Multi-step-ahead prediction of NOx emissions for a coal-based boiler

Until 2016 power plants within the EU will have to meet new limits on emissions as dictated by EU regulations. One of the major challenges is to reduce emissions of nitrogen oxides (NOx) due to health and ozone-formation concerns. Combustion optimisation is one of the primary measures for reducing NOx emissions from boilers burning coal, oil, or natural gas. The optimisation can be achieved by excess air control, boiler fine tuning and balancing the fuel and air flow to the various burners in order to reach minimum NOx formation.

[1]  Guang-Ren Duan,et al.  Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions , 2003, Cognition, Technology & Work.

[2]  Hao Zhou,et al.  Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks , 2004 .

[3]  Norman R. Draper,et al.  Applied regression analysis (2. ed.) , 1981, Wiley series in probability and mathematical statistics.

[4]  Sami El-Ferik,et al.  MPC-based controller for augmented boiler-NOx model , 2012, International Multi-Conference on Systems, Sygnals & Devices.

[5]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[6]  Mário Costa,et al.  Impact of the air staging on the performance of a pulverized coal fired furnace , 2009 .

[7]  Soteris A. Kalogirou,et al.  Artificial intelligence for the modeling and control of combustion processes: a review , 2003 .

[8]  J. Edward Cichanowicz,et al.  SCR performance analysis hints at difficulty in achieving high NOX removal targets , 2002 .

[9]  B. J. Radl,et al.  Neural networks prove effective at NOx reduction , 2000 .

[10]  Hao Zhou,et al.  A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler , 2009, Expert Syst. Appl..

[11]  Zhou Hao,et al.  Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion , 2001 .

[12]  Francisco Cadavid,et al.  Coal combustion modelling of large power plant, for NOx abatement , 2007 .

[13]  Hao Zhou,et al.  Multi‐objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms , 2005 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  A. Copado,et al.  CESAR-SIRE: advanced software for boiler efficiency and NOx optimisation , 2002 .

[16]  Kang Li,et al.  Modelling and Prediction of NOx Emission in a Coal-Fired Power Generation Plant , 2004 .

[17]  Guohe Huang,et al.  Artificial intelligence for management and control of pollution minimization and mitigation processes , 2003 .

[18]  Moustafa Elshafei,et al.  Soft sensor for NOx and O2 using dynamic neural networks , 2009, Comput. Electr. Eng..

[19]  S. Samuelsen,et al.  Optimal, active control of oxides of nitrogen (NO/sub x/) emissions from a natural gas-fired burner using a simple genetic algorithm , 1995, Proceedings of International Conference on Control Applications.

[20]  Steinar Pedersen,et al.  Effects of NOx in the flue gas degradation of MEA , 2011 .

[21]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[22]  Zheng Yao,et al.  A new approach for function approximation in boiler combustion optimization based on modified structural AOSVR , 2009, Expert Syst. Appl..

[23]  S. C. Hill,et al.  Modeling of nitrogen oxides formation and destruction in combustion systems , 2000 .

[24]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[25]  Hao Zhou,et al.  Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization , 2012, Eng. Appl. Artif. Intell..

[26]  Eugenio Schuster,et al.  Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms , 2009 .