Wang Bo, Wei Jun, Zhang Le, Jiang Hui, Jin Cheng, Huang Shaowen
Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China.
Wuxi Key Laboratory of Intelligent Robot and Special Equipment Technology, Wuxi Taihu University, Wuxi, 214064, China.
BMC Biotechnol. 2024 Dec 18;24(1):104. doi: 10.1186/s12896-024-00928-4.
Aiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model.
This paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructure domains, and adaptively weight the substructure domains according to the distances between the sub-source domains and sub-target domains. Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices and that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model.
Taking Pichia pastoris fermentation to produce inulinase as an example, the simulation results verify that the root mean square error of the proposed soft sensor model in predicting Pichia pastoris concentration and inulinase concentration is reduced by 48.7% and 54.9%, respectively.
The proposed soft sensor modeling method can accurately predict Pichia pastoris concentration and inulinase concentration online under different working conditions, and has higher prediction accuracy than the traditional soft sensor modeling method.
针对传统迁移方法在整体域级迁移中容易丢失数据信息，且难以实现源域与目标域完美匹配，从而降低软测量模型精度的问题。
本文提出一种基于子结构域迁移建模框架的软测量建模方法。首先，利用高斯混合模型聚类算法提取局部信息，将源域和目标域聚类为多个子结构域，并根据子源域与子目标域之间的距离对子结构域进行自适应加权。其次，采用集成多度量的最优子空间域自适应方法，获得相互耦合的最优投影矩阵 和 ，将源域和目标域的数据投影到相应子空间进行空间对齐，以减小不同工况样本数据之间的差异。最后，基于子结构域自适应后的源域和目标域数据，采用最小二乘支持向量机算法建立预测模型。
以毕赤酵母发酵生产菊粉酶为例，仿真结果验证了所提软测量模型预测毕赤酵母浓度和菊粉酶浓度的均方根误差分别降低了48.7%和54.9%。
所提软测量建模方法能够在不同工况下准确在线预测毕赤酵母浓度和菊粉酶浓度，且预测精度高于传统软测量建模方法。
