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Fed-batch processes are inherently more difficult to characterize than continuous processes due to the variations under different operation stages, drifting and small-sample condition. The classical kernel-based regression (KR) methods, e.g., least squares support vector regression (LSSVR), aim to achieve a universal generalization performance, which may fail in some local regions when applied to...
On-line optimization and control of fed-batch fermentation is difficult due to its non linearity and dynamic operation points with uncertainty. A novel cascade optimal control algorithm was proposed to treat these difficulties with the operating points, which maximize the objective function, was defined by Necessary Conditions of Optimization (NCO). Consequently, a nonlinear Generic Model Control...
Multiway principal component analysis (MPCA) has been widely used to monitor batch processes. However, due to the nature and complicated changes of batch processes, the data in fact contains inherent non-Gaussian information. Besides, when used for on-line monitoring, MPCA needs future value estimation. These shortcomings may lead to poor monitoring performance. In this paper, a new statistical batch...
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