Constrained Multi-Objective Optimization of a Condenser Coil Using Evolutionary Algorithms

Air cooled cross-flow heat exchangers are an integral part of refrigeration and air-conditioning systems. The design and selection of a particular heat exchanger depends on its performance and the associated economic parameters, which in turn depend on individual components that make up the heat exchanger. A multiobjective genetic algorithm is applied to the optimization of an air cooled condensing unit . The primary optimization objectives are the performance of the condenser coil and the cost. This study illustrates how genetic optimization algorithms can be a powerful tool to develop optimal designs for air cooled condensers. At the end of the optimization run, the decision maker is presented with a set of Pareto optimal solutions from which the decision maker can choose appropriate solutions. Optimization setup and results are discussed and conclusions drawn.

[1]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[2]  Urmila M. Diwekar,et al.  Optimal Design of Heat Exchangers: A Genetic Algorithm Framework , 1999 .

[3]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[4]  Shapour Azarm,et al.  Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set , 2001 .

[5]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[6]  P. Reiser,et al.  Genetic algorithms in engineering systems: innovations and applications , 1996 .

[7]  G. N. Vanderplaats,et al.  Design and Optimization of Air-Cooled Heat Exchangers , 1982 .

[8]  S. Azarm,et al.  On improving multiobjective genetic algorithms for design optimization , 1999 .

[9]  E. Van den Bulck.  Optimal design of crossflow heat exchangers , 1991 .

[10]  Peter J. Fleming,et al.  Multiobjective genetic algorithms made easy: selection sharing and mating restriction , 1995 .

[11]  R. Radermacher,et al.  A User-Friendly Simulation And Optimization Tool For Design Of Coils , 2002 .

[12]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[13]  Urmila M. Diwekar,et al.  An automated approach for the optimal design of heat exchangers , 1997 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .