A dynamic soft senor modeling method based on MW-ELWPLS in marine alkaline protease fermentation process

• Published in: Preparative biochemistry & biotechnology Vol. 51; no. 5; pp. 430 - 439
• Main Authors: Zhu, Xianglin, Cai, Ke, Wang, Bo, Rehman, Khalil Ur
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 2021; Taylor & Francis Ltd
• Subjects: Algorithms; Alkaline protease; Datasets; Ensemble learning; Fermentation; Learning; locally weighted partial least squares (LWPLS); Machine learning; marine alkaline protease (MP); Modelling; moving window (MW); Optimal control; Protease; soft sensor; State variable; soft sensor; locally weighted partial least squares (LWPLS); moving window (MW); Ensemble learning; marine alkaline protease (MP)
• ISSN: 1082-6068; 1532-2297
• DOI: 10.1080/10826068.2020.1827428

The vital state variables in marine alkaline protease (MP) fermentation are difficult to measure in real-time online, hardly is the optimal control either. In this article, a dynamic soft sensor modeling method which combined just-in-time learning (JITL) technique and ensemble learning is proposed. First, the local weighted partial least squares algorithm (LWPLS) with JITL strategy is used as the basic modeling method. For further improving the prediction accuracy, the moving window (MW) is used to divide sub-dataset. Then the MW-LWPLS sub-model is built by selecting the diverse sub-datasets according to the cumulative similarity. Finally, stacking ensemble-learning method is utilized to fuse each MW-LWPLS sub-models. The proposed method is applied to predict the vital state variables in the MP fermentation process. The experiments and simulations results show that the prediction accuracy is better compared to other methods.