2013
DOI: 10.2478/v10247-012-0066-y
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Drying kinetics of dill leaves in a convective dryer

Abstract: A b s t r a c t. Thin layer drying characteristics of dill leaves under fixed, semi-fluidized, and fluidized bed conditions were studied at air temperatures of 30, 40, 50, and 60°C. In order to find a suitable drying curve, 12 thin layer-drying models were fitted to the experimental data of the moisture ratio. Among the applied mathematical models, the Midilli et al. model was the best for drying behavior prediction in thin layer drying of dill leaves. To obtain the optimum network for drying of dill leaves, v… Show more

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Cited by 31 publications
(18 citation statements)
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“…The results indicate that a higher microwave power causes a rapid mass transfer within the sample. Similar result were reported by other researches [8], [11] The moisture contents of mendong samples at different microwave powers were converted to the moisture ratio (MR) and fitted to the three mathematical equation for drying models listed in Tab. 1.…”
Section: Figure 2 Various Of Moisture Ratio With Time For Mendong Sasupporting
confidence: 59%
See 1 more Smart Citation
“…The results indicate that a higher microwave power causes a rapid mass transfer within the sample. Similar result were reported by other researches [8], [11] The moisture contents of mendong samples at different microwave powers were converted to the moisture ratio (MR) and fitted to the three mathematical equation for drying models listed in Tab. 1.…”
Section: Figure 2 Various Of Moisture Ratio With Time For Mendong Sasupporting
confidence: 59%
“…ANN majorly used to predict the processes in the processing of agricultural products. Several previous studies have predicted and modeled the drying rate of some postharvest products, such as, dill leaves [11], tomatoes [12], grape [13], and cassava and mango [15] The type of network used in this study is the multi-layer perceptron network. Multi-layer perceptron network are one of the most popular and successful neural network architectures, which are widely applied such as prediction and process modeling [12].…”
Section: Artificial Neural Network For This Studymentioning
confidence: 99%
“…Water activity is a measure of available water in foods suitable for microbial activities and dehydration is one of the controls. Drying influences physicochemical and quality characteristics of products, thus, evaluation of the drying characteristics as a function of drying conditions could help in predicting suitable drying conditions (Motevali et al, 2013;Raji and Ojediran, 2011). Drying temperature has the greatest effect on thin-layer drying, followed by initial moisture content, air velocity, and relative humidity.…”
Section: Resultsmentioning
confidence: 99%
“…Singh dan Pandey (2011) menggunakan pendekatan JST untuk memprediksi kinetika pengeringan pada pengeringan ubi jalar. Motevali et al (2013) menggunakan JST untuk menentukan energi aktivasi dan difusivitas lengas efektif dill leaves. Kaveh dan Chayyan (2014) memprediksi beberapa sifat fisik dan pengeringan terebinthfruit menggunakan JST.…”
Section: *Penulis Korespodensiunclassified