Processes like combustion, pyrolysis or gasification of coal and biomass are typical applications of gas-solid fluidized beds. These reactors normally use silica sand as the inert material inside the bed and the sand ...Processes like combustion, pyrolysis or gasification of coal and biomass are typical applications of gas-solid fluidized beds. These reactors normally use silica sand as the inert material inside the bed and the sand particles represent around 95% of the total bed weight. Pressure measurements have been used to characterize the dynamic behavior of fluidized beds since early researches in the area. Pressure fluctuations are generally due to bubbles flow which characterizes the fluidization regime. The present work aims to perform a time-frequency analysis of the pressure signal acquired in an experimental apparatus on different gas-solid flow regimes. Continuous and discrete wavelet transforms were applied and the results were compared with image records acquired simultaneously with the pressure signal. The main frequencies observed are in accordance with the ones obtained through Fourier spectra. The time-frequency distribution of the signal agrees with the phenomena observed in the image record, remarkably for the slugging flow. Some additional research is still necessary to completely characterize the flow regimes using the wavelet scalograms but the present results show that the task is a very promising one.展开更多
Concentrating Solar Power Tower(CSPT)plants rely on heliostat fields to focus sunlight onto a central receiver.Although simple aiming strategies,such as directing all heliostats to the receiver’s equator,can maximize...Concentrating Solar Power Tower(CSPT)plants rely on heliostat fields to focus sunlight onto a central receiver.Although simple aiming strategies,such as directing all heliostats to the receiver’s equator,can maximize energy collection,they often result in uneven flux distributions that cause hotspots,thermal stresses,and reduced receiver lifetimes.This paper presents a novel,data-driven approach that combines constraint learning,neural network-based surrogates,and mathematical optimization to address these challenges.The methodology learns complex heliostat-to-receiver flux interactions from simulation data and embeds the resulting surrogate model in a tractable optimization framework.By maximizing a tailored quality score that balances energy collection with flux uniformity,the approach produces smoothly distributed flux profiles and mitigates excessive thermal peaks.An iterative refinement process,guided by a trust region strategy and progressive data sampling,ensures continual improvement of the surrogate model by exploring new solution spaces at each iteration.Results from a real CSPT case study show that the proposed approach outperforms conventional heuristic methods,delivering flatter flux distributions with nearly a 10%reduction in peak values and safer thermal conditions(reflected by up to a 50%decrease in deviations from safe concentration distributions),without significantly compromising overall energy capture.展开更多
文摘Processes like combustion, pyrolysis or gasification of coal and biomass are typical applications of gas-solid fluidized beds. These reactors normally use silica sand as the inert material inside the bed and the sand particles represent around 95% of the total bed weight. Pressure measurements have been used to characterize the dynamic behavior of fluidized beds since early researches in the area. Pressure fluctuations are generally due to bubbles flow which characterizes the fluidization regime. The present work aims to perform a time-frequency analysis of the pressure signal acquired in an experimental apparatus on different gas-solid flow regimes. Continuous and discrete wavelet transforms were applied and the results were compared with image records acquired simultaneously with the pressure signal. The main frequencies observed are in accordance with the ones obtained through Fourier spectra. The time-frequency distribution of the signal agrees with the phenomena observed in the image record, remarkably for the slugging flow. Some additional research is still necessary to completely characterize the flow regimes using the wavelet scalograms but the present results show that the task is a very promising one.
基金supported by the Madrid Government(Comu-nidad de Madrid-Spain)under the Multiannual Agreement with UC3M(SOLAROPIA-CM-UC3M)financial support from MCIN/AEI/10.13039/501100011033,project PID2023-151013NB-I00the FPU grant(FPU20/00916).
文摘Concentrating Solar Power Tower(CSPT)plants rely on heliostat fields to focus sunlight onto a central receiver.Although simple aiming strategies,such as directing all heliostats to the receiver’s equator,can maximize energy collection,they often result in uneven flux distributions that cause hotspots,thermal stresses,and reduced receiver lifetimes.This paper presents a novel,data-driven approach that combines constraint learning,neural network-based surrogates,and mathematical optimization to address these challenges.The methodology learns complex heliostat-to-receiver flux interactions from simulation data and embeds the resulting surrogate model in a tractable optimization framework.By maximizing a tailored quality score that balances energy collection with flux uniformity,the approach produces smoothly distributed flux profiles and mitigates excessive thermal peaks.An iterative refinement process,guided by a trust region strategy and progressive data sampling,ensures continual improvement of the surrogate model by exploring new solution spaces at each iteration.Results from a real CSPT case study show that the proposed approach outperforms conventional heuristic methods,delivering flatter flux distributions with nearly a 10%reduction in peak values and safer thermal conditions(reflected by up to a 50%decrease in deviations from safe concentration distributions),without significantly compromising overall energy capture.
