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A novel online model for furnace exit gas temperature of coal-fired boiler

机译：燃煤锅炉出炉煤气温度在线新模型

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Furnace exit gas temperature (FEGT) is the key parameter in the furnace ash fouling monitoring system. Since the standard least squares support vector machine (LSSVM) is not suitable for online identification and control of FEGT, a novel CM-LSSVM-PLS method is proposed to predict FEGT in this paper. In the process of CM-LSSVM-PLS method, c-means cluster (CM) algorithm is used to partition the training data into several different subsets by considering the characteristics of operational data. Submodels are subsequently developed in the individual subsets based on LSSVM method. Partial least squares algorithm (PLS) is employed as the combination strategy. The online updating algorithm is then applied to the CM-LSSVM-PLS model. The proposed online model is verified through operation data of a 300MW generating unit. The simulation results show that the proposed online updating model is effective for online FEGT forecasting.
机译：炉子出口气体温度（FEGT）是炉灰结垢监测系统中的关键参数。由于标准最小二乘支持向量机（LSSVM）不适合FEGT的在线识别和控制，因此本文提出了一种新颖的CM-LSSVM-PLS方法来预测FEGT。在CM-LSSVM-PLS方法的过程中，考虑到操作数据的特性，使用c均值聚类（CM）算法将训练数据划分为几个不同的子集。随后基于LSSVM方法在各个子集中开发子模型。偏最小二乘算法（PLS）被用作组合策略。然后将在线更新算法应用于CM-LSSVM-PLS模型。通过300MW发电机组的运行数据验证了提出的在线模型。仿真结果表明，所提出的在线更新模型对于在线FEGT预报是有效的。

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