INGENIERIA QUIMICA
Département
Lucas Francisco
dos Santos
Publications dans lesquelles il/elle collabore avec Lucas Francisco dos Santos (12)
2024
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Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression
Computers and Chemical Engineering, Vol. 182
2023
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Multi-objective simulation–optimization via kriging surrogate models applied to natural gas liquefaction process design
Energy, Vol. 262
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Optimization of chemical process simulation: Application to the optimal rigorous design of natural gas liquefaction processes
Optimization of chemical process simulation: Application to the optimal rigorous design of natural gas liquefaction processes
2022
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Energy and economic comparison of five mixed-refrigerant natural gas liquefaction processes
Energy Conversion and Management, Vol. 272
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Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization
Applied Energy, Vol. 310
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MINLP model for work and heat exchange networks synthesis considering unclassified streams
Computer Aided Chemical Engineering (Elsevier B.V.), pp. 793-798
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Multi-objective optimization of natural gas liquefaction process simulation via kriging surrogate model
Computer Aided Chemical Engineering (Elsevier B.V.), pp. 781-786
2021
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Design and optimization of energy-efficient single mixed refrigerant LNG liquefaction process
Brazilian Journal of Chemical Engineering, Vol. 38, Núm. 4, pp. 669-682
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Framework for Embedding Process Simulator in GAMS via Kriging Surrogate Model Applied to C3MR Natural Gas Liquefaction Optimization
Chemical Engineering Transactions, Vol. 88, pp. 475-480
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Kriging-assisted constrained optimization of single-mixed refrigerant natural gas liquefaction process
Chemical Engineering Science, Vol. 241
2020
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Search Space Analysis in Work and Heat Exchange Networks Synthesis using MINLP Models
Computer Aided Chemical Engineering (Elsevier B.V.), pp. 1393-1398
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Synthesis and optimization of work and heat exchange networks using an MINLP model with a reduced number of decision variables
Applied Energy, Vol. 262