热力发电2018，Vol.47Issue(1)：26-32,7.DOI:10.19666/j.rlfd.201704004
基于自适应神经模糊推理系统的煤粉锅炉飞灰含碳量建模
Modelling for unburned carbon content in fly ash from coal-fired boilers based on adaptive neuro-fuzzy inference system
摘要
Abstract
The unburned carbon content in fly ash is a critically important parameter during the boiler operation, which influences both the efficiency and safety of the boiler, therefore, it is of great significance to establish the model for unburned carbon content in fly ash. The present work employs the adaptive neuro-fuzzy inference system (ANFIS) to model the unburned carbon content in fly ash based on the real time data for a 660 MW tangentially coal-fired boiler. First of all, the initial input parameters are determined by expert knowledge and operation experience, then subtractive clustering is used to determine the initial fuzzy rules and structural parameters, the hybrid learning algorithm composed of the least square algorithm and error back propagation algorithm is employed to optimize the parameters of the ANFIS, thus the initial modelling for the unburned carbon content in fly ash is completed. Then, sensitivity analysis is used to determine the final input parameters of the ANFIS model to reduce the complexity and improve the accuracy. Finally, the model for unburned carbon content in fly ash is constructed. When applied to the test datasets, this model has high prediction accuracy, which can reflect the variation of unburned carbon content in fly ash. Moreover, compared to the least squared support vector machine (LSSVM) and typical back propagation neuro network (BP), the proposed ANFIS model has a higher prediction accuracy and greater generalization ability in the case with enough training samples, while the LSSVM is better in the case with small training samples.关键词
飞灰含碳量/煤粉锅炉/ANFIS/减法聚类算法/最小二乘支持向量机/BP神经网络/预测精度Key words
unburned carbon content in fly ash/coal-fired boiler/ANFIS/subtractive clustering/LSSVM/BP neural network/prediction accuracy分类
能源科技引用本文复制引用
王月兰,马增益,尤海辉,唐义军,沈跃良,倪明江,池涌,严建华..基于自适应神经模糊推理系统的煤粉锅炉飞灰含碳量建模[J].热力发电,2018,47(1):26-32,7.基金项目
浙江省科技计划项目(2014C33018) Science and Technology Plan Project of Zhejiang Province (2014C33018) （2014C33018）
