Investigation of contemporary biotechnological processes in animal feed production aimed at increasing food competitiveness, quality and safety

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Investigation of contemporary biotechnological processes in animal feed production aimed at increasing food competitiveness, quality and safety (en)
Истраживање савремених биотехнолошких поступака у производњи хране за животиње у циљу повећања конкурентности, квалитета и безбедности хране (sr)
Istraživanje savremenih biotehnoloških postupaka u proizvodnji hrane za životinje u cilju povećanja konkurentnosti, kvaliteta i bezbednosti hrane (sr_RS)
Authors

Publications

Application of soybean oil and glycerol in animal feed production, ANN model

Dragojlović, Danka; Pezo, Lato; Čolović, Dušica; Vidosavljević, Strahinja; Pezo, Milada L.; Čolović, Radmilo; Kokić, Bojana; Đuragić, Olivera

(2019)

TY  - JOUR
AU  - Dragojlović, Danka
AU  - Pezo, Lato
AU  - Čolović, Dušica
AU  - Vidosavljević, Strahinja
AU  - Pezo, Milada L.
AU  - Čolović, Radmilo
AU  - Kokić, Bojana
AU  - Đuragić, Olivera
PY  - 2019
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8821
AB  - In the past few decades the diet preparation in feed production has evolved towards more complicated technological operations, which include different liquid addition. A wide scale of different liquids is used in contemporary animal feed production, from oils and glycerol to more expensive products in a liquid form, such as enzymes, flavourings, amino acids, vitamins and others. In the presented study the liquid addition in feed production was observed, with a specific goal to investigate the spraying systems in order to better understand the effects of fluids, such as soybean oil and glycerol, on feed production. The dispersion angles of spraying nozzle for glycerol and soybean oil were determined as an indicator of the uniform application of liquids during feed production. Dispersion of the material was accomplished using the two-fluid nozzle. The performance of Artificial Neural Network (ANN) was compared with experimental data in order to develop rapid and accurate method for prediction of dispersion angle. The ANN model showed high prediction accuracy (r2 = 0.945).
T2  - Acta Periodica Technologica
T1  - Application of soybean oil and glycerol in animal feed production, ANN model
IS  - 50
SP  - 51
EP  - 58
DO  - 10.2298/APT1950051D
ER  - 
@article{
author = "Dragojlović, Danka and Pezo, Lato and Čolović, Dušica and Vidosavljević, Strahinja and Pezo, Milada L. and Čolović, Radmilo and Kokić, Bojana and Đuragić, Olivera",
year = "2019",
abstract = "In the past few decades the diet preparation in feed production has evolved towards more complicated technological operations, which include different liquid addition. A wide scale of different liquids is used in contemporary animal feed production, from oils and glycerol to more expensive products in a liquid form, such as enzymes, flavourings, amino acids, vitamins and others. In the presented study the liquid addition in feed production was observed, with a specific goal to investigate the spraying systems in order to better understand the effects of fluids, such as soybean oil and glycerol, on feed production. The dispersion angles of spraying nozzle for glycerol and soybean oil were determined as an indicator of the uniform application of liquids during feed production. Dispersion of the material was accomplished using the two-fluid nozzle. The performance of Artificial Neural Network (ANN) was compared with experimental data in order to develop rapid and accurate method for prediction of dispersion angle. The ANN model showed high prediction accuracy (r2 = 0.945).",
journal = "Acta Periodica Technologica",
title = "Application of soybean oil and glycerol in animal feed production, ANN model",
number = "50",
pages = "51-58",
doi = "10.2298/APT1950051D"
}
Dragojlović, D., Pezo, L., Čolović, D., Vidosavljević, S., Pezo, M. L., Čolović, R., Kokić, B.,& Đuragić, O.. (2019). Application of soybean oil and glycerol in animal feed production, ANN model. in Acta Periodica Technologica(50), 51-58.
https://doi.org/10.2298/APT1950051D
Dragojlović D, Pezo L, Čolović D, Vidosavljević S, Pezo ML, Čolović R, Kokić B, Đuragić O. Application of soybean oil and glycerol in animal feed production, ANN model. in Acta Periodica Technologica. 2019;(50):51-58.
doi:10.2298/APT1950051D .
Dragojlović, Danka, Pezo, Lato, Čolović, Dušica, Vidosavljević, Strahinja, Pezo, Milada L., Čolović, Radmilo, Kokić, Bojana, Đuragić, Olivera, "Application of soybean oil and glycerol in animal feed production, ANN model" in Acta Periodica Technologica, no. 50 (2019):51-58,
https://doi.org/10.2298/APT1950051D . .
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Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal - Chemometric and CFD approach

Banjac, Vojislav; Pezo, Lato; Pezo, Milada L.; Vukmirović, Đuro; Colovic, Dusica; Fistes, Aleksandar; Čolović, Radmilo

(2017)

