Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.

[1]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[2]  Sanjay Ghemawat,et al.  MapReduce: a flexible data processing tool , 2010, CACM.

[3]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[5]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[6]  Xin Bi,et al.  XML document classification based on ELM , 2011, Neurocomputing.

[7]  Jeffrey D. Ullman,et al.  SharesSkew: An Algorithm to Handle Skew for Joins in MapReduce , 2015, Inf. Syst..

[8]  Fuchun Sun,et al.  Multi-Modal Local Receptive Field Extreme Learning Machine for object recognition , 2016, IJCNN.

[9]  Q. M. Jonathan Wu,et al.  Human face recognition based on multidimensional PCA and extreme learning machine , 2011, Pattern Recognit..

[10]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[11]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[12]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[13]  Dragan Tucakovic,et al.  Numerical prediction of processes for clean and efficient combustion of pulverized coal in power plants , 2015 .

[14]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[15]  Renata Dwornicka,et al.  Optimization of heating and cooling operations of steam gate valve , 2009 .

[16]  Weiping Zhang,et al.  Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency , 2014, Knowl. Based Syst..

[17]  Zhao Kai.  Hybrid Modeling Optimization of Thermal Efficiency and NO_x Emission of Utility Boiler , 2011 .

[18]  Xiaoyan Wu,et al.  A hybrid least square support vector machine for boiler efficiency prediction , 2017, 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC).

[19]  D. Ian Barnes.  Understanding pulverised coal, biomass and waste combustion – A brief overview , 2015 .

[20]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[21]  Gang Chen,et al.  Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method , 2016 .

[22]  Guang-Bin Huang,et al.  Face recognition based on extreme learning machine , 2011, Neurocomputing.

[23]  Francisco Herrera,et al.  Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce , 2018, Inf. Fusion.

[24]  Piotr Duda,et al.  Numerical method for determining the allowable medium temperature during the heating operation of a thick‐walled boiler element in a supercritical steam power plant , 2012 .

[25]  Zhiqiong Wang,et al.  ELM ∗ : distributed extreme learning machine with MapReduce , 2013, World Wide Web.

[26]  Qinghua Zheng,et al.  Distributed extreme learning machine with alternating direction method of multiplier , 2017, Neurocomputing.

[27]  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..

[28]  B. He,et al.  A MapReduce-Based ELM for Regression in Big Data , 2016, IDEAL.

[29]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).