|
Ude, C. J.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
Nwankwo, H. F.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
Ibrahim, U. M.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
Ayanyemi, J. O.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
Dirioha, C.
Agricultural and Bio-resources Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
Oke, E. O.
Food Process System Engineering Research Unit, Chemical Engineering Department, Michael Okpara University of Agriculture, Umudike, Abia State State, Nigeria
ABSTRACT
This study was
based on optimal prediction of the physical properties of turmeric rhizome
drying using Artificial Neural Network (ANN) developed in Matlab R2014b. The
drying experiments were conducted at Drying Temperature: 40-650C, Drying Time: 0-240 minutes,
thickness: 2mm – 5mm and air velocity: 1.5-3.0 to determine the angle of repose
of the dried turmeric using wood, mild steel and glass surface, bulk density
and surface area. Levenberg–Marquardt backpropagation (LMA) showed the best
prediction among the ten backpropagation algorithms in all the physical
properties studied. The optimization of the ANN model using LMA algorithm gave
the best MSE value for angle of repose using wood at neuron 8 (0.000041), angle
of repose using mild steel at neuron number 13 (0.000021), angle of repose
using glass at neuron 7 (0.031223), bulk density at neuron 9 (0.000017) and
surface area at neuron 14 (0.000256). It was concluded that increase in the
number of neurons yield better prediction for the model which will be essential
in process control and modelling of drying systems for food crops.
Keywords: Turmeric, Artificial Neural Network, Drying, Physical properties, Backpropagation
|
View: 348 | Download: 6
Conference Code
IECON2019
Conference Title
ENGINEERING FOR SUSTAINABLE ECONOMIC DIVERSIFICATION, FOOD AND NATIONAL SECURITY
ISBN
978-978-53175-8-9
Date Published
Friday, September 20, 2019
Conference Date
2nd - 4th September, 2019
The contents of the articles are the sole opinion of the author(s) and not of UJET.
|
