針對SMB 色譜分離過程中組分純度難于實時在線測量的問題,建立了一種基于BP 神經(jīng)網(wǎng)絡(luò)的軟測量模型。首先對實際生產(chǎn)中的原始數(shù)據(jù)，經(jīng)過過失誤差剔除及濾波處理后得到一套訓(xùn)練數(shù)據(jù)和校驗數(shù)據(jù)組成訓(xùn)練樣本，然后采用BP 神經(jīng)網(wǎng)絡(luò)進行訓(xùn)練，得到組分純度的非參數(shù)模型。為加快網(wǎng)絡(luò)收斂速度，采用改進BP算法對其進行訓(xùn)練。在MATLAB 工作平臺上進行了大量的仿真研究，對該模型進行驗證，仿真結(jié)果表明，該
方法的有效性。
關(guān)鍵詞：模擬移動床;軟測量;人工神經(jīng)元網(wǎng)絡(luò);BP 算法;改進BP 算法
Abstract: In order to realize the online measurement in SMB separation process, a soft sensor model based on BP neural network which measures two components purity is built up. First by filter process of practical primal data, the training samples are gained. Then the nonparametric model on component purity is developed by use of BP neural network. Improved BP algorithm is applied to fast convergence. Much computer simulation on Matlab validates this method.
Keywords: Simulated moving bed; Soft-sensor; Artificial neural network (ANN); BP algorithm; Improved BP algorithm