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The implementation of model predictive control (MPC) requires to solve an optimization problem online.
Such modelling and simulation often requires rapid implementation of model changes by the process engineer followed by simulation runs.
The digital implementation of model predictive control (MPC) is fundamentally governed by two design parameters; sampling time and prediction horizon.
In addition, we provide a batch sequential design framework for iterative implementation of model migration, which thus avoids an exhaustive treatment of a predetermined number of design points.
For this purpose, a particular implementation of Model Predictive Control (MPC) based on a corresponding Mean Value Engine Model (MVEM) is reported here.
Thus, in a theoretical scheme, the higher values of λ can play a role in increasing the magnitude of axial velocity that can be used in further implementation of model in the construction of an implanted vascular system.
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The methodology was implemented within a multi-tiered software environment for real-time implementation of model-based control strategies.
The design and implementation of model-based controllers can be performed easier in the discrete domain.
The strategy developed within a gPROMS-API-DCS environment allowed real-time implementation of model-based control of the process.
Successful implementation of model-based design processes is a key factor for remaining competitive in the automotive industry.
This article focuses on the design and implementation of model-based control strategies for real time control of crystal size distribution (CSD) in semi batch crystallization processes.
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