Evaluation of energy consumption patterns in rice processing using Taguchi and artificial neural network models

Simple item page
dc.contributor.authorMayowa Saheed Sanusi
dc.contributor.authorRahman Akinoso
dc.date.accessioned2024-04-18T10:13:50Z
dc.date.available2024-04-18T10:13:50Z
dc.date.issued2022-06-28
dc.description.abstractThis study was designed to evaluate and model the impact of processing parameters (steaming time, soaking time, paddy moisture content and soaking temperature) on the energy consumption of five rice varieties (NERICA 8, FARO 52, FARO 61, FARO 60 and FARO 44). Energy consumption in the cleaning, soaking, steaming, drying, dehusking, polishing and grading operations were estimated by fitting data on labour, fuel and electricity consumption, time and machine efficiency into standard equations to determine total energy consumption. The energy consumptions were separately modelled using Taguchi and Artificial Neural Network (ANN) models for each rice variety. The accuracy of models was determined using the coefficient of determination (R2) and Mean Square Error (MSE). Total energy consumption among the rice varieties varied significantly, ranging from 2.31 to 2.33 MJ for white rice, and 45.3 to 76.9 MJ for parboiled rice. Paddy moisture content was observed to be the most important process parameter that influenced energy consumption. Taguchi models were more accurate for total energy consumption prediction [R2 (0.95-0.97); MSE (1.24-1.96)], than ANN [R2 (0.93-0.94); MSE (3.21-3.52)]. The study established appropriate processing conditions that can guarantee minimum energy consumption for NERICA 8, FARO 52, FARO 61, FARO 60 and FARO 44.
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/12387
dc.language.isoen
dc.publisherAgricEngInt: CIGR Journal Open access at http://www.cigrjournal.org
dc.subjectartificial neural network
dc.subjectenergy consumption
dc.subjectmodelling
dc.subjectrice varieties
dc.subjectTaguchi
dc.titleEvaluation of energy consumption patterns in rice processing using Taguchi and artificial neural network models
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
7235-Article Text-36893-1-10-20220628.pdf
Size:
949.04 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections