A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data
A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data
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DOI：
10.1016/j.energy.2017.02.031
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发表时间：
2017-04-01
期刊：
影响因子：
9
通讯作者：
Liu, Jizhen
中科院分区：
文献类型：
--
作者：
Lv, You;Hong, Feng;Liu, Jizhen
循环流化床（CFB）燃烧是一种新型洁净煤技术，具有燃料适应性广、污染物排放低等优点。循环流化床锅炉床温是影响锅炉安全运行和污染物排放的重要因素。准确描述床温动态特性的模型有利于减小床温波动。基于真实的运行数据，提出了一种基于最小二乘支持向量机方法的300 MW循环流化床锅炉床温动态预测模型。以给煤量和一次风量为自变量。将变量的当前值和先前序列作为模型输入，描述床温的动态特性。此外，床层温度的过去值作为反馈，然后添加到输入。粒子群优化技术被用来确定最佳延迟订单。对几种模型模式进行了讨论和比较。对比结果表明，该模型结构合理，能够实现床层温度的准确预测。(C)2017爱思唯尔有限公司版权所有
Circulating fluidized bed (CFB) combustion is a new clean coal technology with advantages of wide fuel flexibility and low pollutant emissions. The bed temperature of CFB boilers is an important factor that influences operating security and pollutant emission generation. An accurate model to describe the dynamic characteristics of bed temperature is beneficial in reducing temperature fluctuations. This study presents a dynamic model for predicting the bed temperature of a 300 MW CFB boiler based on the least squares support vector machine method with real operational data. Coal feed rate and primary air rate are selected as the independent variables. The current values and previous sequences of the variables are considered as the model inputs to describe the dynamic characteristics of bed temperature. In addition, the past values of bed temperature are taken as feedback and then added to the inputs. The particle swarm optimization technique is used to determine optimal delay orders. Several model patterns are also discussed and compared. Comparison results show that the proposed model structure is reasonable and that the model can achieve the accurate prediction of the bed temperature. (C) 2017 Elsevier Ltd. All rights reserved.