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In the optimization step, the loading volume and the elution gradient are optimized with regard to the most important costs: the fixed costs and the feed cost.
We solve (2.7) or (2.8) in the optimization step by the simplex method (see [19]).
In the optimization step, a two-level full factorial experimental design was used.
In the optimization step pinch analysis and mixed-integer nonlinear programming method are applied.
In the optimization step, gradient based approaches as one of the most common methods in control field are employed.
In the optimization step, we model the target area based on the pre-measured location and allocate multiple particles into the area to mine confident candidates.
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Facing the challenge of dealing with the numerous nonlinear constraints and integer variables in the optimization steps, genetic algorithm is applied as a means in the solution procedure to aid in finding the optimal decision.
With respect to the risks in transient operations, the Optimization step improves the conveyor's dynamic behaviors and optimizes the speed adjustment time.
In Methods we discuss the optimization step from singular value decomposition and we provide the details of the selected cost function.
It allows to change the topology of the mesh during the segmentation process and to create discontinuities in the mesh structure while the optimization step progresses.
A validation test is added after the optimization step in order to check if the Geometrical Optics laws are respected, specifically the Snell-Descartes ones.
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