Publication: Development of a Predictive Model of Water Hyacinth Drying in Solar Parabolic Dome with Artificial Neural Networks
2
0
Issued Date
2025-12-01
Resource Type
ISSN
26877295
eISSN
26877309
DOI
Scopus ID
2-s2.0-105027808222
Journal Title
Es Food and Agroforestry
Volume
22
Rights Holder(s)
SCOPUS
Bibliographic Citation
Es Food and Agroforestry Vol.22 (2025)
Suggested Citation
Jongpluempiti J., Vengsungnle P., Poojeera S., Srichat A., Siricharoenpanich A., Naphon P. Development of a Predictive Model of Water Hyacinth Drying in Solar Parabolic Dome with Artificial Neural Networks. Es Food and Agroforestry Vol.22 (2025). doi:10.30919/faf1932 Retrieved from: https://hdl.handle.net/20.500.14740/55141
Corresponding Author(s)
Other Contributor(s)
Abstract
Water hyacinth benefits humans and the environment. Cellulose makes water Hyacinth a biofuel. It encourages eco-friendly furniture, décor, and toys. This study dries hyacinths of various sizes in a solar parabolic dome to investigate how they dehumidify and then builds an artificial neural network model to forecast relative humidity. This study includes statistical and artificial neural network analysis at all experimental stages. Drying conditions in solar parabolic greenhouses reveal that all three hyacinth sizes dehumidify over time, decreasing the moisture ratio. These differences may be due to a tighter internal fiber structure or crust formation on the material's surface during baking, which slows water evaporation from the inside to the outside. This illustrates that material size affects dehumidification performance. Operating time and Hyacinth size affect the drying rate. We assessed the model's performance using R<sup>2</sup> and MSE values. These studies show that a few nodes may regulate automated and industrial drying processes. ANNs simplify machine learning and optimization by revealing complex data patterns and linkages, which are crucial for time series prediction, classification, and regression. Big data can help them improve over time.