Data-driven real-time predictive control for industrial heating loads

Wu, Chuanshen, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2024. Data-driven real-time predictive control for industrial heating loads. Electric Power Systems Research 232 , 110420. 10.1016/j.epsr.2024.110420

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Abstract

Uncertainties and computational complexity are two growing challenges in scheduling industrial heating loads. In this paper, a data-driven real-time predictive control approach is proposed to deal with these challenges in the industrial scheduling of bitumen tanks. Specifically, predictive control technology is utilized to leverage the updated information to mitigate the negative impact of past uncertainties in equipment parameters and external environmental factors, which may lead to temperature constraint violations in the bitumen tank operation processes. Meanwhile, a data-driven method using artificial neural networks (ANN) is developed to ensure efficient computation for real-time predictive control. Moreover, a two-layer control method is devised to reduce the calculation time for day-ahead optimal scheduling of a large scale of bitumen tanks, aiming to generate sufficient high-quality data for training ANN. In the two-layer control method, the clustered temperature transfer processes of bitumen tanks are analyzed and modeled for the first time. Simulation results indicate that the two-layer control method can significantly reduce the computational time required for the day-ahead optimal scheduling of bitumen tanks, facilitating the generation of a large amount of high-quality data for training ANN. Subsequently, the application of ANN enables real-time predictive control, helping to eliminate the negative impact of uncertainties.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0378-7796
Funders:	EPSRC
Date of First Compliant Deposit:	22 April 2024
Date of Acceptance:	16 April 2024
Last Modified:	17 Jun 2024 13:47
URI:	https://orca.cardiff.ac.uk/id/eprint/168213

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