Semi-supervised regression based on Representation Learning for fermentation processes

Biofermentation faces challenges in obtaining real-time quality variables, making it necessary to predict these variables. However, the fermentation process data vary in length and lack sufficient labeled data for model establishment. To solve this problem, this study introduces a framework named RL-SSR(Representation Learning-based Semi-Supervised Regression). First, a data rotation mechanism is designed to address the issue of non-equal-length data. Second, representation learning pre-tasks containing contrastive learning and data reconstruction tasks are implemented to introduce a priori knowledge and numeric features. Finally, the pre- trained model will be fine-tuned with limited labeled data. Experimental results using an industrial-scale penicillin fermentation dataset reveal that RL-SSR outperforms other baseline models, particularly with a small number of labels, confirming the robustness and effectiveness of RL-SSR in the real-time prediction of quality variables in fermentation processes.