DateThe final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline.iii can preserve product quality and maintain stable product compositions, resulting in a more efficient and cost-effective operation, which can ultimately lead to scale-up and commercialization of solar thermochemical technologies. ABSTRACTIn this work, we propose a model predictive control (MPC) system for a solar-thermal reactor for the steam-gasification of biomass. The proposed controller aims at rejecting the disturbances in solar irradiation caused by the presence of clouds. A first-principles dynamic model of the process was developed. The model was used to study the dynamic responses of the process variables and to identify a linear time-invariant model used in the MPC algorithm.To provide an estimation of the disturbances for the control algorithm, a one-minute-ahead direct normal irradiance (DNI) predictor was developed. The proposed predictor utilizes information obtained through the analysis of sky images, in combination with current atmospheric measurements, to produce the DNI forecast.In the end, a robust controller was designed capable of rejecting disturbances within the operating region. Extensive simulation experiments showed that the controller outperforms a iv finely-tuned multi-loop feedback control strategy. The results obtained suggest that our controller is suitable for practical implementation.v
A model predictive control (MPC) system for a solar-thermal reactor was developed and applied to the solar-thermal steam-gasification of carbon. The controller aims at rejecting the disturbances in solar irradiance, caused by the presence of clouds. Changes in solar irradiance are anticipated using direct normal irradiance (DNI) forecasts generated using images acquired through a Total Sky Imager (TSI). The DNI predictor provides an estimation of the disturbances for the control algorithm, for a time horizon of 1 min. The proposed predictor utilizes information obtained through the analysis of sky images, in combination with current atmospheric measurements, to produce the DNI forecast. The predictions of the disturbances are used, in combination with a dynamic model of the process, to determine the required control moves at every time step. The performance of the proposed DNI predictor-controller scheme was compared to the performance of an equivalent MPC that does not use DNI forecasts in the calculation of the control signals. In addition, the performance of a controller fed with perfect DNI predictions was also evaluated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.