文章摘要
引用本文:许巧玲,王彦端,林伟豪,梁 航,万 晋.基于ACO-LSSVM燃煤碳元素分析的锅炉CO2排放量计算[J].福州大学学报(自然科学版),2015,43(4):548~553
基于ACO-LSSVM燃煤碳元素分析的锅炉CO2排放量计算
Calculation of CO2 emissions in boilers based on ACO-LSSVM method for carbon element analysis of coal
  
DOI:
中文关键词: 锅炉  CO2排放量  最小二乘支持向量机  碳元素含量
英文关键词: boiler  CO2 emissions  least square support vector machine  carbon content
基金项目:
作者单位
许巧玲 福州大学石油化工学院福建 福州 350116 
王彦端 福州大学石油化工学院福建 福州 350116 
林伟豪 福建省锅炉压力容器检验研究院福建 福州 350008 
梁 航 福建省锅炉压力容器检验研究院福建 福州 350008 
万 晋 福州大学石油化工学院福建 福州 350116 
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中文摘要:
      针对生产实际中无法对燃煤组分进行在线检测分析的问题,建立了基于最小二乘支持向量机的燃煤的碳含量预测模型. 以工业锅炉燃煤的相关工业分析数据作为模型的输入,并采用蚁群算法对最小二乘支持向量机相关参数进行寻优以提高模型的建模精度. 应用该模型对福建地区工业锅炉燃煤的碳元素含量进行预测,预测结果的最大误差为1.18%,平均误差为0.64%. 和传统多元逐步回归预测方法相比,该预测模型具有较高的预测精度,为工业锅炉二氧化碳排放量的科学计算奠定了基础.
英文摘要:
      The carbon content of coal is one of the important parameters in calculating emissions of carbon dioxide in industrial coal boilers,but it is difficult to be measured quickly or accurately during the boilers operation. Based on the element analysis of coal from laboratory,a LSSVM (least-squares support vector machine) model for predicting the carbon content of coal is developed. Ant colony algorithm optimization is used to optimize the parameters in LSSVM in order to improve the accuracy of the model. Propose predictive model is applied to predict the carbon content of coal that comes from Fujian region,the maximum error and average error of the prediction results are 1.18% and 0.64%,respectively. Comparing proposed method with multivariate stepwise regression method,the results show that this method has higher simulation accuracy. This carbon element analysis model provides solid basis for calculating emissions of carbon dioxide in industrial boilers.
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