A soft sensor based on nonlinear principal component analysis

An accurate on-line measurement of quality variables is essential for the successful monitoring and control tasks in chemical process operations. A soft sensor is developed based on nonlinear principal analysis (PCA), due to the ability of capturing the linear and nonlinear features of the data. The proposed method is applied to an industrial crude oil atmospheric distillation tower and is illustrated by comparisons with other familiar methods. The results have shown that the proposed method gives a better or equal performance over the conventional PCA method and neural networks method.