Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning.

Background: 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.

Methods: 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 $W_s$ and $W_t$ 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.

Results: 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.

Conclusion: 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.