A soft sensor model based on compression sensing (CS) and least square support vector machine (LSSVM) was proposed to predict the NOx concentration in flue gas.The mapping matrix of the LSSVM was sparsified by the least support orthogonal matching pursuit algorithm (LS-OMP) in the compression sensing theory,and then used in soft sensor model building.Compared with the conventional LSSVM model,this model reduces the computational cost and improves the real-time performance by sparsifying the mapping matrix.Compared with the conventional sparse-LSSVM model,LSSVM model needs to make new support vectors sparse after modeling.The CS-LSSVM model reduces the computational cost and improves the soft-sensing accuracy through once compression.The model was used in soft sensor of NOx emission from a coal-fired boiler.The simulation results show that,only 50％ support vectors is enough to achieve good performance with the model and provides data support for online monitoring of the NOx emission.%提出了一种基于压缩感知(CS)和最小二乘支持向量机(LSSVM)构成的压缩感知最小二乘支持向量机(CS-LSSVM)软测量模型,用于预测烟气中的NOx质量浓度.利用压缩感知理论中的最小二乘匹配追踪算法(LS-OMP)对LSSVM在建模过程中的映射矩阵进行压缩,采用压缩后的稀疏映射矩阵直接建立CS-LSSVM软测量模型.与传统LSSVM模型相比,本模型通过稀疏映射矩阵,降低了运算成本的同时提高了模型的计算速度;与传统稀疏化LSSVM (Sparse-LSSVM)模型相比,LSSVM模型仍需要在建模后不断稀疏新输入的支持向量,本文CS-LSSVM模型仅通过在建模过程中一次性压缩,降低了运算成本的同时提高了软测量精度,将该模型用于电厂燃煤锅炉NQ排放的软测量中,现场数据仿真结果表明,用本文提出的方法以50％的支持向量就能达到很好的表现能力,为现场NOx的在线软测量提供了数据支持.
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