A soft sensor model of Pichia pastoris cell concentration based on IBDA-RELM

Abstract

For Pichia pastoris 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 Pichia pastoris 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 Pichia pastoris cell concentration under multi-operating conditions.

Keywords:

• Domain adaptation
• Pichia pastoris
• soft sensor
• transfer learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is especially grateful for the technical and financial support of the Key R&D Program in Zhenjiang City: Social Development Project [SH2020005], the Key R&D Program in Zhenjiang City: R&D on soft-sensing and control of key parameters for microbial fermentation [SH2017002], National Science Research Foundation of China [41376175], Natural Science Foundation of Jiangsu Province [BK20140568, BK20151345] and a project funded by the priority academic program development of Jiangsu higher education institutions (PAPD).