Abstract: Due to the fact that the differential algebraic equations (DAEs) of considered chemical processes are often difficult to build, a new data-based modeling approach is proposed using ARX-neural network under partial least squares framework (ARX-NNPLS), where less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined neural network structure is used in the PLS inner model between the input and output latent variables. In the proposed ARX-NNPLS model based dynamic optimization strategy, neither parameterization nor iterative DAEs solving is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the considered process, and the computing time is greatly reduced comparing to the conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition problem in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach grows quicker to the target values and the computing time cost is much less.

摘要： 鉴于化工过程对象的微分代数方程(DAEs)通常难以准确获取的情况，本文给出了一种在PLS框架下的采用ARX和神经网络串联结构的数据建模方法(ARX-NNPLS)，建模过程只需要对象的输入输出数据和很少的过程对象知识，为体现对象的动态和非线性特性，在PLS内模型中运用ARX结合神经网络的结构表示输入输出隐变量之间的关系。在所提出的基于ARX-NNPLS模型的动态优化策略中，由于ARX-NNPLS模型结构很好地体现了对象的动态特性，在优化命题求解过程中不需要参数化处理和DAEs方程的迭代求解，相比基于DAEs的控制变量参数化方法求解时间能大大缩短。将基于ARX-NNPLS模型的优化策略用于气相流化床聚乙烯牌号切换过程中，仿真结果表明质量变量的最优轨迹比起控制变量参数化方法能更快地过渡到目标指标值，并且耗费的计算时间更少。