Optimization of combustion process in coal-fired power plant with utilization of acoustic system for in-furnace temperature measurement

Abstract This paper presents methodology and results of a research project on software optimization of combustion process efficiency in coal-fired power plant. The general goal of this project was to increase boiler efficiency by proper control of the combustion process using optimization software, integrated with Distributed Control System and in-furnace temperature profile measurement system. The research goal relays on new approach in combustion modelling based on in-furnace temperature distribution and utilization of this model in on-line boiler control. it is assumed that this approach allows for more precise control of the combustion process, what finally has a positive influence on boiler performance – the efficiency in specific. The solution has been designed, installed and tested on existing, utility plant – 225 MW (650 t/h of nominal steam generation). Final analysis has shown positive results – the boiler efficiency increased over 0.25%.

[1]  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..

[2]  K. Swirski,et al.  Immune Inspired System for Chemical Process Optimization using the example of a Combustion Process in a Power Boiler , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[3]  Ramesh C. Bansal,et al.  Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant , 2014 .

[4]  G. Kino,et al.  Remote temperature measurement using an acoustic probe , 1982 .

[5]  Jussi Mäkilä,et al.  Neural Network Combustion Optimisation in Naantali Power Plant , 2001 .

[6]  Bin Huang,et al.  TDLAS for measurement of temperature in combustion environment , 2013, Other Conferences.

[7]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[8]  Guoqiang Li,et al.  Combustion optimization of a coal-fired boiler with double linear fast learning network , 2016, Soft Comput..

[9]  Waldemar Wójcik,et al.  Conception of genetic controller application in power boiler , 2005, SPIE Optics + Optoelectronics.

[10]  Lijun Xu,et al.  Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler , 2013 .

[11]  A. Sappey,et al.  Results of Closed-Loop Coal-Fired Boiler Operation Using a TDLAS Sensor and Smart Process Control Software , 2011 .

[12]  Konrad Wojdan,et al.  Conditioning of Model Identification Task in Immune Inspired Optimizer SILO , 2009 .

[13]  Konrad Wojdan A Practical Approach To Combustion Process Optimization Using An Improved Immune Optimizer , 2011 .

[14]  Erik Schaffernicht,et al.  Machine Learning Techniques for Selforganizing Combustion Control , 2009, KI.

[15]  Konrad Wojdan,et al.  Transition State Layer in the Immune Inspired Optimizer , 2010, IEA/AIE.

[16]  Konrad Wojdan,et al.  Transition States Handling in Self-Adaptive Steady State Optimizer of Industrial Processes , 2010 .

[17]  Ilamathi Balamurugan,et al.  ANN - SQP Approach For NOx Emission Reduction In Coal Fired Boilers , 2012 .

[18]  Tomasz Janda,et al.  A validation of computational fluid dynamics temperature distribution prediction in a pulverized coal boiler with acoustic temperature measurement , 2015 .

[19]  K. Mizutani,et al.  Temperature Measurement Using Acoustic Reflectors , 2004 .

[20]  Wang Weiqing,et al.  Multi-objective Optimization of Coal-Fired Boiler Efficiency and NOx Emission under Different Ecological Environment , 2012 .

[21]  M. Warchoł,et al.  Methods Providing Good Conditioning of Model Identification Task in Immune Inspired, Steady-State Controller of an Industrial Process , 2009 .

[22]  Feng Wu,et al.  A comparative study of the multi-objective optimization algorithms for coal-fired boilers , 2011, Expert Syst. Appl..

[23]  Piotr Tatjewski,et al.  Advanced Control of Industrial Processes: Structures and Algorithms , 2006 .

[24]  Huaichun Zhou,et al.  Optimization of combustion based on introducing radiant energy signal in pulverized coal-fired boiler , 2010 .

[25]  V. Selladurai,et al.  ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler , 2013, Clean Technologies and Environmental Policy.

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

[27]  Eduardo F. Camacho,et al.  Introduction to Model Based Predictive Control , 1999 .

[28]  L. Zhang,et al.  Acoustic travel-time measurement in acoustic temperature field monitoring , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[29]  Alireza Rahrooh,et al.  Identification of nonlinear systems using NARMAX model , 2009 .

[30]  Huan Zhao,et al.  Modeling and Optimization of Efficiency and NOx Emission at a Coal-Fired Utility Boiler , 2009, 2009 Asia-Pacific Power and Energy Engineering Conference.

[31]  Vladimir Havlena,et al.  Application of model predictive control to advanced combustion control , 2005 .

[32]  Waldemar Wojcik,et al.  Controlling combustion process in power boiler by genetic algorithm and neural network , 2005, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[33]  Konrad Wojdan,et al.  Maintaining Good Conditioning of Model Identification Task in Immune Inspired On-line Optimizer of an Industrial Process , 2009, Eng. Lett..