TY  - JOUR
AU  - Banjac, Vojislav
AU  - Pezo, Lato
AU  - Pezo, Milada L.
AU  - Vukmirović, Đuro
AU  - Colovic, Dusica
AU  - Fistes, Aleksandar
AU  - Čolović, Radmilo
PY  - 2017
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/1492
AB  - In this study, sunflower meal is ground by a hammer mill after which air zigzag gravitational air classifier is used for separating sunflower hulls from the kernels in order to obtain protein rich fractions. Three hammer mill sieves with sieve openings diameter of 3, 2 and 1 mm were used, while three air flows (5, 8.7 and 12.5 m(3)/h) and three feed rates (30%, 60% an 90% of bowl feeder oscillation maximum rate) were varied during air classification process. For describing the effects of the test variables on the observed responses Principal Component Analysis, Standard Score analysis and Response Surface Methodology were used. Beside experimental investigations, CFD model was used for numerical optimization of sunflower meal air classification process. Air classification of hammer milled sunflower meal resulted in coarse fractions enriched in protein content. The decrease in sieve openings diameter of the hammer mill sieve increased protein content in coarse fractions of sunflower meal obtained at same air flow, and at the same time decreased matching fraction yield. Increase in air flow lead to the increase in protein content along the same hammer mill sieve. Standard score analysis showed that optimum values for protein content and ratio of coarse and fine fractions have been obtained by using a sieve with 1 mm opening diameter, air flow of 12.5 m(3)/h and 60% of the maximum feeder rate. Fraction ratio and protein content were mostly affected by the linear term of air flow and the sieve openings diameter of the hammer mill sieve in the Second Order Polynomial model. The main focus of CFD analysis was on the particle simulation and the evaluation of the separation efficiency of the zigzag classifier. (C) 2017 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.
T2  - Advanced Powder Technology
T1  - Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal - Chemometric and CFD approach
VL  - 28
IS  - 3
SP  - 1069
EP  - 1078
DO  - 10.1016/j.apt.2017.01.013
ER  - 
@article{
author = "Banjac, Vojislav and Pezo, Lato and Pezo, Milada L. and Vukmirović, Đuro and Colovic, Dusica and Fistes, Aleksandar and Čolović, Radmilo",
year = "2017",
abstract = "In this study, sunflower meal is ground by a hammer mill after which air zigzag gravitational air classifier is used for separating sunflower hulls from the kernels in order to obtain protein rich fractions. Three hammer mill sieves with sieve openings diameter of 3, 2 and 1 mm were used, while three air flows (5, 8.7 and 12.5 m(3)/h) and three feed rates (30%, 60% an 90% of bowl feeder oscillation maximum rate) were varied during air classification process. For describing the effects of the test variables on the observed responses Principal Component Analysis, Standard Score analysis and Response Surface Methodology were used. Beside experimental investigations, CFD model was used for numerical optimization of sunflower meal air classification process. Air classification of hammer milled sunflower meal resulted in coarse fractions enriched in protein content. The decrease in sieve openings diameter of the hammer mill sieve increased protein content in coarse fractions of sunflower meal obtained at same air flow, and at the same time decreased matching fraction yield. Increase in air flow lead to the increase in protein content along the same hammer mill sieve. Standard score analysis showed that optimum values for protein content and ratio of coarse and fine fractions have been obtained by using a sieve with 1 mm opening diameter, air flow of 12.5 m(3)/h and 60% of the maximum feeder rate. Fraction ratio and protein content were mostly affected by the linear term of air flow and the sieve openings diameter of the hammer mill sieve in the Second Order Polynomial model. The main focus of CFD analysis was on the particle simulation and the evaluation of the separation efficiency of the zigzag classifier. (C) 2017 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.",
journal = "Advanced Powder Technology",
title = "Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal - Chemometric and CFD approach",
volume = "28",
number = "3",
pages = "1069-1078",
doi = "10.1016/j.apt.2017.01.013"
}
Banjac, V., Pezo, L., Pezo, M. L., Vukmirović, Đ., Colovic, D., Fistes, A.,& Čolović, R.. (2017). Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal - Chemometric and CFD approach. in Advanced Powder Technology, 28(3), 1069-1078.
https://doi.org/10.1016/j.apt.2017.01.013
Banjac V, Pezo L, Pezo ML, Vukmirović Đ, Colovic D, Fistes A, Čolović R. Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal - Chemometric and CFD approach. in Advanced Powder Technology. 2017;28(3):1069-1078.
doi:10.1016/j.apt.2017.01.013 .
Banjac, Vojislav, Pezo, Lato, Pezo, Milada L., Vukmirović, Đuro, Colovic, Dusica, Fistes, Aleksandar, Čolović, Radmilo, "Optimization of the classification process in the zigzag air classifier for obtaining a high protein sunflower meal - Chemometric and CFD approach" in Advanced Powder Technology, 28, no. 3 (2017):1069-1078,
https://doi.org/10.1016/j.apt.2017.01.013 . .
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