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

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.

Soft - sensing modeling based on ABC - MLSSVM inversion for marine low - temperature alkaline protease MP fermentation process.

BACKGROUND: Aiming at the characteristics of nonlinear, multi-parameter, strong coupling and difficulty in direct on-line measurement of key biological parameters of marine low-temperature protease fermentation process, a soft-sensing modeling method based on artificial bee colony (ABC) and multiple least squares support vector machine (MLSSVM) inversion for marine protease fermentation process is proposed. METHODS: Firstly, based on the material balance and the characteristics of the fermentation process, the dynamic "grey box" model of the fed-batch fermentation process of marine protease is established. The inverse model is constructed by analyzing the inverse system existence and introducing the characteristic information of the fermentation process. Then, the inverse model is identified off-line using MLSSVM. Meanwhile, in order to reduce the model error, the ABC algorithm is used to correct the inverse model. Finally, the corrected inverse model is connected in series to the marine alkaline protease MP fermentation process to form a composite pseudo-linear system, thus, real-time on-line prediction of key biological parameters in fermentation process can be realized. RESULTS: Taking the alkaline protease MP fermentation process as an example, the simulation results demonstrate that the soft-sensing modeling method can solve the real-time prediction problem of key biological parameters in the fermentation process on-line, and has higher accuracy and generalization ability than the traditional soft-sensing method of support vector machine. CONCLUSIONS: The research provides a new method for soft-sensing modeling of key biological parameters in fermentation process, which can be extended to soft-sensing modeling of general nonlinear systems.

Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process.

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model's adaptive abilities in various operation conditions and can improve its generalization ability.