Jiangsu University, Zhenjiang, China.
Prep Biochem Biotechnol. 2022;52(6):618-626. doi: 10.1080/10826068.2021.1980799. Epub 2021 Oct 20.
For fermentation process with multi-operating conditions, it is difficult to predict the cell concentration under the new operating conditions by the soft sensor model established under the specific operating conditions. Inspired by the idea of transfer learning, a method based on an improved balanced distribution adaptive regularization extreme learning machine (IBDA-RELM) was proposed to solve the problem. The domain adaptation (DA) method in transfer learning is developed to reduce distribution distance by transforming data. However, the joint distribution adaptation (JDA) and the balanced distribution adaptation (BDA) in DA cannot be directly applied to regression problems. The fuzzy sets (FSs) method was proposed to solve this issue. Finally, a soft sensor model of cell concentration was realized by inputting the converted data to the RELM model. Simulation verification was carried out with three operating conditions at the scene of fermentation. The transfer effects of three DA methods, including transfer component analysis (TCA), improved joint distribution adaptation (IJDA) as well as IBDA, were compared. The predicted results show that IBDA-RELM had a better performance in the soft sensor of cell concentration under multi-operating conditions.
对于具有多种操作条件的发酵过程，通过在特定操作条件下建立的软传感器模型，难以预测新操作条件下的细胞浓度。受迁移学习思想的启发，提出了一种基于改进的平衡分布自适应正则化极限学习机（IBDA-RELM）的方法来解决该问题。迁移学习中的域自适应（DA）方法通过转换数据来减小分布距离。然而，DA 中的联合分布自适应（JDA）和平衡分布自适应（BDA）不能直接应用于回归问题。提出了模糊集（FS）方法来解决这个问题。最后，通过将转换后的数据输入 RELM 模型，实现了细胞浓度的软传感器模型。在发酵现场的三个操作条件下进行了仿真验证，比较了三种 DA 方法，包括迁移成分分析（TCA）、改进的联合分布自适应（IJDA）和 IBDA 的迁移效果。预测结果表明，IBDA-RELM 在多操作条件下的细胞浓度软传感器中具有更好的性能。
