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The QP problem is iteratively decomposed into a series of one variable optimisation problems which can be solved analytically.
These variables were also identified by the random forest variable optimisation as the most valuable in coffee foliar N prediction.
Response surface methodology (RSM) was used to show the relationships between the production variables and predict the board property by variable optimisation.
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This eliminates the multi-variable optimisation which results when stream-dependent minimum approach temperatures are used.
The design parameters of each configuration were determined by performing a multi-variable optimisation.
Some of these programs are equipped with their own optimisation tools; however, as is often the case, most of them are not able to carry out a multi-variable optimisation with a large number of decision variables.
Because cross-sectional areas are usually defined by discrete values in practice due to manufacturing limitations, the optimisation algorithm must assess a mixed-variable optimisation problem that includes both discrete and continuous variables at the same time.
It also opens for further simplification into one-variable optimisation problems, which in many cases can be solved explicitly, as shown in the results section.
In case of the instrumental variables, the optimisation was achieved following the experimental design approach, while the chemical variables were adjusted following the variation of the signals of the analytes.
It intents to analyse some general aspects of process synthesis including trade-offs, important variables, possible optimisation energy integration, system's layout and important aspects of systems' design and operation.
Of particular interest within these comparisons is the interplay between catalyst concentration and temperature as variables for optimisation.
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