Drying Characteristics, Kinetic Modeling, Energy and Exergy Analyses of Water Yam (Dioscorea alata) in a Hot Air Dryer
Artykuł w czasopiśmie
MNiSW
140
Lista 2023
| Status: | |
| Autorzy: | Okunola Abiodun A., Adekanye Timothy A., Okonkwo Clinton E., Kaveh Mohammad, Szymanek Mariusz, Idahosa Endurance O., Olayanju Adeniyi T., Wojciechowska Krystyna |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2023 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 4 |
| Wolumen/Tom: | 16 |
| Numer artykułu: | 1569 |
| Strony: | 1 - 21 |
| Impact Factor: | 3,0 |
| Web of Science® Times Cited: | 4 |
| Scopus® Cytowania: | 5 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 4 lutego 2023 |
| Abstrakty: | angielski |
| In this study, drying characteristics, kinetic modelling, energy and exergy analyses of a convective hot air dryer are presented for water yam. The drying experiments were carried out at temperature levels of 50, 60, and 70°C and slice thicknesses of 3, 6, and 9 mm. The effects of drying variables on the drying rate (DR), moisture diffusivity (Deff), activation energy (Ea), energy utilization (EU), energy utilization ratio (EUR), exergy loss (EXL), exergy efficiency (EXeff), improvement potential (IP), and exergetic sustainability index (ESI) were investigated. The results showed that increasing air temperature increased the DR, Deff, EU, EUR, EXL, EXeff, IP, and ESI, while increasing the slice thickness increased Deff and Ea, but decreased the DR. The highest Deff and Ea values were 4.2 × 10−8 m2/s, and 53 KJ/mol, respectively. EU and EUR varied from 10 to 150 J/s and 0.39 to 0.79, respectively. EXL and EXeff varied between 2 and 12.5 J/s and 58 to 75 %, respectively. Midilli’s model had the best performance in predicting the moisture ratio of water yam with coefficient of determination (R2 = 0.9998), root mean square error (RMSE = 0.0049), and sum of square error (SSE = 0.0023). |
