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The implementation of modeling techniques to simulate the molecular composition of petroleum feedstocks from routine analyses represents an alternative route towards molecule-based kinetic modeling.
In addition, the proposed paper also provides a demonstration for the implementation of modeling involving both fluid dynamics and electro-chemical analysis in the context of fuel cells.
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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 implementation of model predictive control (MPC) requires to solve an optimization problem online.
The strategy developed within a gPROMS-API-DCS environment allowed real-time implementation of model-based control of the process.
Such modelling and simulation often requires rapid implementation of model changes by the process engineer followed by simulation runs.
Successful implementation of model-based design processes is a key factor for remaining competitive in the automotive industry.
The digital implementation of model predictive control (MPC) is fundamentally governed by two design parameters; sampling time and prediction horizon.
We cannot expect the exact match of the curves, because of the uncertainty in parameterization and possible particularities in implementation of models.
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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