An integrated online dynamic modeling scheme for organic Rankine cycle (ORC): Adaptive self-organizing mechanism and convergence evaluation

•Proposed integrated dynamic modeling methodology for organic Rankine cycle (ORC).•A mapping potential concept towards the ORC data-driven model is introduced.•Methodology proved to be effectiveness and robustness. The reasonable construction of organic Rankine cycle (ORC) data-driven model is the basis of analysis, prediction and optimization. The validity of data, the rationality of input variables and the adaptability of structural parameters are the key to the efficient construction of the model. In the modeling process, the data selected based on experience, the introduction of redundant variables and fixed structural parameters will reduce the prediction accuracy and enhance the instability of the model. In this paper, we fully consider the abnormal characteristics of data, the coupling correlation of variables and the instability characteristics of the actual operation of the system, and propose an integrated online dynamic modeling scheme, which aims to improve the prediction accuracy, anti-interference ability and adaptability of the ORC data-driven model under changeable environment. Parameter information extraction can further improve the prediction accuracy of the model. Mean absolute error can improve by 42.2%. The introduction of network structure design strategy can reduce the construction time of the model by 79.56% and improve the R-square by 62.18%. The combination of adaptive gradient approach and neuronal mapping potential evaluation method can improve the search speed and ensure the fast convergence of the model. The integrated online dynamic modeling scheme proposed can provide necessary theoretical guidance and new modeling idea for the whole process design of ORC data-driven model.