Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Abstract

The organic Rankine cycle (ORC) is widely acknowledged as a sustainable power cycle. However, the traditional approach commonly adopted for its optimal design involves sequential consideration of working fluid selection, plant configuration, and component types, before the optimization of state parameters. This way, the design process fails to achieve an optimal design in most cases, since the process relies heavily on empirical judgments. To improve the design process, researchers have been exploring lately the suitability of machine learning techniques. It is however not clear yet if data‐driven designs of ORC plants are practically viable and accurate. To bridge this gap, this article reviews literature studies in the field. Overviews were first presented on the ORC technology and machine learning modeling approaches. Next, studies that applied machine‐learning methods for the design and performance prediction of ORC plants were discussed. Furthermore, studies that focused on ORC machine learning optimizations were discussed. The artificial neural network (ANN) approach was observed as the technique most frequently applied for ORC design and optimization. Additionally, researchers agree in general that machine‐learning methods can achieve accurate results, with significant reductions of computational time and cost. However, there is the risk of using inadequate data size in the machine learning design approach, or insufficient data set training time, all of which can affect accuracy. It is hoped that this effort would spur the practical implementation of machine learning techniques in the future design and optimization of ORC plants, toward the achievement of more sustainable energy technology.