### Multiobjective optimal design of heat exchanger networks using new adaptations of the elitist nondominated sorting genetic algorithm, NSGA-II

Abstract

A new approach for generating optimal heat exchanger networks (HENs) is described
that does not use any heuristics. This approach involves generating the number of
intermediate temperatures in each of the hot and cold streams and their values,
randomly, using the binary coded NSGA-II-sJG. The substreams so generated are
then matched randomly. This procedure results in a variable number of decision
variables in each solution (chromosome). Dummy decision variables are introduced
so as to make the length of each chromosome the same. A new crossover strategy,
crossA, as well as a few other adaptations, are described that enable faster convergence
to the optimal solution(s). Three single-objective problems involving the minimization of
the annualized cost are solved and the results compared with those reported in the
literature. Thereafter, a few problems with two- and three-objective functions are
solved. In these, the objective functions are selected from among the annualized cost,
the amount of (hot + cold) utilities required (these are important due the environmental
issues associated with them), the energy recovery, and the total number of units. To the
best of our knowledge, such multiobjective optimization of HENs has not been reported
in the open literature yet. A decision maker can choose any of the solutions from among
the set of several nondominated (equally good) Pareto-optimal solutions generated.
These are more meaningful than those obtained using single objective functions. Though
the algorithm developed is specific to HENs, it can easily be applied to other similar
optimization problems.