Digital twin model and global sensitivity analysis of an indirect type solar dryer with sensible heat storage material: An approach from exergy sustainability indicators under tropical climate conditions

Cetina-Quiñones A., ARICI M., Cisneros-Villalobos L., Bassam A.

Journal of Energy Storage, vol.58, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 58
  • Publication Date: 2023
  • Doi Number: 10.1016/j.est.2022.106368
  • Journal Name: Journal of Energy Storage
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Keywords: Sustainable agriculture, Solar dryer technology, Thermal energy storage, Intelligent models, Exergy performance, ARTIFICIAL NEURAL-NETWORK, DRYING SYSTEMS, ENERGY, PERFORMANCE, COLLECTORS
  • Kocaeli University Affiliated: Yes


© 2022 Elsevier LtdThis research study presents the development of a digital twin model using artificial neural networks and global sensitivity analysis to estimate different exergy sustainability indicators (improvement potential, waste exergy ratio, and sustainability index) of a conventional indirect type solar dryer and an indirect solar dryer with a thermal storage system (ITSD-TSS) from the experimental evaluation of tomato drying under tropical climate. The artificial neural network was trained using the database generated from the solar drying process, where solar radiation, ambient temperature, wind velocity, and relative humidity were considered model inputs to estimate the annual values of the absorber plate, solar collector, and drying chamber outlet air temperatures. Subsequently, these temperatures were used to determine the exergetic sustainability indicators from a solar dryer energy balance. On the other hand, the Sobol method was used for the global sensitivity analysis, which allowed obtaining the sensitivity indices and the percentage contribution of each input variable. The experimental results demonstrated that the minimum drying time was reached at 22 h with ITSD-TSS with limestone and the drying efficiency was 12.57 %. Regarding the artificial neural network, a maximum R2 value of 0.9896 was achieved between the experimental and simulated data obtained using the Levenberg-Marquardt algorithm, and the maximum values of the absorber plate, solar collector, and drying chamber outlet air temperatures were 98.62, 81.71, and 81.64 °C, respectively. The best annual average values obtained for the improvement potential and waste exergy ratio were 389.87 W and 0.49, respectively, for the conventional dryer, and the sustainability index was 1.58, corresponding to the ITSD-TSS with beach sand. The global sensitivity analysis revealed maximum values of 0.6096 and 0.8911 for the first-order and total-order Sobol indices, respectively, and the percentage with the highest contribution was 58.4 %, corresponding to the ambient temperature in the drying chamber outlet air temperature. Finally, the economic evaluation indicated that the ITSD-TSS with limestone presented the highest annual savings with $ 1264 USD, but the conventional dryer reported the lowest payback period of 0.12 years. The methodology used in this work allows a more representative estimation of the storage system integration in an indirect solar dryer from an exergetic approach, which is essential for evaluating new technologies that integrate energy storage or other processes to improve its performance.