Immunological AI Optimizer Deployment in a 330 MW Lignite-Fired Unit for NO x Abatement

Konrad Świrski (), Łukasz Śladewski (), Konrad Wojdan and Xianyong Peng
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Konrad Świrski: Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warszawa, Poland
Łukasz Śladewski: Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warszawa, Poland
Konrad Wojdan: Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warszawa, Poland
Xianyong Peng: Jiangsu Provincial Engineering Research Center for Smart Energy Technology and Equipment, School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China

Energies, 2025, vol. 18, issue 12, 1-19

Abstract: This study presents an advanced NO x reduction strategy for a 330 MW lignite-fired boiler using an immunological AI system: the SILO (Stochastic Immune Layer Optimizer) combustion optimizer inspired by artificial immune systems. The immunological AI optimizer adaptively models multi-variable interactions and fireball shape in real time, optimizing fuel–air mixing to reduce NO x formation at the source. Unlike reactive secondary methods, the combustion optimizer reshapes the combustion process to reduce emissions while improving efficiency. Real-time temperature data from the AGAM acoustic system inform the combustion optimizer’s fireball modeling, ensuring combustion uniformity. A urea-based SNCR system serves as a secondary layer, controlled based on local furnace conditions to target thermal zones. Field results confirmed that SILO reduced NO x emissions below 200 mg/Nm 3 , decreased urea consumption by up to 34%, and improved boiler efficiency by 0.29%. The architecture offers a scalable, DCS-integrated solution for aligning fossil-fueled operations with tightening emission standards.

Keywords: combustion optimization; NO x emission reduction; SNCR technology; selective non-catalytic reduction; lignite-fired power plant; acoustic temperature measurement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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