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Neuro-Fuzzy Digital Twin of a High Temperature Generator
- Source :
- IFAC-PapersOnLine; January 2022, Vol. 55 Issue: 9 p466-471, 6p
- Publication Year :
- 2022
-
Abstract
- Solar absorption plants are renewable energy systems with a special advantage: the cooling demand follows the solar energy source. The problem is that this plant presents solar intermittency, phenomenological complexity, and nonlinearities. That results in a challenge for control and energy management. In this context, this paper develops a Digital Twin of an absorption chiller High Temperature Generator (HTG) seeking accuracy and low computational efort for control and management purposes. A neuro-fuzzy technique is applied to describe HTG, internal Lithium-Bromide temperature, and water outlet temperature. Two Adaptative Neuro-Fuzzy Inference Systems (ANFIS) are trained considering real data of eight days of operation. Then, the obtained model is validated considering two days of real data. The validation shows a RMSE of 1.65e−2for the internal normalized temperature, and 2.05e−2for the outlet normalized temperature. Therefore, the obtained Digital Twin presents a good performance capturing the dynamics of the HTG with adaptive capabilities considering that each day can update the learning step.
Details
- Language :
- English
- ISSN :
- 24058963
- Volume :
- 55
- Issue :
- 9
- Database :
- Supplemental Index
- Journal :
- IFAC-PapersOnLine
- Publication Type :
- Periodical
- Accession number :
- ejs61977946
- Full Text :
- https://doi.org/10.1016/j.ifacol.2022.07.081