Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes

doi:10.11949/j.issn.0438-1157.20181355

Abstract

Abstract:

The chemical production processes are increasingly complex and the traditional extreme learning machine (ELM) cannot effectively model the chemical processes data. To tackle this problem, a novel PCE-RELM model based on principal components extraction (PCE) is proposed. Through principal component analysis of the ELM hidden layer, the principal component features of the data are extracted, the linear correlation between variables is removed, and the research problem is simplified. The influence of the number of hidden layer nodes on the accuracy of the model can be reduced, the number of hidden layer nodes in the ELM can be quickly and randomly selected, and the ELM model becomes robust. To verify the effectiveness of the proposed method, the PCE-RELM model was applied to modeling the purified terephthalic acid (PTA) production process. The simulation results show that, compared with the traditional ELM, the PCE-RELM model has the advantages of simple design, good robustness and high accuracy, which can guide the chemical process control and analysis.

Key words: extreme learning machine, neural network, principal components analysis, processes modeling, chemical production, process control

摘要：

化工生产过程日益复杂，传统极限学习机(extreme learning machine, ELM)无法有效地对化工过程数据建模。针对该问题，提出一种基于主元提取(principal components extraction, PCE)的鲁棒极限学习机(PCE-RELM)。通过对ELM隐含层进行主元分析，提取数据的主元特征，去除变量间的线性相关性，简化研究问题。可以减小隐含层节点数对模型精度的影响，实现对ELM隐含层节点数的快速随机选取，同时使ELM具有鲁棒性。为验证提出方法的有效性，将PCE-RELM模型应用于精对苯二甲酸(purified terephthalic acid,PTA)生产过程建模。仿真结果显示，相比传统的ELM，PCE-RELM模型具有设计简单、鲁棒性好、精度高等优势，可以对化工过程控制、分析起到指导作用。

关键词: 极限学习机, 神经网络, 主元分析, 过程建模, 化工生产, 过程控制

CLC Number:

TP 29

Xiaohan ZHANG, Pingjiang WANG, Xiangbai GU, Yuan XU, Yanlin HE, Qunxiong ZHU. Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes[J]. CIESC Journal, 2019, 70(2): 475-480.

张晓晗, 汪平江, 顾祥柏, 徐圆, 贺彦林, 朱群雄. 基于主元提取的鲁棒极限学习机研究及其化工建模应用[J]. 化工学报, 2019, 70(2): 475-480.

Figures/Tables 6

References 30

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输入变量编号	变量名称
1	蒸汽流量
2	水回流
3	进料量
4	乙酸正丁酯侧回流
5	乙酸正丁酯主回流
6	回流罐液位
7	进料温度
8	回流温度
9	顶塔温度
10	顶塔产量
11	第三十五层塔板上方温度
12	进料成分(乙酸含量)
13	高灵敏板附近的塔板温度
14	低灵敏板附近的塔板温度
15	第三十五层塔板与第四十层塔板之间的温度
16	第四十四层塔板与第五十层塔板之间的温度
17	第五十三层塔板与第五十八层塔板之间的温度

输入变量编号	变量名称
1	蒸汽流量
2	水回流
3	进料量
4	乙酸正丁酯侧回流
5	乙酸正丁酯主回流
6	回流罐液位
7	进料温度
8	回流温度
9	顶塔温度
10	顶塔产量
11	第三十五层塔板上方温度
12	进料成分(乙酸含量)
13	高灵敏板附近的塔板温度
14	低灵敏板附近的塔板温度
15	第三十五层塔板与第四十层塔板之间的温度
16	第四十四层塔板与第五十层塔板之间的温度
17	第五十三层塔板与第五十八层塔板之间的温度

隐含层节点数	PCE-RELM	ELM
10	0.010067	0.008021
30	0.010250	0.006363
50	0.010186	0.006707
100	0.010212	0.027203
300	0.010275	0.021646
500	0.010337	0.019315
1000	0.010252	0.016531
10000	0.010318	0.015991

隐含层节点数	PCE-RELM	ELM
10	0.010067	0.008021
30	0.010250	0.006363
50	0.010186	0.006707
100	0.010212	0.027203
300	0.010275	0.021646
500	0.010337	0.019315
1000	0.010252	0.016531
10000	0.010318	0.015991

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