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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Konrad Świrski & Łukasz Śladewski & Konrad Wojdan & Xianyong Peng, 2025. "Immunological AI Optimizer Deployment in a 330 MW Lignite-Fired Unit for NO x Abatement," Energies, MDPI, vol. 18(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3032-:d:1674249
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