期刊
AICHE JOURNAL
卷 61, 期 10, 页码 3249-3269出版社
WILEY
DOI: 10.1002/aic.14838
关键词
computer-aided molecular and process design; statistical associating fluid theory; multiobjective optimization; reduced model; CO2 absorption
资金
- Engineering and Physical Sciences Research Council (EPSRC) of the UK [EP/E016340, EP/J014958]
- German Academic Exchange Service (DAAD)
- EPSRC [EP/J003840]
- Imperial College Scholarship
- EPSRC [EP/J003840/1, EP/J014958/1, EP/E016340/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/J003840/1, EP/E016340/1, EP/J014958/1] Funding Source: researchfish
Molecular-level decisions are increasingly recognized as an integral part of process design. Finding the optimal process performance requires the integrated optimization of process and solvent chemical structure, leading to a challenging mixed-integer nonlinear programming (MINLP) problem. The formulation of such problems when using a group contribution version of the statistical associating fluid theory, SAFT- Mie, to predict the physical properties of the relevant mixtures reliably over process conditions is presented. To solve the challenging MINLP, a novel hierarchical methodology for integrated process and solvent design (hierarchical optimization) is presented. Reduced models of the process units are developed and used to generate a set of initial guesses for the MINLP solution. The methodology is applied to the design of a physical absorption process to separate carbon dioxide from methane, using a broad selection of ethers as the molecular design space. The solvents with best process performance are found to be poly(oxymethylene)dimethylethers. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 3249-3269, 2015
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