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Simulation of mass exchange networks using modified genetic algorithms
Jutamart Thunyawart,Thongchai Srinophakun,Waranya Henwatthana 한국화학공학회 2011 Korean Journal of Chemical Engineering Vol.28 No.2
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.
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Solving mixed-integer nonlinear programming problems using improved genetic algorithms
Tawan Wasanapradit,Thongchai Srinophakun,Nalinee Mukdasanit,Nachol Chaiyaratana 한국화학공학회 2011 Korean Journal of Chemical Engineering Vol.28 No.1
Abstract−This paper proposes a method for solving mixed-integer nonlinear programming problems to achieve or approach the optimal solution by using modified genetic algorithms. The representation scheme covers both integer and real variables for solving mixed-integer nonlinear programming, nonlinear programming, and nonlinear integer programming. The repairing strategy, a secant method incorporated with a bisection method, plays an important role in converting infeasible chromosomes to feasible chromosomes at the constraint boundary. To prevent premature convergence,the appropriate diversity of the structures in the population must be controlled. A cross-generational probabilistic survival selection method (CPSS) is modified for real number representation corresponding to the representation scheme. The efficiency of the proposed method was validated with several numerical test problems and showed good agreement.
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