Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

The dominant role of electricity generation and environment consideration have placed strong requirements on coal fired power plants, requiring them to improve boiler combustion efficiency and decrease carbon emission. Although neural network based optimization strategies are often applied to improve the coal fired power plant boiler efficiency, they are limited by some combustion related problems such as slagging. Slagging can seriously influence heat transfer rate and decrease the boiler efficiency. In addition, it is difficult to measure slag build-up. The lack of measurement for slagging can restrict conventional neural network based coal fired boiler optimization, because no data can be used to train the neural network. This paper proposes a novel method of integrating non-dominated sorting genetic algorithm (NSGA II) based multi-objective optimization with computational fluid dynamics (CFD) to decrease or even avoid slagging inside a coal fired boiler furnace and improve boiler combustion efficiency. Compared with conventional neural network based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties such as temperature field inside a boiler by adjusting the temperature and velocity of primary and secondary air in coal fired power plant boiler control systems. The temperature in the vicinity of water wall tubes of a boiler can be maintained within the ash melting temperature limit. The incoming ash particles cannot melt and bond to surface of heat transfer equipment of a boiler. So the trend of slagging inside furnace is controlled. Furthermore, the optimized boiler combustion can keep higher heat transfer efficiency than that of the non-optimized boiler combustion. The software is developed to realize the proposed method and obtain the encouraging results through combining ANSYS 14.5, ANSYS Fluent 14.5 and CORBA C++.

[1]  Ramesh C. Bansal,et al.  Optimizing combustion process by adaptive tuning technology based on Integrated Genetic Algorithm and Computational Fluid Dynamics , 2012 .

[2]  Ronald W. Breault,et al.  Parametric behavior of a CO2 capture process: CFD simulation of solid-sorbent CO2 absorption in a riser reactor , 2013 .

[3]  Kalyanmoy Deb,et al.  On self-adaptive features in real-parameter evolutionary algorithms , 2001, IEEE Trans. Evol. Comput..

[4]  Luis Miguel García-Cuevas,et al.  Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling , 2013 .

[5]  Indra Narayan Kar,et al.  Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks , 2008 .

[6]  Jiyuan Tu,et al.  Computational Fluid Dynamics: A Practical Approach , 2007 .

[7]  Jie Zhou,et al.  Design and Experiment of Silencer for Discharging Waste Water of High Temperature And Pressure In Nuclear Power Plant , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[8]  Weeratunge Malalasekera,et al.  An introduction to computational fluid dynamics - the finite volume method , 2007 .

[9]  Ali Pourmohammad,et al.  2011 International Conference on Electrical Engineering and Informatics 17-19 July 2011 , Bandung , Indonesia Multi Objective Optimization of a LNA Using Genetic Algorithm Based on NSGA-II , 2011 .

[10]  Wenli Yang,et al.  Fuzzy Fault Diagnosis and Accommodation System for Hybrid Fuel-Cell/Gas-Turbine Power Plant , 2010, IEEE Transactions on Energy Conversion.

[11]  Xiangwei Zhao,et al.  Notice of RetractionAnalysis and Numerical Simulation on An Improved Oxygen-enriched Burner , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[12]  Hari B. Vuthaluru,et al.  Control of ash related problems in a large scale tangentially fired boiler using CFD modelling , 2010 .

[13]  Ganapati Panda,et al.  An efficient multi-objective pulse radar compression technique using RBF and NSGA-II , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[14]  C Thompson,et al.  Applied CFD techniques: An introduction based on finite element methods , 2002 .

[15]  Qiang Jin,et al.  CFD simulations of gate leaves design in the SCR-DeNOx facility for coal-fired power plant , 2011, Proceedings of the 30th Chinese Control Conference.

[16]  S. G. Dukelow,et al.  The Control of Boilers , 1986 .

[17]  Hongsheng Hu,et al.  Study of Numerical Simulation and Experimental of Gas Flow Distribution of Electric Composite Filter Bag in 300MW Power Unit in Coal-Fired Power Plant , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[18]  Jyotirmay Mathur,et al.  ‘Derating Factor’ new concept for evaluating thermal performance of earth air tunnel heat exchanger: A transient CFD analysis , 2013 .

[19]  William Yang,et al.  Numerical Modelling of Brown Coal Combustion in a Tangentially-Fired Furnace , 2009 .

[20]  Xiangzhong Meng,et al.  Research of active vibration control optimal disposition based on MIGA and NSGA-II , 2010, 2010 Sixth International Conference on Natural Computation.

[21]  Mohamed Pourkashanian,et al.  CFD modeling of oxy-coal combustion: Prediction of burnout, volatile and NO precursors release , 2013 .

[22]  Ramesh C. Bansal,et al.  Improving fossil fuel boiler combustion efficiency based on integrating real time simulation with online learning technology , 2012 .

[23]  S. Baskar,et al.  Application of NSGA-II Algorithm to Single-Objective Transmission Constrained Generation Expansion Planning , 2009, IEEE Transactions on Power Systems.

[24]  Ali Aminian,et al.  Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model , 2011 .

[25]  Xianming Li,et al.  Computational Fluid Dynamics in Industrial Combustion , 2001 .

[26]  Wilfrido Rivera,et al.  Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse , 2011 .

[27]  Zhaoping Zhong,et al.  Computational fluid dynamics-based simulation of optimal design in power plant flue gas denitrification , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[28]  Thomas Palmé,et al.  Gas turbine sensor validation through classification with artificial neural networks , 2011 .