultaneously varied. Two real-time control strategies are then adopted to maintain the sCO 2 outlet temperature at its designed value. In strategy 1, the particle flow rate is adjusted; In strategy 2, the sCO 2 flow is divided into two branches: one branch flows through the heat exchanger for heating, and the other enters a bypass. The values of the adjusting parameters are instantly determined based on the BP neural network. By setting 50 random disturbance cases, we demonstrate that the control strategy based on sCO 2 bypass and BP neural network can effectively make the outlet temperatures of particle and sCO 2 flows reach their designed values with minimal deviations. • Transient response of a particle heat exchanger is given by continuum modeling. • Evolution of outlet particle/sCO2 temperatures follow exponential functions. • Real-time dynamic control strategy based on BP neural network is proposed. • Control strategy is proved to be effective for 50 random disturbance cases." @default.
• W4283449418 created "2022-06-26" @default.
• W4283449418 creator A5010433744 @default.
• W4283449418 creator A5039674609 @default.
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• W4283449418 date "2022-10-01" @default.
• W4283449418 modified "2023-10-18" @default.
• W4283449418 title "Dynamic characteristics and real-time control of a particle-to-sCO2 moving bed heat exchanger assisted by BP neural network" @default.
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• W4283449418 doi "https://doi.org/10.1016/j.energy.2022.124597" @default.
• W4283449418 hasPublicationYear "2022" @default.
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