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 degrees C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13m3/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.

Automated summary

This work develops digital models of a commercial Fresnel Solar Collector (FSC) in an absorption cooling plant. Two modeling approaches are employed and their twinning/adaptation time and performance validation are evaluated. The results show that both models perform well and are suitable for control and optimization.