Towards sustainable separation of the ternary azeotropic mixture based on the intensified reactive-extractive distillation configurations and multi-objective particle swarm optimization

被引:105
|
作者
Yang, Ao [1 ]
Su, Yang [2 ]
Sun, Shirui [3 ]
Shen, Weifeng [3 ]
Bai, Mengna [4 ]
Ren, Jingzheng [5 ]
机构
[1] Chongqing Univ Sci Technol, Coll Safety Engn, Chongqing 401331, Peoples R China
[2] Chongqing Univ Sci Technol, Sch Intelligent Technol & Engn, Chongqing 401331, Peoples R China
[3] Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China
[4] Suzhou Univ Sci & Technol, Sch Chem & Life Sci, Suzhou 215009, Peoples R China
[5] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
关键词
Energy efficiency; Process intensification; Ternary azeotropic mixture; Multi-objective optimization; Sustainability; CATALYTIC DISTILLATION; ACETATE; DESIGN; ENERGY; COLUMN; PURIFICATION; DEHYDRATION; ETHANOL;
D O I
10.1016/j.jclepro.2021.130116
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The separation of ternary azeotropic systems has received significant interest as it enables the recovery of value-added organic solvents, subsequently contribute towards environmental protection. In this work, we propose a novel approach that involves the conceptual design, multi-objective optimization, and process evaluations for developing two different processes, i.e., double-column reactive-extractive distillation (DCRED) and reactive-extractive dividing wall column (REDWC), for the separation of ethanol/tert-butanol/water ternary azeotropic mixture. The conceptual design of the proposed processes was conducted using kinetic and thermodynamic analysis while optimal operating conditions of the established processes were obtained via multi-objective particle swarm optimization algorithm. Then, both developed processes were evaluated based on the total annual cost (TAC), CO2 emissions, and thermodynamic efficiency. From the steady-state simulation, DCRED and REDWC provides a TAC of 1.056 x 10(6) US$ and 1.117 x 10(6) US$, respectively. Likewise, it provides CO2 emissions of 731.27 kg/h and 733.42 kg/h, respectively. The energy efficiency of the DCRED and REDWC were found to be 1.285% and 1.055%, respectively. Relative to the conventional extractive distillation process, the TAC and CO2 emission for the proposed DCRED reduced significantly by 55.4% and 61.8%, respectively. Similar reduction was also observed for the REDWC which provides 52.8% and 61.7% lower TAC and CO2 with respect to the conventional process. In addition, the thermodynamic efficiency of the developed DCRED and REDWC processes are improved by 40.4% and 15.3% in comparison to the conventional extractive distillation scheme.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] A Comprehensive Study of Particle Swarm Based Multi-objective Optimization
    Mohankrishna, Samantula
    Maheshwari, Divya
    Satyanarayana, P.
    Satapathy, Suresh Chandra
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS 2012 (INDIA 2012), 2012, 132 : 689 - +
  • [42] A Multi-Objective Particle Swarm Optimization Based on Grid Distance
    Leng, Rui
    Ouyang, Aijia
    Liu, Yanmin
    Yuan, Lian
    Wu, Zongyue
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2020, 34 (03)
  • [43] Multi-Objective Particle Swarm Optimization Based on Grid Ranking
    Li L.
    Wang W.
    Xu X.
    Li W.
    Wang, Wanliang (zjutwwl@zjut.edu.cn), 1600, Science Press (54): : 1012 - 1023
  • [44] Surrogate-based Multi-Objective Particle Swarm Optimization
    Santana-Quintero, Luis V.
    Coello Coello, Carlos A.
    Hernandez-Diaz, Alfredo G.
    Osorio Velazquez, Jesus Moises
    2008 IEEE SWARM INTELLIGENCE SYMPOSIUM, 2008, : 166 - +
  • [45] Multi-Objective Particle Swarm Optimization Based on Gaussian Sampling
    Li, Guosen
    Yan, Li
    Qu, Boyang
    IEEE ACCESS, 2020, 8 : 209717 - 209737
  • [46] Application of improved particle swarm optimization algorithm to multi-objective reactive power optimization
    Li, Xinbin
    Zhu, Qingjun
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2010, 25 (07): : 137 - 143
  • [47] Multi-Objective Particle Swarm Optimization Based on Fuzzy Optimality
    Shen, Yongpeng
    Ge, Gaorui
    IEEE ACCESS, 2019, 7 : 101513 - 101526
  • [48] Multi-objective Particle Swarm Optimization Based on Adaptive Mutation
    Saha, Debasree
    Banerjee, Suman
    Jana, Nanda Dulal
    2015 THIRD INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT), 2015,
  • [49] Multi-objective robust design of vehicle structure based on multi-objective particle swarm optimization
    Liu, Haichao
    Jin, Xiangjie
    Zhang, Fagui
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (06) : 9063 - 9071
  • [50] A multi-objective particle swarm optimizer based on reference point for multimodal multi-objective optimization
    Li, Guosen
    Zhou, Ting
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 107