Comparing generative process synthesis approaches with superstructure optimization for the conception of supercritical CO2 Brayton cycles

Abstract

In process synthesis, while heuristic-based approaches are most often used for proposing relevant alternatives (that must then be thoroughly analyzed), this strategy may not be the most efficient. When it comes to the search for innovative processes, prior domain knowledge may be scarce or may not effectively exploit the properties contributing to the process novelty. Generative synthesis approaches that can freely explore the search space and that do not rely on any previous knowledge, have been proposed in the literature. Yet, a lack of benchmarks on complex problems strongly hinders their use. In this work, we address this gap by comparing two generative approaches, based on Evolutionary Programming and Machine Learning, to a superstructure optimization (which serves as a baseline). They are applied to the synthesis of supercritical CO₂ Brayton cycles. Despite starting with no field of expertise, the generative approaches not only manage to identify multiple known heuristics of the domain, but also a counter-intuitive and new way of increasing the efficiency of sCO₂ cycles — by expanding the fluid at lower temperatures. The approaches’ use-cases are discussed, based on the amount of computational resources necessary, implementation difficulties and quality of the results.