Research Papers

Use of Image-Based Direct Normal Irradiance Forecasts in the Model Predictive Control of a Solar-Thermal Reactor

[+] Author and Article Information
Elizabeth Saade

e-mail: maria.saadesaade@colorado.edu

David E. Clough

e-mail: david.clough@colorado.edu

Alan W. Weimer

e-mail: alan.weimer@colorado.edu
Department of Chemical
and Biological Engineering,
University of Colorado,
Jennie Smoly Caruthers Biotechnology Building,
596 UCB,
Boulder, CO 80309-0596

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received August 2, 2013; final manuscript received October 20, 2013; published online November 19, 2013. Assoc. Editor: Aldo Steinfeld.

J. Sol. Energy Eng 136(1), 010905 (Nov 19, 2013) (9 pages) Paper No: SOL-13-1217; doi: 10.1115/1.4025825 History: Received August 02, 2013; Revised October 20, 2013

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.

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Fig. 1

Schematic of solar-thermal reactor for the steam-gasification of carbon

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Fig. 2

Diagram of proposed control system

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Fig. 3

Strategy for the image-based DNI forecasts

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Fig. 6

Example of characteristics obtained from images. (a) Uncovered case. (b) Covered case.

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Fig. 4

Output images after applying masks. (a) Mask 1, (b) Mask 2, (c) Mask 3.

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Fig. 5

Four sky scenarios and main characteristics. (a) Sunny scenario. (b) Cloudy scenario. (c) Transition scenario, covered case. (d) Transition scenario, uncovered case.

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Fig. 7

Comparison between the predicted one-minute-ahead DNI and the measured DNI, for two different dates. (a) February 27th, 2012. (b) November 14th, 2012.

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Fig. 8

Integration of image-based DNI predictor with MPC algorithm

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Fig. 9

Comparison of MPC strategies. (a) Synthesis gas fraction. (b) CO:CO2 ratio.

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Fig. 10

Manipulated variables using different MPC strategies. (a) Gas flow rate. (b) Steam flow.




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