Experimental investigation of solar PVT collector with the dryer on mass and temperature of dried red chili with Machine Learning Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10258967" target="_blank" >RIV/61989100:27230/25:10258967 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2590123025018985?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123025018985?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.105827" target="_blank" >10.1016/j.rineng.2025.105827</a>

Result language

angličtina

Original language name

Experimental investigation of solar PVT collector with the dryer on mass and temperature of dried red chili with Machine Learning Models

Original language description

The growing demand for efficient drying methods in agricultural processes has motivated researchers to explore hybrid systems using photovoltaic and thermal systems. One challenge faced in this research was the limited amount of drying days and dealing with one crop (red chilies), which could limit the potential application for other crops. The kinetics of drying red chilies with a photovoltaic thermal (PVT) system were in combination with a dryer, and the objective was to enhance the drying process of red chilies under various environmental conditions of surface glazing temperature, solar radiation, the temperature of the outflow fluid, and ambient temperature. Three drying techniques open sun drying, forced convection drying, and natural convection drying with varying flow velocities served as the foundation for the research. Using this controlled case-study method, 3 kg of red chilies were dried under standard sun radiation from an ambient temperature of 31 degrees C to a high of 58 degrees C. The moisture content reduction process was measured by starting with 79 % moisture content by weight after drying the red chilies for a total of six days conducting the experiments between 9am and 4pm during Mars, April, and May 2023, each time on clear days with direct sunlight. The drying process was stop at 11 % moisture content after 6-day testing round of red chilies drying. Machine learning (ML) models, namely the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree (DT), were used to forecast the temperature and mass dryness variables.The RBF model showed the best performance with 0.98, 0.95, and 0.92 for temperature dryness, above MLP and DM. The research finds that the RBF model had the highest capacity to forecast drying efficiency and that forced convection drying is more effective than open solar drying.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Results in Engineering

ISSN

2590-1230

e-ISSN

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Volume of the periodical

27

Issue of the periodical within the volume

Neuveden

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

9

Pages from-to

nestránkováno

UT code for WoS article

001523253700015

EID of the result in the Scopus database

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