Izvorni znanstveni članak
https://doi.org/10.15255/KUI.2020.049
Novel Approach for Predicting Direct and Open Solar Drying Using Artificial Neural Network for Medicinal Plant
Ahmed Sadadou
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares Médéa, Algeria
Salah Hanini
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares Médéa, Algeria
Maamar Laidi
orcid.org/0000-0002-8977-9895
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares Médéa, Algeria
Ahmed Rezrazi
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares Médéa, Algeria
Sažetak
In this study, an artificial neural network (ANN) was developed to obtain a generalized model for predicting the direct and open sun drying process for some medicinal plants. Since the quality of the experimental dataset can lead to a very performant model, in this study the dataset was collected from previously published papers and divided randomly into three subsets, namely 70 %, 15 %, and 15 % for training, testing, and validation. Based on the complex solar drying behaviour, ten parameters were considered as inputs: time, global solar radiation (GSR), outside temperature, inclination, emissivity, altitude, longitude, latitude, inside temperature, and nutritional value, to predict moisture content (MC), and drying rate (DR). Based on a trial and error method, the best ANN model was found with a topology of 10-28-14-2, with regression coefficient and root mean square error of (R = 97.044 %. RMSE = 4.589 %) and (R = 99.968 %, RMSE = 1.185 %) for MC and DR, respectively. It can be concluded that the obtained ANN model provides the best method for solar dryer modelling which can be generalized for any location in the world.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
artificial neural network; medicinal plant; solar drying; moisture content; drying rate
Hrčak ID:
254683
URI
Datum izdavanja:
27.3.2021.
Posjeta: 762 *