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RTO can be described as a sequence of three separate functions: (1) Data reconciliation and parameter estimation to establish the current operation point, (2) optimization to find the optimal operation, and (3) implementation of the optimal result as controller set points.
Reliable implementation of the optimal controller is also discussed.
On-line implementation of the optimal reactor temperature profiles in batch reactors is considered here.
The implementation of the optimal policies clearly highlights the role of labor market institutions for short-run stabilization.
Finally, we develop a related access protocol, which supports a fully distributed and scalable implementation of the optimal controller.
We also discuss the conditions under which a simple implementation of the optimal prediction method can be expected to perform well.
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A possible neural implementation of the optimal-observer model is discussed, and we provide behavioral data that support the model.
Further improvements in broadband excitation by optimized pulses (BEBOP) may be possible by applying more sophisticated implementations of the optimal control formalism.
Compared to the exact distributed implementations of the optimal global estimate in Section 3.1, the diffusion solution in Section 4, although suboptimal, is designed to have a much lower inter-node communication cost and, therefore, is better suited for real-time applications.
While the implementation of this optimal control poses no challenge on in silico metabolic networks, on real metabolic networks complex bioengineering skills are required.
We also provide the different implementation schemes of the optimal patterns both for the soft and hard source cases.
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