On-line Estimation in Fed-batch Fermentation Process by Using State Space Model and Unscented Kalman Filter
On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO),is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM)method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF)is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.
On-line Estimation Simplified Mechanistic Model Support Vector Machine Unscented Kalman Filter
Qiguo Yao Yuxiang Su Lili Li
School of Naval Architecture & Mechanical-electrical Engineering,Zhejiang Ocean University,Zhoushan,Zhejiang,316022,China
国际会议
大连
英文
102-108
2018-12-21（万方平台首次上网日期，不代表论文的发表时间）