Overview on artificial intelligence in design of Organic Rankine Cycle

Converting thermal energy into mechanical work by means of Organic Rankine Cycle is a validated technology to exploit low-grade waste heat. The typical design process of Organic Rankine Cycle system, which commonly in-volves working fluid selection, cycle configuration selection, operating parameters optimization, and component selection and sizing, is time-consuming and highly dependent on engineer's experience. Thus, it is difficult to achieve the optimal design in most cases. In recent decades, artificial intelligence has been gradually introduced into the design of energy system to overcome above shortcomings. In order to clarify the research field of arti-ficial intelligence technique in Organic Rankine Cycle design and guide artificial intelligence technique to assist Organic Rankine Cycle design better, this study presents a preliminary literature summary on recent progresses of artificial intelligence technique in organic Rankine cycle systems design. First, this study analyzes four main procedures which constitute a typical design process of Organic Rankine Cycle systems and finds that design problems encountered during design process can be divided into three categories: decision making, parameter optimization and parameter prediction. In the second section, a detailed literature review on each design proce-dures using artificial intelligence algorithms is presented. At last, the state of art in this field and the prospects for the future work are provided.