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Operating cost of a batch process can be reduced using multiple resources.
The state space model of a 2-D batch process can be identified with canonical variate analysis (CVA) method based on the auto-determined support region (ROS).
By analyzing the contextual information of each time-slice, the batch process can be roughly divided using the time-varying characteristics.
Due to its countercurrent purification principle, the performance increase with respect to the batch process can be up to 10× in productivity and even more at higher yields.
Take the injection molding process as an example, a multi-phase batch process can be regarded as a switched system with different-dimensional subsystems in each batch.
Moreover, on the basis of the heuristic method adapted from concentration interval analysis method for the continuous process network design, the network design for the discontinuous or batch process can be obtained through the designs for every time interval.
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This book demonstrates how batch processes can be analyzed, synthesized, and designed optimally using proven mathematical formulations.
Controlling multi-phase batch processes can be decomposed into two subtasks: detecting the dynamics-switching-time; designing the control law for each phase with considering switching effect.
The recognition that optimal control trajectories for batch processes can be highly sensitive to model uncertainties has motivated the development of methods for explicitly addressing robustness during batch processes.
It could be shown, that high and well-defined cooling rates (up to 104 times higher than standard batch processes) can be achieved by using a micro heat exchanger device for the continuous melt crystallization of lipid nanoparticles distributed in an emulsion, resulting in well-defined product qualities.
Models of the batch distillation process can be used to provide estimates of the required compositions.
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