A COSMO-based approach to computer-aided mixture design

被引:53
|
作者
Austin, Nick D. [1 ]
Sahinidis, Nikolaos V. [1 ]
Trahan, Daniel W. [2 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Dow Chem Co USA, Freeport, TX USA
关键词
Computer-aided mixture design; Computer-aided molecular design; Derivative-free optimization; Solvents; Reaction rates; DERIVATIVE-FREE OPTIMIZATION; MOLECULAR DESIGN; INTEGRATED SOLVENT; PREDICTION; SAFT; REFINEMENT; ABSORPTION; MODEL; SAC; RS;
D O I
10.1016/j.ces.2016.05.025
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
In this work, we adapt the COSMO-RS and-SAC methods to solve computer-aided mixture design (CAMxD) problems. Popular methods in CAMxD require the use of binary interaction parameters to calculate mixture thermodynamics, and this necessity places inherent limitations on the possible chemical search space. Our COSMO-based approach is free of binary interaction parameters and requires only molecular volumes and molecule-specific charge density distributions called sigma profiles for the estimation of solution properties. Additionally, this methodology enables the integration of highly accurate molecular information from ab initio quantum chemistry calculations into mixture design problems. To address the search problem, we project molecular identities and mole fractions on the space of each mixture component's sigma moments, which are analogous to statistical moments for sigma profiles. This approach exploits a natural problem decomposition and capitalizes on fast methods for pure compound design and mixture fraction design. We apply the methodology to two case studies: the design of a liquid-liquid extraction solvent and a reaction rates optimization solvent. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:93 / 105
页数:13
相关论文
共 50 条
  • [1] COSMO-based computer-aided molecular/mixture design: A focus on reaction solvents
    Austin, Nick D.
    Sahinidis, Nikolaos V.
    Konstantinov, Ivan A.
    Trahan, Daniel W.
    AICHE JOURNAL, 2018, 64 (01) : 104 - 122
  • [2] COSMO-CAMD: A framework for optimization-based computer-aided molecular design using COSMO-RS
    Scheffczyk, Jan
    Fleitmann, Lorenz
    Schwarz, Annett
    Lampe, Matthias
    Bardow, Andre
    Leonhard, Kai
    CHEMICAL ENGINEERING SCIENCE, 2017, 159 : 84 - 92
  • [3] Computer-aided reaction solvent design based on transition state theory and COSMO-SAC
    Liu, Qilei
    Zhang, Lei
    Liu, Linlin
    Du, Jian
    Meng, Qingwei
    Gani, Rafiqul
    CHEMICAL ENGINEERING SCIENCE, 2019, 202 : 300 - 317
  • [4] Computer-aided ionic liquid design for separation processes based on group contribution method and COSMO-SAC model
    Peng, Daili
    Zhang, Jianan
    Cheng, Hongye
    Chen, Lifang
    Qi, Zhiwen
    CHEMICAL ENGINEERING SCIENCE, 2017, 159 : 58 - 68
  • [5] Rx-COSMO-CAMPD: Enhancing Reactions by Integrated Computer-Aided Design of Solvents and Processes based on Quantum Chemistry
    Gertig, Christoph
    Fleitmann, Lorenz
    Schilling, Johannes
    Leonhard, Kai
    Bardow, Andre
    CHEMIE INGENIEUR TECHNIK, 2020, 92 (10) : 1489 - 1500
  • [6] COMPUTER-AIDED MIXTURE DESIGN WITH SPECIFIED PROPERTY CONSTRAINTS
    KLEIN, JA
    WU, DT
    GANI, R
    COMPUTERS & CHEMICAL ENGINEERING, 1992, 16 : S229 - S236
  • [7] COSMO-descriptor based computer-aided ionic liquid design for separation processes Part II: Task-specific design for extraction processes
    Zhang, Jianan
    Qin, Lei
    Peng, Daili
    Zhou, Teng
    Cheng, Hongye
    Chen, Lifang
    Qi, Zhiwen
    CHEMICAL ENGINEERING SCIENCE, 2017, 162 : 364 - 374
  • [8] A machine learning based computer-aided molecular design/screening methodology for fragrance molecules
    Zhang, Lei
    Mao, Haitao
    Liu, Linlin
    Du, Jian
    Gani, Rafiqul
    COMPUTERS & CHEMICAL ENGINEERING, 2018, 115 : 295 - 308
  • [9] A robust and efficient solution for COSMO-based activity coefficient models
    Yan, Wei
    CHEMICAL ENGINEERING SCIENCE, 2024, 300
  • [10] COSMO-descriptor based computer-aided ionic liquid design for separation processes. Part I: Modified group contribution methodology for predicting surface charge density profile of ionic liquids
    Zhang, Jianan
    Peng, Daili
    Song, Zhen
    Zhou, Teng
    Cheng, Hongye
    Chen, Lifang
    Qi, Zhiwen
    CHEMICAL ENGINEERING SCIENCE, 2017, 162 : 355 - 363