Abstract: Most of chemical processes with nonlinear characteristics are difficult to model by general linear modeling approaches in practice.Hence, a novel recurrent neural network integrated with priori-knowledge for modeling nonlinear dynamic processes was presented.In the form of non-linear constraints, the priori-knowledge exploited from industrial chemical processes was embedded into the feed-forward neural network with the NARMAX(nonlinear autoregressive moving average with exogenous input)structure.Meanwhile, based on the augmented Lagrange multiplier(ALM)method, a hybrid PSO-IPOPT algorithm was introduced for network weight optimization.The PKRNN model with process-priori-knowledge constraint either ensured good dynamic modeling and prediction(especially extrapolation)ability, or guaranteed safety in the implementation of industrial chemical processes.The effectiveness of the PKRNN model was validated by an actual double-loop liquid propylene polymerization reaction process.

Key words: process-priori-knowledge, recurrent neural network, augmented Lagrange multiplier, particle swarm optimization-interior-point optimization, propylene polymerization reaction process

摘要： 大部分化工过程具有非线性特性,一般的线性建模方法难以有效应用。针对非线性化工过程动态建模,提出了一种基于过程先验知识的递归神经网络模型,充分发掘化工过程隐含的先验知识,并将这些先验知识以非线性约束的形式嵌入NARMAX结构的前馈神经网络中,同时基于增广拉格朗日乘子法约束处理机制,用PSO-IPOPT混合优化算法对过程先验知识递归神经网络权值进行优化。该过程先验知识递归神经网络模型对非线性化工过程动态建模,不仅有良好的建模精度和预测外推能力,而且能避免零增益的出现和增益反转,确保网络模型在实际应用中的安全性。文中以环管式丙烯聚合反应过程实际工业数据验证了所提网络模型的有效性。

关键词: 过程先验知识, 递归神经网络, 增广拉格朗日乘子法, 粒子群-内点法优化算法, 丙烯聚合反应过程