The synthesis of cost optimal heat exchanger networks with unconstrained topology

Abstract

The optimization of heat exchanger network (HEN) synthesis still remains an open problem because of the complexity of the space comprising all the possible solutions, and most of the proposed methods introduce simplifying assumptions that mainly affect the topological features of the candidate solutions considered and thus artificially limit the boundaries of the search space.This work is devoted to the pursuit of cost-optimal HENs with unconstrained topology, exploiting the advantages deriving from two graph representations of a HEN. One representation is used by an evolutionary algorithm to manage HEN topology and the other is used by a NLP algorithm to manage heat load distribution among the exchangers. The proposed two-level hybrid optimization method is applied to four test cases taken from the literature about HEN synthesis, among which the well-known Aromatics Plant problem.Keywords: synthesis optimization, heat exchanger networks, graph theory. ACCEPTED MANUSCRIPTThe synthesis of cost optimal heat exchanger networks with unconstrained topology -Andrea Toffolo

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

“…8, both hot utilities are placed before the end of the cold stream C3 and better optimal results are obtained. Furthermore, it is worth noting that the exponent and fixed charge coefficient for calculating area cost is 1 and 0 respectively, which means that the small units will be not penalized [55]. Therefore, the optimal results obtained for this case will be quite independent from the number of heat exchanger units.…”

Review of metaheuristics applied to heat exchanger network design

“…Both Toffolo () and Fraga () used a two‐level structure with an evolutionary algorithm (EA) in the outer level and NLP in the inner level. Each of them uses a representation that does not limit the search space prior to the optimization.…”

“…Each of them uses a representation that does not limit the search space prior to the optimization. Toffolo () uses a graph‐based representation, and Fraga () proposes a grammar that is capable of representing an arbitrary network and can be used to evolve a starting solution toward better solutions.…”

“…This method consists of applying a metaheuristic algorithm at the outer level and mathematical programming model at the inner level. The mathematical programming model is used to optimize the heat duty (Fraga, ; Khorasany and Fesanghary, ; Rezaei and Shafiei, ; Toffolo, ; Gorji‐Bandpy et al., ) or temperatures (Athier et al., ) of the structure given by the outer level. This method allows using an NLP or LP model instead of an MINLP model, which is required when optimizing the complete problem (Athier et al., ).…”

Bi-level heat exchanger network synthesis with evolution method for structure optimization and memetic particle swarm optimization for parameter optimization

“…Errico et al (2007) adopted the Lindo-Lingo package and the CPLEX packages to handle the maximum energy recovery problem. Khorasany and Fesanghary (2009) and Toffolo (2009) both employed sequential quadratic programming in the lower level optimization to find optimal exchanger duties and stream-split fractions for generated network structures. The deterministic methods are relatively easy to apply, but convergence to the global optimum is difficult to guarantee.…”