摘要
在对某电厂锅炉混煤掺烧的飞灰含碳量特性进行多工况热态测试的基础上,应用人工神经网络的非线性动力学特性及自学习特性,建立了飞灰含碳量特性的神经网络模型,检验样本的预测值与实测值的相对误差分别为1.14%、1.19%和2.45%,证实了该模型的可行性。将锅炉运行调节参数作为优化目标函数的自变量结合全局最优的遗传算法,针对无印尼煤掺烧、1台磨煤机掺烧和2台磨煤机掺烧3种工况进行了寻优并获得了最佳操作参数,飞灰含碳量分别由原来的2.25%、1.96%和1.08%降至1.87%、1.69%和0.73%,表明印尼煤的掺烧有助于降低飞灰含碳量,提高锅炉效率。同时,也对掺烧印尼煤工况下磨煤机或者磨煤机组合的选择进行了寻优,结果表明用B、C磨煤机进行掺烧为最佳方案。
On the basis of hot - state tests under many operating conditions for investigating the characters of unburned carbon in fly ash of boilers for mixedly burning blended coal,a neural network model has been established by using the nonlinear dynamics and self - learning characters of the artificial neu- ral network. The relative error between predicted value and measured value are 1.4%, 1.19%, and 2.45% respectively, comfirming the feasibility of said model. Taking the adjustment parameters in boiler operation as independent variables of the target function for optimization, and combining with the genetic algorithm in overall situations, directing against three typical operating conditions, namely blending Indonisian coal in 0,1, and 2 mills, the optimal operating parameters have been found and obtained,the unburned carbon content in fly ash has decreased from original 2.25% ,1.96% ,and 1.08% to 1.87~, 1.69% ,and 0. 73% respectively. It shows that the mixedly burbing of Indonisian coal is helpful to decrease the unburned carbon content in fly ash,enhancing the boiler efficiency.
出处
《热力发电》
CAS
北大核心
2010年第3期30-35,共6页
Thermal Power Generation
关键词
锅炉
飞灰含碳量
遗传算法
印尼煤
混煤掺烧
磨煤机
boiler
unburned carbon content in fly ash
genetic algorithm
Indonisian coal
mixedly burning of blended coal
mills
