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The flash point shows positive deviation from ideal solution behavior for both the binary mixture.
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Both systems present slight negative deviations from ideal solution behavior.
Additive mixing rules provide little insight, as superior compositions often exhibit strong deviations from ideal solution behavior.
It was found that multi-objective optimization could yield results with a relatively low deviation from the ideal solution in comparison to the conventional single objective approach.
No matter what kind of configuration we set, our weighted set covering algorithm outperforms the random algorithm (51)–(62) %; the deviation from the ideal solutions is no more than (6.8) %.
Our experiments show that using our RBBD framework and WSCS, the data transfer volume is reduced by an average of 36.37 % and our weighted set covering algorithm outperforms the random algorithm by 51 62 %, with the deviation from the ideal solutions of not more than 6.8%%.
The comparisons of triple-objective and dual-objective optimization with single-objective optimization indicate that multi-objective optimization can yield the more suitable results due to the lower deviation index from the ideal solution.
In addition, the comparisons of single-objective and double-objective optimization approaches clearly show that multi-objective optimization methods yield more appropriate results respect to the single ones, mainly because of the lower deviation index value from the ideal solution.
The first two of them show negative deviations and the last one shows positive deviation from ideal mixtures.
However, in the case of EVAc, the deviation from ideal is significantly negative.
The PDE model represents fairly this deviation from ideal flow (less dead zone).
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