The fundamental challenge in the synthesis/design optimization of energy conversion systems is the definition of the system configuration and design parameters. The traditional way to operate in system engineering practice is to follow the previous experience, starting from design solutions that already exist. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem, and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top-down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are really feasible.

To solve the general problem of the synthesis/design of complex energy systems a new bottom-up methodology is proposed, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle (compression, heat transfer with the hot source, expansion, heat transfer with the cold source). So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure.

In this paper a deep analysis of the major features of the methodology is presented to show, through different examples of applications, how an artificial intelligence is able to generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.

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