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Towards data-driven narx ann simulation for optimal control of the flue gas desulphurization for coal power plants

• Dominika Cywicka
• Agnieszka Jakobik

(2023). ECMS 2023, 37th Proceedings
Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2023
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-80-7
ISBN: 978-3-937436-79-1 (CD) Communications of the ECMS Volume 37, Issue 1, June 2023, Florence, Italy June 20th – June 23rd, 2023

https://doi.org/10.7148/2023-0562

Dominika cywicka, Agnieszka jakobik (2023). Towards Data-Driven NARX ANN Simulation for Optimal Control of the Flue Gas Desulphurization for Coal Power Plants, ECMS 2023, Proceedings Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation. doi:10.7148/2023-0562

This paper presents the ANN-based algorithm for data-driven optimal control of desulfurization of the flue gases from Coal Power Plants.  We have proposed the NARX recurrent neural network with experimentally selected feedback connection length as the black box model for the first stage of the control process. Then simple brute force algorithm was used to find the optimal level of the reagent added into the system to keep the SOx concentration outlet below the assumed level. This procedure was designed for a known level of SOx concentration inlet. The proposed approach was tested on real data collected from the selected  Coal Power Plant in Poland. The simulation that was made confirms that such an approach is effective for coal power plants to increase their energy efficiency and meet the appropriate environmental standards.