Abstract Organic Rankine cycle (ORC) is a promising technology to recovery and utilization of low grade thermal energy. In recent years, there are few researches on ORC performance prediction based on Machine Learning, mainly due to a lack of reasonable methodology and case demonstration. This paper presented a comprehensive method to achieve a reasonable application of Machine Learning into ORC research for prediction and optimization of ORC’s parameter and performance. Firstly, a cycle database was established by thermodynamic modeling, including four ORC configurations and seven working fluids. Then, for Machine Learning, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) prediction models for ORC were built by predicting error analysis with part of the database which can determine the best parameters of BPNN and SVR. Finally, taking RORC as example, cycle parameter analysis and multi-objective optimization of ORC were conducted based on the thermodynamic model and prediction model to maximize the thermal and exergy efficiency simultaneously. By the prediction and optimization results, it can be deserved that the accurate and fast prediction of the thermal efficiency and exergy efficiency of ORC with multi-parameter, multi-configuration and multi-working fluid was realized, and the optimization results based on the prediction model as the proxy model were also greatly close to the traditional optimization results based on the thermodynamic model. It should be noted that the comprehensive performance of prediction and optimization will be better with more data input. In conclusion, considering accuracy, calculation time, economic cost and safety, the ORC prediction and optimization method proposed in this paper is a promising technology combining Machine Learning and energy utilization, which could provide a new perspective for research in this field.

中文翻译：

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机器学习在有机朗肯循环中的应用，以预测和优化热效率和火用效率

摘要 有机朗肯循环（ORC）是一种很有前景的低品位热能回收利用技术。近年来，基于机器学习的ORC性能预测研究较少，主要是缺乏合理的方法论和案例论证。本文提出了一种全面的方法，以实现机器学习在 ORC 研究中的合理应用，以预测和优化 ORC 的参数和性能。首先，通过热力学建模建立循环数据库，包括四种ORC配置和七种工作流体。然后，对于机器学习，通过对部分数据库进行预测误差分析，建立了ORC的反向传播神经网络（BPNN）和支持向量回归（SVR）预测模型，确定BPNN和SVR的最佳参数。最后，以RORC为例，基于热力学模型和预测模型对ORC进行循环参数分析和多目标优化，同时最大化热效率和火用效率。通过预测和优化结果，实现了对多参数、多配置、多工质的ORC热效率和火用效率的准确快速预测，基于预测的优化结果当之无愧。作为代理模型的模型也非常接近传统的基于热力学模型的优化结果。需要注意的是，输入的数据越多，预测和优化的综合性能会越好。综上所述，综合考虑精度、计算时间、经济成本和安全性，