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His main interests are in Network Science and in particular mathematical models for processes on dynamic networks.
It argues that the use of discrete time models for processes that are in fact evolving in continuous time produces problems that make their application in practice highly questionable.
It could also be useful in choosing appropriate models for processes in organisms, such as plants, that due to their long duplication times cannot be easily studied.
The method is derived in a linear setting, and is applied to two examples of previously published linear models for processes involved in photosynthesis.
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Mathematical models for process simulation were developed.
Six Sigma models for process improvement include DMAIC and DMADV.
D-optimal method is conducted to develop mathematical models for process responses.
This chapter discusses the symbolic discretization of population models for process simulation.
The models for process engineering and biological processes are formulated separately and implemented in a suitable program structure.
In order to dynamically simulate the crushing circuit, models for process equipment need to be further developed.
Detailed capital cost models for process equipment are formulated and implemented in GAMS to maximize net present value (NPV).
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