Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks

标题
Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks
作者
关键词
Surrogate-based global optimization, McCormick relaxations, Reduced-space formulation, Organic Rankine cycle, Thermodynamic properties, MAiNGO
出版物
COMPUTERS & CHEMICAL ENGINEERING
Volume 121, Issue -, Pages 67-74
出版商
Elsevier BV
发表日期
2018-10-16
DOI
10.1016/j.compchemeng.2018.10.007

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