Digital twin of a Fresnel solar collector for solar cooling.

This work develops digital entities of a commercial Fresnel Solar Collector (FSC) installed in an absorption cooling plant. The objective is to create and validate models that describe the FSC dynamics across its whole operation range during the day and the night. Thus, the temperatures range between operation temperature of 180 ° C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13 m 3 / h. The idea is that the digital twin will aid start-up and shut-down optimization and control design reliability. The paper employs two modeling approaches, then evaluates their twinning/adaptation time and performance validation. One model uses phenomenological modeling through Partial Differential Equations (PDE) and parameters identification, and another uses a data-driven technique with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The available measurement data sets comprise 25 days of operation with a sampling time of 20 s which, after outlier removal, filtering and treatment, resulted in 108416 samples. The validation considers six separate operating days. Results show that both models can twinning/adapt considering measured data. The models present pretty good results and are suitable for control and optimization. Besides, this is the first paper considering the FSC mirror defocus action on dynamic modeling and validation. • This work validates neuro-fuzzy (NF) and differential (PDE) models with massive data. • The models generally represent the process day and night. • The models are fairly accurate and precise—worst-case MAPE of 2.49%. • The NF model has ten times faster simulation time than the PDE model. • The validated dynamic models are the first accounting with mirror's focus action. [ABSTRACT FROM AUTHOR]