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Coal-fired utility boilers online multi-objective optimization of combustion

Author: WangJiaZuo
Tutor: YanWeiPing
School: North China Electric Power University
Course: Fluid Machinery and Engineering
Keywords: Coal-fired utility boilers Support Vector Machine Mixed kernel Multi-objective optimization of combustion Fluid Machinery
CLC: TK227.1
Type: Master's thesis
Year: 2011
Downloads: 79
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Abstract


In recent years, more and more attention as energy conservation , coal-fired boiler combustion optimization problem has been widely studied . Boiler -line multi-objective optimization problem burning a measuring device is a comprehensive large-scale fluid mechanical power consumption, combustion predictive modeling , controllable parameter optimization problem of Excellence various factors , this paper focuses on one of the modeling portion , mixed nuclear Constructs support Vector Machines combustion prediction model through genetic algorithm parameter optimization , combustion optimization written guidance system to achieve the corresponding functions. For the wind , pink tube flow field uniformity makes it difficult problem of measuring points are arranged proposed measures to improve the measurement accuracy ; against wind , coal concentration , coal fineness , coal characteristics and measuring devices for Fans principle was introduced analyze the measurement accuracy of the importance for combustion optimization . Using least squares support vector machine to establish combustion predictive models. In order to improve support vector machine regression model accuracy on the choice of kernel function and its parameters were studied, the mixed kernel function applied to the combustion prediction model , the researchers found that the prediction of different objectives and number of training samples , have their own best of the kernel function , overall , for the boiler combustion optimization problem , the use of radial basis kernel and polynomial kernel additive mixing methods are an ideal fit with the prediction accuracy ; in the parameter selection, for the use of nuclear additive mixed support vector machine, were investigated regularization parameter , radial basis kernel width , polynomial order of the role of nuclear summarized parameter selection rules ; They also found that the training samples should be taken as far as possible to the upper and lower limits of all input parameters , or it may a larger prediction bias . Introduced a genetic algorithm calculation steps , considering the large fluid mechanical power consumption , mill power consumption , the boiler thermal efficiency and NOx emission concentration and other factors, derived fitness function \Programming of the \By combustion optimization guidance tangentially center simulation , historical data query , Model Online updates and other functions , to achieve real-time online combustion optimization foundation.

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CLC: > Industrial Technology > Energy and Power Engineering > Steam Power Engineering > Steam boiler > Run > Combustion and adjustments
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