Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization
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DOI: 10.1016/j.apenergy.2022.118537
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Cited by:
- Tak, Kyungjae & Park, Jaedeuk & Moon, Il & Lee, Ung, 2023. "Comparison of mixed refrigerant cycles for natural gas liquefaction: From single mixed refrigerant to mixed fluid cascade processes," Energy, Elsevier, vol. 272(C).
- Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).
- Zhou, Jianzhao & Chu, Yin Ting & Ren, Jingzheng & Shen, Weifeng & He, Chang, 2023. "Integrating machine learning and mathematical programming for efficient optimization of operating conditions in organic Rankine cycle (ORC) based combined systems," Energy, Elsevier, vol. 281(C).
- Santos, Lucas F. & Costa, Caliane B.B. & Caballero, José A. & Ravagnani, Mauro A.S.S., 2023. "Multi-objective simulation–optimization via kriging surrogate models applied to natural gas liquefaction process design," Energy, Elsevier, vol. 262(PB).
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Keywords
Simulation optimization; Kriging; Process simulation; Surrogate-based optimization; Natural gas liquefaction; Mathematical programming;All these keywords.
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