Abstract

Synthesis of Heat Exchanger Networks (HENs) is inherently a Mixed Integer and Nonlinear Programming (MINLP) problem. Solving such problems leads to difficulties in optimization of continuous and binary variables. This study presents a new efficient and robust method in which structural parameters are optimized by G.A. and continuous variables are handled due to a modified objective function for maximum energy recovery. Node representation is used for addressing the exchangers and networks considered as a sequence of genes. Each gene consists of nodes for generating different structures within a network. Results show this method may find new or near optimal solutions with less than 2% increase in HENs annual cost.

Keywords:

Genetic algorithm, heat exchanger network, mixed integer and nonlinear programming problem, optimization,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.