关键词: 回声状态网络, 软测量, 动态建模, 预测, 算法, 化工过程

Abstract: Soft sensor dynamic modeling based on echo state networks (ESN) with leaky-integrator neurons was proposed, in which the off-line learning algorithm using ridge regression and on-line learning algorithm using recursive least squares (RLS) were given respectively. By adding a regularization coefficient, ridge regression algorithm could control large sizes of output weight matrix and improve the properties of ESN solution. On-line learning algorithm could allow on-line processing of large data sets and attain requirement of real time for process modeling. Leaky integrator ESN (LiESN) was used to estimate the butane(C4) concentration in the bottom flow of a debutanizer column and to compute sulfur recovery unit (SRU) tail gas composition for improving product quality monitoring and control in a refinery. Simultaneously, the modeling performance was evaluated by 4-plot analysis of model residuals. Compared with existing soft sensor modeling such as ESN, support vector machines (SVM) etc, under the same condition, experimental results confirmed that LiESN could achieve better performance and the accuracy of the model could meet practical need.

Key words: echo state networks, soft sensor, dynamic modeling, prediction, algorithm, chemical processes