Elman Neural Network Soft-Sensor Model of PVC Polymerization Process Optimized by Chaos Beetle Antennae Search Algorithm

被引：9

作者：

Gao, Shuzhi ^{[1
]}

Zhang, Yimeng ^{[1
]}

Zhang, Yimin ^{[1
]}

Zhang, Guoguang ^{[1
]}

机构：

[1] Shenyang Univ Chem Technol, Equipment Reliabil Inst, Shenyang 110142, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2021年 / 21卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Polymers; Feature extraction; Prediction algorithms; Optimization; Predictive models; Recurrent neural networks; PVC polymerization process; multi-cluster feature selection; soft-sensor; Elman neural networks; chaotic map; beetle antennae search algorithm; FLOW;

D O I：

10.1109/JSEN.2020.3026550

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The conversion rate of vinyl chloride monomer (VCM) is an important product quality indicator in the process of Polyvinyl chloride (PVC) polymerization. Due to the complexity of the PVC polymerization process and the limitation of site conditions, it is difficult to obtain the VCM conversion rate online in real time.Therefore, this article puts forward a soft-sensor model based on Beetle Antennae Search Algorithm (BAS) to optimize Elman neural network(Elman). Firstly, Multi-Cluster Feature Selection (MCFS) is used to reduce the dimensionality of the high-dimensional input variables, so that we get auxiliary variables of the soft-sensor model. Then, using Elman neural network as a soft-sensor model, and it is trained by the proposed optimization algorithm, which combines the chaotic map and the Beetle Antennae Search Algorithm (CBAS). The simulation results show that the model can significantly improve the prediction accuracy of the VCM conversion rate while realizing the real-time control of the PVC polymerization production process.

引用

页码：3544 / 3551

页数：8

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