Simulation of mass exchange networks using modified genetic algorithms

Jutamart Thunyawart; Thongchai Srinophakun; Waranya Henwatthana

Korean Journal of Chemical Engineering, Vol.28, No.2, 332-341, February, 2011

DOI10.1007/s11814-010-0360-y Export Citation

Simulation of mass exchange networks using modified genetic algorithms

Jutamart Thunyawart¹, Thongchai Srinophakun^{1, 2, †}, and Waranya Henwatthana³

¹Department of Chemical Engineering, Kasetsart University, Bangkok 10900, Thailand
²National Center of Excellence for Petroleum, Petrochemicals, and Advanced Materials, Pathumwan, Bangkok 10330, Thailand
³Department of Chemical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand

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The optimum water usage network leads to both a minimum of freshwater consumption and a minimum of generated wastewater. This work is to develop a mass-exchange networks (MENs) module for a minimum freshwater usage target. This module works as an interface to retrieve supplemental data of chemical processes from a process simulator and to communicate this to the genetic algorithm optimizer. A reuse system and a regeneration/recyclingsystem with a single contaminant are considered as approaches for freshwater minimization. In the formulated model, as mixed integer nonlinear programming (MINLP), all of the variables are divided into independent and dependent variables. The values of independent variables come from randomization,whereas the values of dependent variables come from simultaneous solutions of a set of equality constraints after assigning the values of independent variables. This method is applied to the steps of initialization, crossover and mutation. The MENs module is validated with a tricresylphosphate process consisting of five unit operations. Water is used to remove a fixed content of cresol. From the result, the module gives a reliable solution for freshwater minimization, which can satisfy mass balance and constraints. The results show that reuse and regeneration/recycling strategies can reduce freshwater consumption, including wastewater generated. Reuse cannot decrease the mass load of the contaminant, while regeneration /recycling can. In addition, regeneration requires less freshwater than the reuse process.

Keywords:Wastewater Minimization;Genetic Algorithm;Mixed Integer Nonlinear Programming

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[1] Wang YP, Smith R, Chem. Eng. Sci., 49(7), 981 (1994)

[2] El-Halwagi MM, Manousiouthakis V, Amer. Ins. Chem. Eng. J., 35, 1233 (1989)

[3] Papalexandri KP, Pistikopoulos EN, Floudas A, Chem. Eng. Res. Des., 72(3), 279 (1994)

[4] Floudas CA, Nonlinear and mixed-integer optimization: fundamentals and applications, Oxford University Press, New York, 460 (1995)

[5] Yee TF, Grossmann IE, Comput. Chem. Eng., Simultaneous optimization models for heat integration - II. Heat exchanger network synthesis, 14, 1165 (1990)

[6] Garrard A, Fraga ES, Comput. Chem. Eng., 22, 12 (1998)

[7] Suriyapraphadilok U, Network prototype of water-wastewater management, Master’s thesis, Chemical Engineering Practice School, King Mongkut’s University of Technology Thonburi, 113 (1998)

[8] Krirkkraikijporn T, Multi-component optimization of simultaneous heat and mass exchanger networks using GAMs, Master’s thesis, Chemical Engineering Program, Kasetsart University, 221 (2002)

[9] GAMs Development Corporation, http://www.gams.com/solvers/index.html.

[10] Prakotpol D, Srinophakun T, Chem. Eng. Process., 43(2), 203 (2004)

[11] Lingo Systems, http://www.lingo.com.

[12] Wasanapradit T, Solving nonlinear mixed integer programming using genetic algorithms, Master’s thesis, Chemical Engineering Practice School, King Mongkut’s University of Technology Thonburi, 90 (2000)