基于Ms-NIPLS-GPR的化工过程性能等级评估方法

doi:10.11949/0438-1157.20201752

摘要/Abstract

摘要：

针对化工过程中因输入输出数据间非线性关系造成在线性能评估准确度不足的问题，提出一种基于多数据空间非线性迭代偏最小二乘和高斯过程回归（multi-space nonlinear iterative partial least squares and Gaussian process regression，Ms-NIPLS-GPR）的性能分级评估方法。首先将性能相近的过程历史数据划分成不同性能等级的集合，利用Ms-NIPLS方法提取不同性能等级训练数据的特征子空间，然后用GPR获得特征子空间与性能等级标签之间的非线性映射结构，建立输入数据与性能等级之间的离线模型。得到模型后，在线评估当前过程性能等级，同时通过构造过渡性能系数来区分稳态性能等级和稳态性能等级间的过渡性能状态。最后，将该方法应用到乙烯裂解过程在线性能评估中，说明该性能评估方法的有效性和准确性。

关键词: 多数据空间, 非线性迭代偏最小二乘, 高斯过程回归, 模型, 稳态, 过渡, 性能评估

Abstract:

A performance assessment method based on multi-space nonlinear iterative partial least squares(Ms-NIPLS) and Gaussian process regression(GPG) is proposed to solve the insufficient accuracy caused by the non-linear relationship between input and output data of chemical process. First, this method divides the similar historical datasets into different sets of performance grades, extracting the feature subspaces of training datasets by Ms-NIPLS method, and then using GPR to obtain a non-linear mapping structure between the feature subspaces and the performance grades labels to achieve off-line modeling. With the model obtained, current performance can be assessed online, and the transition performance coefficient is constructed to distinguish the steady-state performance grades and the transition performance states between the steady-state performance grades. Finally, the method in this paper is applied to the online performance assessment of ethylene cracking process to illustrate the effectiveness and accuracy of the performance assessment method proposed.

Key words: multi-space, nonlinear iterative partial least squares, Gaussian process regression, model, steady state, transition, performance assessment

中图分类号:

王浩东, 王昕, 王振雷, 曹晨鑫. 基于Ms-NIPLS-GPR的化工过程性能等级评估方法[J]. 化工学报, 2021, 72(3): 1549-1556.

WANG Haodong, WANG Xin, WANG Zhenlei, CAO Chenxin. Grading performance assessment method of chemical process based on Ms-NIPLS-GPR[J]. CIESC Journal, 2021, 72(3): 1549-1556.

图/表 11

图1 性能等级数据离线建模过程

Fig.1 Offline modeling process of performance grade data

表1 乙烯裂解炉过程变量

Table 1 Ethylene cracking furnace process variables

No.	变量	描述	单位
1	ρ_NAP	裂解原料密度	kg·m^-3
2	C_NP	正链烷浓度	%
3	C_IP	异构烷烃浓度	%
4	C_OLE	烯烃浓度	%
5	C_NAP	环戊烃浓度	%
6	C_BTX	芳烃浓度	%
7	F_feed	原料流量	t·h^-1
8	F_DS	稀释蒸汽流量	t·h^-1
9	F_Bfuel	底部燃料流量	m³·h^-1
10	F_Sfuel	侧壁燃料流量	m³·h^-1
11	C_o2	排烟氧含量	%
12	T_g1	排烟温度1	℃
13	T_g2	排烟温度2	℃
14	F_ss	高压蒸汽流量	kg·h^-1
15	T_ss	高压蒸汽温度	℃
16	COT	裂解出口温度	℃
17	T_HK	初馏点温度	℃
18	T_KK	终馏点温度	℃

图2 乙烯裂解炉工艺

Fig.2 Ethylene cracking furnace process

图3 不经过滑动时间窗的数据采样点各通道输出情况

Fig.3 The sample curves of the channel outputs without the sliding data window

图4 基于PCA-NN的在线评估结果

Fig.4 PCA-NN-based online assessment results

图5 基于Ms-PLS-NN的在线评估结果

Fig.5 Ms-PLS-NN-based online assessment results

图6 基于Ms-NIPLS-GPR的在线评估结果

Fig.6 Ms-NIPLS-GPR-based online assessment results

图7 基于PCA-NN的在线评估过渡性能系数曲线

Fig.7 Performance transition coefficient curve based on PCA-NN online assessment method

图8 基于Ms-PLS-NN的在线评估过渡性能系数曲线

Fig.8 Performance transition coefficient curve based on Ms-PLS-NN online assessment method

图9 基于Ms-NIPLS-GPR的在线评估过渡性能系数曲线

Fig.9 Performance transition coefficient curve based on Ms-NIPLS-GPR online assessment method

表2 在线评估结果与实际情况对比

Table 2 Comparison of online assessment result and actual

性能等级	实际情况	PCA_NN	Ms-PLS-NN	Ms-NIPLS-GPR
最优	1~442	1~106 122~454	1~105 123~342 359~449	1~456
过渡	443~486	455~495	450~480	457~484
中等	487~994	496~1022	481~1024	485~1014
过渡	995~1033	1023~1118	1025~1039	1015~1036
较差	1034~1250	1119~1250	1040~1098 1117~1250	1037~1250
过渡	—	107~121	106~122 343~358 1099~1116	—
准确率	—	88.08%	92%	96.88%

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