A Soft Sensor Modeling Method Based on Local Migration Modeling Framework

To address the issues of low model fitting accuracy and insufficient prediction capability caused by the multi-stage characteristics and batch-to-batch data distribution heterogeneity in the fermentation process of Pichia pastoris, this study proposed a novel soft sensor modeling method with deep transfer learning (DTL) strategies to propose a novel soft sensor modeling method based on a local transfer modeling framework. Fermentation process data were partitioned into multiple sub-source domains using the K-means clustering algorithm. For each sub-source domain, Deep Neural Networks (DNNs) were employed to establish prediction models, which were further optimized using an improved firefly algorithm. The Euclidean distance between the target domain samples and the cluster centroids of each sub-source domain was calculated to perform correlation analysis and identification. The sub-source domain with the highest correlation to the target domain samples was selected, and a deep transfer fine-tuning method was applied to optimize the corresponding sub-source domain model, ultimately obtaining the target domain prediction model. The experimental results indicated that the proposed method extracts local feature information from fermentation process data, enhancing prediction accuracy and model generalization performance. This provides a viable approach for soft sensor modeling in multi-condition fermentation scenarios of Pichia pastoris fermentation processes.