基于Tri-training GPR的半监督软测量建模方法

doi:10.11949/0438-1157.20240322

摘要/Abstract

摘要：

集成学习因通过构建并结合多个学习器，常获得比单一学习器显著优越的泛化能力。但是在标记数据比例较少时，建立高性能的集成学习软测量模型依然是个挑战。针对这一个问题，提出一种基于半监督集成学习的软测量建模方法——Tri-training GPR 模型。该建模策略充分发挥了半监督学习的优势，减轻建模过程对标记样本数据的需求，在低数据标签率下，仍能通过对无标记数据进行筛选从而扩充可用于建模的有标记样本数据集，并进一步结合半监督学习和集成学习的优势，提出一种新的选择高置信度样本的思路。将所提方法应用于青霉素发酵和脱丁烷塔过程，建立青霉素和丁烷浓度预测软测量模型，与传统的建模方法相比获得了更优的预测结果，验证了模型的有效性。

关键词: 软测量, 集成学习, 半监督学习, Tri-training, 高斯过程回归, 过程控制, 动力学模型, 化学过程

Abstract:

Ensemble learning often achieves significantly superior generalization capabilities than a single learner by building and combining multiple learners. However, it is still a challenge to build a high-performance ensemble learning soft sensor model when the proportion of labeled data is small. In order to solve this problem, this paper proposes a soft-sensor modeling method based on the semi-supervised ensemble learning: Tri-training Gaussian process regression (Tri-training GPR) model. The modeling strategy gives full play to the advantages of semi-supervised learning, reducing the demand for labeled sample data in the modeling process. Under low data labeling rate, the labeled sample data set for modeling can still be expanded by filtering unlabeled data. Furthermore, a new idea of selecting high-confidence samples is proposed by combining the advantages of semi-supervised learning and ensemble learning. The proposed method was applied to the penicillin fermentation and debutanization tower process, and the soft sensor models for predicting penicillin and butane concentrations were established. Compared with the traditional modeling methods, the proposed method obtained better prediction results, which verified the effectiveness of the model.

Key words: soft senor, ensemble learning, semi-supervised learning, Tri-training, Gaussian process regression, process control, kinetic modeling, chemical processes

中图分类号:

TQ 018

马君霞, 李林涛, 熊伟丽. 基于Tri-training GPR的半监督软测量建模方法[J]. 化工学报, 2024, 75(7): 2613-2623.

Junxia MA, Lintao LI, Weili XIONG. A semi-supervised soft sensor modeling method based on the Tri-training GPR[J]. CIESC Journal, 2024, 75(7): 2613-2623.

图/表 15

图1 Tri-training GPR模型框架图

Fig.1 The framework of Tri-training GPR model

图2 青霉素发酵工艺流程图

Fig.2 The process flow diagram of penicillin fermentation

表1 建立青霉素浓度预测模型所用变量

Table 1 Variables used to establish a penicillin concentration prediction model

序号	变量描述
$x 1$	搅拌功率/ $W$
$x 2$	底物进料速度/ $L / h$
$x 3$	溶解氧浓度/ $g / L$
$x 4$	产生热量/ $k c a l$
$x 5$	培养体积/ $L$
$x 6$	二氧化碳浓度/ $g / L$
$x 7$	pH
$x 8$	青霉素浓度/ $g / L$

表1 建立青霉素浓度预测模型所用变量

Table 1 Variables used to establish a penicillin concentration prediction model

序号	变量描述
$x 1$	搅拌功率/ $W$
$x 2$	底物进料速度/ $L / h$
$x 3$	溶解氧浓度/ $g / L$
$x 4$	产生热量/ $k c a l$
$x 5$	培养体积/ $L$
$x 6$	二氧化碳浓度/ $g / L$
$x 7$	pH
$x 8$	青霉素浓度/ $g / L$

表2 不同标签率下4种建模方法的预测RMSE对比

Table 2 Comparison of four modeling methods for predicting RMSE under different label rates

方法	预测均方根误差
方法	50%	35%	25%	15%	10%
PLS	0.0329	0.0365	0.0370	0.0416	0.0441
GPR	0.0182	0.0193	0.0228	0.0274	0.0317
SSEGPR	0.0190	0.0205	0.0230	0.0266	0.0305
Tri-training GPR	0.0170	0.0181	0.0203	0.0254	0.0290

图3 不同标签率下3种建模方法青霉素浓度预测误差

Fig.3 The prediction errors under different label rates with three modeling methods for penicillin concentration

图4 不同标签率下3种建模方法对青霉素浓度的跟踪效果

Fig.4 The tracking effect of three modeling methods on penicillin concentration under different label rates

表3 不同标签率下3种建模方法的TP对比

Table 3 Comparison of three modeling methods for TP under different label rates

方法	TP
方法	50%	25%	10%
PLS	0.9942	0.9935	0.9926
GPR	0.9986	0.9954	0.9930
Tri-training GPR	0.9987	0.9980	0.9952

图5 不同建模方法在标签率25%下对青霉素浓度的预测结果散点图

Fig.5 Scatter plot of prediction results of penicillin concentration using different modeling methods at a label rate of 25%

表4 在不同信噪比、不同标签率下Tri-training GPR的预测RMSE对比

Table 4 Comparison of predicted RMSE of Tri-training GPR under different signal-to-noise ratios and different label rates

信噪比	预测均方根误差
信噪比	50%	35%	25%	15%	10%
80	0.0170	0.0181	0.0203	0.0254	0.0290
20	0.0181	0.0245	0.0262	0.0346	0.0372
10	0.0224	0.0381	0.0433	0.0500	0.0591

图6 脱丁烷塔过程工艺流程图

Fig.6 The process flow diagram of debutanizer tower

表5 建立丁烷浓度预测模型辅助变量

Table 5 The auxiliary variables for establishing a butane concentration prediction model

序号	变量描述
$x 1$	塔顶温度
$x 2$	塔顶压力
$x 3$	回流的流量
$x 4$	流入下一过程的流量
$x 5$	第6级塔板温度
$x 6$	塔板温度1
$x 7$	塔板温度2

表5 建立丁烷浓度预测模型辅助变量

Table 5 The auxiliary variables for establishing a butane concentration prediction model

序号	变量描述
$x 1$	塔顶温度
$x 2$	塔顶压力
$x 3$	回流的流量
$x 4$	流入下一过程的流量
$x 5$	第6级塔板温度
$x 6$	塔板温度1
$x 7$	塔板温度2

表6 不同标签率下4种模型丁烷浓度预测RMSE对比

Table 6 Comparison of RMSE prediction for butane concentration using four models under different label rates

方法	预测均方根误差
方法	60%	50%	30%	10%
PLS	0.0916	0.0953	0.0976	0.1057
GPR	0.0721	0.0789	0.0844	0.0983
SSEGPR	0.0708	0.0773	0.0903	0.1186
Tri-training GPR	0.0713	0.0770	0.0827	0.0977

图7 不同标签率下3种建模方法丁烷浓度预测误差

Fig.7 The predicting butane concentration error under different label rates with three modeling methods

表7 不同标签率下3种建模方法的TP对比

Table 7 Comparison of three modeling methods for TP under different label rates

方法	TP
方法	60%	50%	30%	10%
PLS	0.2341	0.2040	0.1936	0.1192
GPR	0.5693	0.5353	0.4905	0.1333
Tri-training GPR	0.7348	0.7087	0.5786	0.2733

图8 标签率30%下3种建模方法对丁烷浓度的跟踪效果

Fig.8 The tracking effect of three modeling methods on butane concentration under a label rate of 30%

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