Shell and tube heat exchanger design using mixed-integer linear programming

被引:25
|
作者
Goncalves, Caroline de O. [1 ]
Costa, Andre L. H. [1 ]
Bagajewicz, Miguel J. [2 ]
机构
[1] Rio de Janeiro State Univ UERJ, Inst Chem, Rua Sao Francisco Xavier 524, BR-20550900 Rio De Janeiro, RJ, Brazil
[2] Univ Oklahoma, Sch Chem Biol & Mat Engn, Norman, OK 73019 USA
关键词
optimization; design; POINT-OF-VIEW; GENETIC ALGORITHMS; OPTIMIZATION; MODEL; COST;
D O I
10.1002/aic.15556
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The design of heat exchangers, especially shell and tube heat exchangers was originally proposed as a trial and error procedure where guesses of the heat transfer coefficient were made and then verified after the design was finished. This traditional approach is highly dependent of the experience of a skilled engineer and it usually results in oversizing. Later, optimization techniques were proposed for the automatic generation of the best design alternative. Among these methods, there are heuristic and stochastic approaches as well as mathematical programming. In all cases, the models are mixed integer non-linear and non-convex. In the case of mathematical programming solution procedures, all the solution approaches were likely to be trapped in a local optimum solution, unless global optimization is used. In addition, it is very well-known that local solvers need good initial values or sometimes they do not even find a feasible solution. In this article, we propose to use a robust mixed integer global optimization procedure to obtain the optimal design. Our model is linear thanks to the use of standardized and discrete geometric values of the heat exchanger main mechanical components and a reformulation of integer nonlinear expressions without losing any rigor. (c) 2016 American Institute of Chemical Engineers AIChE J, 63: 1907-1922, 2017
引用
收藏
页码:1907 / 1922
页数:16
相关论文
共 50 条
  • [1] Alternative Mixed-Integer Linear Programming Formulations for Shell and Tube Heat Exchanger Optimal Design
    Goncalves, Caroline de O.
    Costa, Andre L. H.
    Bagajewicz, Miguel J.
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2017, 56 (20) : 5970 - 5979
  • [2] Design of complex neuroscience experiments using mixed-integer linear programming
    Slivkoff, Storm
    Gallant, Jack L.
    NEURON, 2021, 109 (09) : 1433 - 1448
  • [3] Irrigation scheduling using mixed-integer linear programming
    Anwar, AA
    Clarke, D
    JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2001, 127 (02) : 63 - 69
  • [4] Design of grounding systems in substations using a mixed-integer linear programming formulation
    Khodr, H. M.
    Salloum, G. A.
    Saraiva, J. T.
    Matos, M. A.
    ELECTRIC POWER SYSTEMS RESEARCH, 2009, 79 (01) : 126 - 133
  • [5] Safe bounds in linear and mixed-integer linear programming
    Arnold Neumaier
    Oleg Shcherbina
    Mathematical Programming, 2004, 99 : 283 - 296
  • [6] Optimizing Dynamic Evacuation Using Mixed-Integer Linear Programming
    Obaid, Hamoud Bin
    Trafalis, Theodore B.
    Abushaega, Mastoor M.
    Altherwi, Abdulhadi
    Hamzi, Ahmed
    MATHEMATICS, 2025, 13 (01)
  • [7] Optimization of sewer networks using the mixed-integer linear programming
    Safavi, Hamidreza
    Geranmehr, Mohammad A.
    URBAN WATER JOURNAL, 2017, 14 (05) : 452 - 459
  • [8] Safe bounds in linear and mixed-integer linear programming
    Neumaier, A
    Shcherbina, O
    MATHEMATICAL PROGRAMMING, 2004, 99 (02) : 283 - 296
  • [9] Optimal design and operation of building services using mixed-integer linear programming techniques
    Ashouri, Araz
    Fux, Samuel S.
    Benz, Michael J.
    Guzzella, Lino
    ENERGY, 2013, 59 : 365 - 376
  • [10] SelfSplit parallelization for mixed-integer linear programming
    Fischetti, Matteo
    Monaci, Michele
    Salvagnin, Domenico
    COMPUTERS & OPERATIONS RESEARCH, 2018, 93 : 101 - 112