UI Postgraduate College

DEVELOPMENT AND OPTIMISATION OF AN INTEGRATED SEMI-AUTOMATED GRADING MACHINE FOR COWPEA (Vigna unguiculata (L.) WALP) SEEDS

Show simple item record

dc.contributor.author AUDU, John
dc.date.accessioned 2024-05-23T12:11:18Z
dc.date.available 2024-05-23T12:11:18Z
dc.date.issued 2023-01
dc.identifier.uri http://hdl.handle.net/123456789/2304
dc.description.abstract Nigeria is the largest producer of cowpea. Despite this relatively large production, its export has been hindered by poor seed grading and inefficient processing. Existing cowpea grading machines are mostly for unit operations. Integrated grading machine are needed for improved seed grading and efficient processing. Therefore, this study was designed to develop an integrated semi-automated cowpea grading machine. Standard methods were used to determine the optical and electrical parameters of three indigenous cowpea seed varieties (NG/AD/11/08/0033, NG/OA/11/08/063 and NGB/OG/0055) for the automation unit design considerations. This was carried out at seed moisture levels (8.0, 10.0, 12.0, 14.0 and 16.0%), light wavelength (320, 420, 520, 620 and 720 nm) and current frequency (1, 500, 1000, 1500, 2000 kHz). Thereafter, an integrated semi-automated machine with three separating units was developed and automated using machine vision technology. Operational parameters used for evaluation were speed of drum (40, 60 and 80) rpm, bucket conveyor speed (250, 300 and 350) rpm and metering disc (12, 16 and 20) rpm; seed variety and grade (9.8%, 16.0% and 21.0%) of impurity. The total machine system output was evaluated and optimised in terms of efficiency, throughput, maximum capacity, actual utilisation and backlog, using response surface methodology. Prediction interval and multiple regression analysis were used for validation at α 0.05. The optical properties ranged from: 0-1.8%, 0-1.0%, 0-12.0%, ([38-92.2%] [0.7-9.0%] [13.6-27.3%]) for absorbance, reflectance, transmittance and colour (L* a* b*), respectively; while electrical properties ranged from 1.926-15.625 Ω, 0.272-2.209 Ωm, 0.064–0.519 S, 0.453–3.671 S/m, 1.800x10-11–1.380x10-7 F, 0.500-4928.570, 6.020 x107- 9.040x1021 H) and 1.150x106–1.450x107 Ω, for resistance, resistivity, conductance, conductivity, inductance and impedance, respectively. The two separating units (sieve drums) removed impurities > 12 mm and < 2 mm with efficiency of 76.6±9.343% and 85.3±11.1%; throughput of 0.220±0.139kg/hr and 0.144±0.111kg/hr, respectively. The third digital automated sorting unit separated diseased and insect damaged seeds by colour with efficiency of 82.1±7.2% and throughput of 1.386± 0.758kg/hr. Operational parameters were found to have significant effect on all evaluation terms. The efficiency, throughput,vi maximum capacity, actual utilisation and backlog of the total system output ranged from 63.5-80.4%, 0.574–3.732 kg/hr, 6.882-44.778 kg/12hr, 0.083-0.083 (8.3%) and 0.03–0.182 kg, respectively. At 80.4% efficiency, the impurity of grade 3 was reduced to grade 2, and 2 to 1 based on the standard export grade range. The integrated machine system optimisation achieved two best solutions. The first and second having maximum total system impurity separating efficiency of 81.3 and 79.9%, maximum total system throughput of 3.470 and 5.077 kg/hr and minimal total system backlog of 0.064 and 0.07 kg, respectively. The validation data were within 95% low and high prediction intervals. The evaluation terms had coefficient of determinations (R2) values > 0.9 showing no significance between predicted and validation data. The developed integrated semi-automated grading machine for cowpea reduced the impurity in indigenous cowpea varieties to exportable grade. en_US
dc.language.iso en en_US
dc.subject Digital sorting, Machine optimisation, Cowpea grading machine. en_US
dc.title DEVELOPMENT AND OPTIMISATION OF AN INTEGRATED SEMI-AUTOMATED GRADING MACHINE FOR COWPEA (Vigna unguiculata (L.) WALP) SEEDS en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics