Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Grading Process Engineering within Tilapia (Oreochromis niloticus) Fish Ponds

Grading Process Engineering within Tilapia (Oreochromis niloticus) Fish Ponds Despite the emergent trend of trying to mechanize all agricultural operations in Egyptian fields several decades ago, but aquaculture still does not have any significant share in this direction through nursing, rearing, harvesting and even postharvest techniques. Tilapias grading is considered to be one of the most important postharvest processes for marketing optimization. The main idea of manufacturing Tilapias grader is depending on the basics of fish behavior (attractive to current water (rheotactic)) by stimulating the Tilapias to trace the withdrawn water within the grader passed or retained through four sieves which have been placed in Tilapias movement course doing self-grading. Sieves sizes were determined according to the preliminary study of Tilapias morphology. The relationship between Tilapias individual mass and its dimensions was obtained. Tilapia’s depth and thickness were the main dimensions used to determine the Tilapias identity. Three levels of water flow discharging or three profiles of water escaping (superficial) velocity (100, 375 and 500LPM) and two inclinations of grader raceway (5° and 7°) were investigated. The grader performance was demonstrated by studying the selectivity curve, selection range, individual mass mean selection and grading efficiency for each sieve. Maximum sieve grading efficiency achieved was of 97.87% at 375 LPM and 5° grader inclination. Grader operational capacity was of 2000 kg/h. This productivity can be achieved manually by eight workers for three hours. The behavior of each sieve (allow to pass or retain) during grading process towards each length or individual mass was modeled correspondingly with Logistic and Richard models. Richard model was found to be the best fit model for all the investigated sieves. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Agricultural Engineering and Biotechnology Bowen Publishing Company Ltd.

Grading Process Engineering within Tilapia (Oreochromis niloticus) Fish Ponds

Download PDF
12 pages

Loading next page...
 
/lp/bowen-publishing-company-ltd/grading-process-engineering-within-tilapia-oreochromis-niloticus-fish-s3lwEdQL8I
Publisher
Bowen Publishing Company Ltd.
Copyright
Copyright © BOWEN PUBLISHING COMPANY LTD.
ISSN
2331-3234
eISSN
2331-3463
DOI
10.18005/JAEB0302001
Publisher site
See Article on Publisher Site

Abstract

Despite the emergent trend of trying to mechanize all agricultural operations in Egyptian fields several decades ago, but aquaculture still does not have any significant share in this direction through nursing, rearing, harvesting and even postharvest techniques. Tilapias grading is considered to be one of the most important postharvest processes for marketing optimization. The main idea of manufacturing Tilapias grader is depending on the basics of fish behavior (attractive to current water (rheotactic)) by stimulating the Tilapias to trace the withdrawn water within the grader passed or retained through four sieves which have been placed in Tilapias movement course doing self-grading. Sieves sizes were determined according to the preliminary study of Tilapias morphology. The relationship between Tilapias individual mass and its dimensions was obtained. Tilapia’s depth and thickness were the main dimensions used to determine the Tilapias identity. Three levels of water flow discharging or three profiles of water escaping (superficial) velocity (100, 375 and 500LPM) and two inclinations of grader raceway (5° and 7°) were investigated. The grader performance was demonstrated by studying the selectivity curve, selection range, individual mass mean selection and grading efficiency for each sieve. Maximum sieve grading efficiency achieved was of 97.87% at 375 LPM and 5° grader inclination. Grader operational capacity was of 2000 kg/h. This productivity can be achieved manually by eight workers for three hours. The behavior of each sieve (allow to pass or retain) during grading process towards each length or individual mass was modeled correspondingly with Logistic and Richard models. Richard model was found to be the best fit model for all the investigated sieves.

Journal

Journal of Agricultural Engineering and Biotechnology – Bowen Publishing Company Ltd.

Published: May 27, 2015

There are no references for this article.