基于PLS子空间对齐的2,6-二甲酚纯度迁移学习建模  

Transfer Learning Modeling of 2,6-Dimethylphenol Purity Based on PLS Subspace Alignment

作　　者：邬云飞 栾小丽 刘飞 WU Yun-fei;LUAN Xiao-li;LIU Fei(Key Laboratory of Advanced Process Control for Light Industry of the Ministry of Education,Institute of Automation,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学自动化研究所,轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《光谱学与光谱分析》2022年第11期3608-3614,共7页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61991402,61833007)资助。

摘　　要：采用近红外光谱对物质浓度进行准确的在线检测对于生产优化具有重要意义。建立检测模型需要从近红外光谱中提取相关信息,代表性样本越多,提取的信息越有效,所建模型的精度越高。随着产品纯度的提高,样本的区分度下降,样本的变异系数小,多样性不足,并且存在测量噪声以及化验室人工检测样品浓度值时的测量误差,会导致物质浓度与光谱之间缺乏相关性,传统的建模方法无法建立可靠的近红外检测模型。为了解决这个问题,提出了一种基于PLS子空间对齐的迁移学习建模方法,应用于2,6-二甲酚精馏提纯过程中产品塔高纯度产品的在线检测。在制备化工单体2,6-二甲酚过程中,存在副反应和未反应完全的杂质,生产反应后的物料要顺序经过不同的精馏塔,最后在产品塔获得纯度高于99%的产品,产品塔的质量检测尤为重要。由于产品塔检测点近红外光谱数据缺乏多样性,检测模型的泛化能力较弱。该研究采用偏最小二乘为2,6-二甲酚精馏提纯过程中不同检测点的数据集创建子空间,然后通过最小化其他检测点数据子空间与产品塔检测点数据子空间的布雷格曼(Bregman)散度,将其他检测点数据的子空间对齐到产品塔数据子空间,减小其他检测点数据子空间与产品塔检测点数据子空间的特征分布差异,既避免了投影到公共子空间产品塔检测点数据特征信息的损失,又能充分利用其他检测点数据的特征信息,然后在迁移后的子空间完成偏最小二乘回归建模,通过竞争学习加权策略确定最终的模型系数,从而提升产品塔检测模型的性能。在2,6-二甲酚纯度近红外检测数据集上进行了仿真验证,并探讨了迁移其他检测点不同数量的数据对产品塔检测模型性能的影响,产品塔检测模型的最大性能提升达到了52.19%, RMSEP值由0.059 4下降到0.028 4,与传统建模方法支持向量机回归和BThe highly accurate on-line detection of solute concentration by using near-infrared spectroscopy analysis technology is of great significance for production optimization. Establishing a detection model needs to extract relevant information from the near-infrared spectrum, more representative samples will extract more effective information, and the model will also be more accurate. However, the purity of products has been continuously improved, and sample discrimination is reduced. The coefficient of variation of the sample is small, which leads to the diversity of samples being insufficient. Moreover, there are measurement errors when measuring the sample concentration in the laboratory, which will lead to the lack of correlation between the solute concentration and the spectrum. It is hard to establish a reliable and highly accurate near-infrared detection model. A new transfer learning modeling method based on PLS subspace alignment is proposed and applied in the near-infrared on-line detection of 2,6-dimethylphenol high purity in the product tower of the distillation purification process. In preparing the chemical monomer 2,6-dimethylphenol, there are side reactions and unreacted impurities, the materials after reaction must flow through different rectifying towers in sequence, and finally, the product with purity higher than 99% is obtained in the product tower. The product quality inspection of the product tower is particularly important. Due to the lack of diversity of the detection data of product tower detection point, the generalization ability of the detection model is weak. We create subspaces for the data sets of different detection points in the separation and purification of 2,6-dimethylphenol using the partial least squares method. Then, a mapping that aligns the other tower subspace into the product tower is learned by minimizing a Bregman matrix divergence function, reducing the feature distribution discrepancy between other towers and product towers. It avoids information loss in product tower

关 键 词：近红外光谱 迁移学习 子空间对齐 2 6-二甲酚 精馏提纯 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]