Research On Dynamic Modeling Method Based On Least Squares Support Vector Machine For Fermentation Process And Software Implementation

The microbial fermentation involves the pharmaceutical, food and other industries, and is closely related to economic development and people's everyday life. High cost and energy consumption are the characteristics of microbial fermentation. Therefore, in order to improve the fermentation unit and reduce production cost, it has become an important issue to realize optimal control of fermentation process.Since the fermentation process is a kind of very complex biochemical reaction process, the human have not fully made clear of its mechanism yet. What is more, the existing online biosensor is difficult to ensure measurement accuracy, the measurement of biomechanical parameters is mainly obtained through off-line analysis, it usually has a big lag which can't feedback control information in time. Therefore, the establishment of high-precision prediction model of fermentation process products becomes core content to be researched in optimal control.In this paper, we aim at the characteristics of fermentation process, including variability,nonlinear,irreversible and multivariable coupling. By comparing existing fermentation process modeling methods, a new online local modeling method is proposed for fed-batch fermentation processes based on dynamic time warping (DTW) and least squares support vector machine (LS_SVM). The major research findings and innovation are as follows:1) Online constructing similar training sample set based on DTW:A set of data within the sliding window is set as a query sequence in the current batch, and then using DTW as standard to judge the similarity of time series, we search for the most similar sub-sequence from the historical batch database to form the training set.2) Selection method of model input variables and parameters sensitivity analysis:By analyzing the influence on model MSE of different input variables and the kernel function through simulation experiments, I select RBF kernel function for the fermentation process and analyze sensitivity degree of the model accuracy onγandσ2, and determine the optimal range of super parameters.3) Online super parameters optimization method based on PSO-CV:Through analysis of cross-validation method used to determine the model's super parameters, a new online super parameters optimization method based on Particle Swarm Optimization(PSO)is proposed to minimize the K-CV error of for the model, it not only ensure accuracy of the model, but also take into account the model generalization ability. Results show that PSO-CV method has better performance compared with the grid search method.4) Online modeling software development:A set of software for modeling and optimization of the fermentation process has been designed by VC++ 6.0 in Windows system. The software can read current and historical data through OPC and ADO to establish the DTW LS_SVM online local model of actual fermentation process, at the same time the dynamic real-time curve of model predictive output and the major measurable variables can be draw when the new data arriving. The development of the software is helpful to realize the optimal control of the fermentation process.