Data Driven Modeling Using an Optimal Principle Component Analysis Based Neural Network and Its Application to a Nonlinear Coke Furnace,Industrial & Engineering Chemistry Research

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Data Driven Modeling Using an Optimal Principle Component Analysis Based Neural Network and Its Application to a Nonlinear Coke Furnace
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-04-27 , DOI: 10.1021/acs.iecr.8b00071
Ridong Zhang ₁ , Qiang Lv ₁ , Jili Tao ₂ , Furong Gao ₃

Affiliation

In order to fully exploit the data information among process variables, an optimization based principle component analysis (PCA) using a neural network is proposed. First, a new RV similarity criterion of the PCA variable selection method is developed to select the main variables and construct the nonlinear industrial process. Second, a radial basis function neural network (RBFNN) is utilized to construct the nonlinear process model, where the modeling accuracy and RV criterion are optimized by an improved multiobjective evolutionary algorithm, namely, NSGA-II. To obtain the optimization of the structure and parameter of the RBFNN, encoding, prolong, and pruning operators are designed. The RBFNN with good generalization capability will then be obtained based on root mean squared error of the training and testing data considering the Pareto optimal solutions. The proposed approach has efficiently selected the main disturbance of the chamber pressure control loop in a coke furnace, and the RBFNN has obtained satisfactory data extraction accuracy compared with the other three typical methods.

中文翻译：

基于最优主成分分析的神经网络数据驱动建模及其在非线性焦炉中的应用

为了充分利用过程变量之间的数据信息，提出了使用神经网络的基于优化的主成分分析（PCA）。首先，开发了一种新的PCA变量选择方法的RV相似性准则，以选择主要变量并构建非线性工业过程。其次，利用径向基函数神经网络（RBFNN）构造非线性过程模型，通过改进的多目标进化算法NSGA-II优化建模精度和RV准则。为了优化RBFNN的结构和参数，设计了编码，延长和修剪运算符。然后，基于帕累托最优解的训练和测试数据的均方根误差，将获得具有良好泛化能力的RBFNN。所提出的方法有效地选择了焦炉内室压力控制回路的主要扰动，并且与其他三种典型方法相比，RBFNN获得了令人满意的数据提取精度。

更新日期：2018-04-28

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