Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria

Abstract In this work, Excel-based multi-objective optimization (EMOO) program, based on the elitist non-dominated sorting genetic algorithm, is improved by implementing two termination criteria: a Chi-squared test-based termination criterion and a novel termination criterion based on steady-state detection. The termination criteria were shown to perform reliably during the MOO of four constrained test functions. Then, the improved EMOO program was applied to the design of several shell-and-tube heat exchangers (STHE) from the literature; these STHE are simulated and then optimized using the EMOO program for two objectives: capital cost and operating cost. It provided better optimal results compared to those in the literature. The termination criteria performed reliably for MOO of STHE design, showing their applicability on industrial MOO problems.

[1]  Louis Gosselin,et al.  Review of utilization of genetic algorithms in heat transfer problems , 2009 .

[2]  Saeid Nahavandi,et al.  Effectiveness of evolutionary algorithms for optimization of heat exchangers , 2015 .

[3]  Marc A. Rosen,et al.  Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms , 2015 .

[4]  Gade Pandu Rangaiah,et al.  An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes , 2013, Comput. Chem. Eng..

[5]  Gade Pandu Rangaiah,et al.  Performance Comparison of Jumping Gene Adaptations of the Elitist Non‐dominated Sorting Genetic Algorithm , 2013 .

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Hassan Hajabdollahi,et al.  Multi-objective optimization of shell and tube heat exchangers , 2010 .

[8]  Salim Fettaka,et al.  Design of shell-and-tube heat exchangers using multiobjective optimization , 2013 .

[9]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[10]  Hassan Hajabdollahi,et al.  CFD modeling and multi-objective optimization of compact heat exchanger using CAN method , 2011 .

[11]  Kalyanmoy Deb,et al.  Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems , 1995, Complex Syst..

[12]  Gongnan Xie,et al.  Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm , 2014 .

[13]  Mohsen Amini,et al.  Two objective optimization in shell-and-tube heat exchangers using genetic algorithm , 2014 .

[14]  Sandip Kumar Lahiri,et al.  Particle swarm optimization technique for the optimal design of shell and tube heat exchangers , 2012 .

[15]  Wei Liu,et al.  Optimization of shell-and-tube heat exchangers using a general design approach motivated by constructal theory , 2014 .

[16]  Heike Trautmann,et al.  Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms , 2009, Evolutionary Computation.

[17]  R. R. Rhinehart,et al.  An efficient method for on-line identification of steady state , 1995 .

[18]  Ray Sinnott,et al.  Chemical Engineering Design , 2007 .

[19]  R. Russell Rhinehart Convergence criterion in optimization of stochastic processes , 2014, Comput. Chem. Eng..

[20]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[21]  Gade Pandu Rangaiah,et al.  Multi-Objective Optimization in Chemical Engineering: Developments and Applications , 2013 .

[22]  Mehdi Bahiraei,et al.  Effects of geometrical parameters on hydrothermal characteristics of shell-and-tube heat exchanger with helical baffles: Numerical investigation, modeling and optimization , 2015 .

[23]  Kalyanmoy Deb,et al.  Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[24]  Francisco Herrera,et al.  Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis , 1998, Artificial Intelligence Review.

[25]  Amin Hadidi,et al.  Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm , 2013 .

[26]  Ehsanolah Assareh,et al.  Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO) , 2014 .

[27]  Oguz Emrah Turgut,et al.  Design and economic investigation of shell and tube heat exchangers using Improved Intelligent Tuned Harmony Search algorithm , 2014 .

[28]  Gade Pandu Rangaiah,et al.  Multi-objective optimization using MS Excel with an application to design of a falling-film evaporator system , 2012 .

[29]  Francisco Herrera,et al.  A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study , 2003, Int. J. Intell. Syst..

[30]  Kusum Deep,et al.  A new mutation operator for real coded genetic algorithms , 2007, Appl. Math. Comput..