Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X

Modeling and optimization of the effect of broiler diet formulation and conditioning temperature on the physical quality of pellet and production efficiency of poultry feed factory

Document Type : Research Paper

Authors

Mohaddeseh Esnaashari 1
Hamed Ahmadi 1
Farid Shariatmadari 1
Mostafa Lotfi 2

1 Department of Poultry Science, Tarbiat Modares University

2 College of Agriculture, Tarbiat Modares University

https://doi.org/10.22059/jap.2023.346524.623701
Abstract
This study was conducted to investigate the effects of mixer added fat, crude protein and conditioning temperature on the pellet durability index, and electrical energy consumption during feed production using computational modeling tools. A total of 192 broiler feed samples with different levels of mixer added fat and crude protein in feed components and different conditioning temperatures to determine the pellet durability index, modified pellet durability index and electrical energy consumption during feed production were used. Multiple linear regression and artificial neural network were used to analyze data. Both models had the ability to predict the value of the pellet durability index, modified pellet durability index and the electrical energy consumption during feed production; but the prediction accuracy of the artificial neural network model was higher than that of the multiple linear regression model for all three outputs. Optimization was done using the artificial neural network model, and in these calculations, in order to achieve the highest possible level of pellet physical quality and the lowest possible level of electrical energy consumption, the crude protein amount was 20-20.5% and the conditioning temperature was predicted to be 85 C. However, the amount of fat was predicted to be 1% for the highest amount of pellet physical quality and 4% for the lowest amount of electrical energy consumption during production. In practical conditions, this model can help in more accurate prediction of electricity consumption and the quality of produced feed in order to achieve the optimal situation in feed production factories.

Keywords

Artificial neural network
Computational modeling
Electric energy consumed
Pellet physical quality
Production efficiency
  1. Abadi MHMG, Moravej H, Shivazad M, Torshizi MAK and Kim WK (2019) Effect of different types and levels of fat addition and pellet binders on physical pellet quality of broiler feeds. Poultry science, 98(10): 4745-4754.
  2. Abdollahi M, Ravindran V and Svihus B (2013) Pelleting of broiler diets: An overview with emphasis on pellet quality and nutritional value. Animal Feed Science and Technology, 179(1-4): 1-23.
  3. Abdollahi MR, Zaefarian F and Ravindran V (2019) Maximising the benefits of pelleting diets for modern broilers. Animal Production Science, 59(11): 2023-2028.
  4. Ahmadi H (2017) A mathematical function for the description of nutrient-response curve. Plos one, 12(11): e0187292.
  5. Ahmadi H and Golian A (2010) The integration of broiler chicken threonine responses data into neural network models. Poultry science, 89(11): 2535-41.
  6. Ahmadi H and Rodehutscord M (2017) Application of artificial neural network and support vector machines in predicting metabolizable energy in compound feeds for pigs. Frontiers in nutrition, 4 27.
  7. Ayoola OA (2020) Influence of the animal feed binders on optimal nutritional and physical qualities of the animal feed pellets and feed production capacity-A literature review. Norwegian University of Life Sciences, Ås.
  8. Buchanan N, Lilly K, Gehring C and Moritz J (2010) The effects of altering diet formulation and manufacturing technique on pellet quality. Journal of Applied Poultry Research, 19(2): 112-120.
  9. Buchanan N and Moritz J (2009) Main effects and interactions of varying formulation protein, fiber, and moisture on feed manufacture and pellet quality. Journal of Applied Poultry Research, 18(2): 274-283.
  10. Catalá-Gregori P, García V, Madrid J, Orengo J and Hernández F (2009) Inclusion of dried bakery product in high fat broiler diets: effect on pellet quality, performance, nutrient digestibility and organ weights. Asian-Australasian Journal of Animal Sciences, 22(5): 686-693.
  11. Cubes P Crumbles-Definitions and Methods for Determining Density, Durability and Moisture Content. ASAE Standards S, 269.
  12. Fahrenholz AC (2012) Evaluating factors affecting pellet durability and energy consumption in a pilot feed mill and comparing methods for evaluating pellet durability: Kansas State University.
  13. Glover B, Foltz K, Holásková I and Moritz J (2016) Effects of modest improvements in pellet quality and experiment pen size on broiler chicken performance. Journal of Applied Poultry Research, 25(1): 21-28.
  14. Gulati T, Chakrabarti M, Sing A, Duvuuri M and Banerjee R (2010) Usporedno ispitivanje metode odzivnih površina, umjetne neuralne mreže i genetskog algoritma radi optimiranja hidratacije zrna soje. Food Technology and Biotechnology, 48(1): 11-18.
  15. InstituteInc S (1982) SAS/STAT User’s Guide: SAS Institute Inc. Cary, North Carolina.
  16. LOAR II R and Corzo A (2011) Effects of feed formulation on feed manufacturing and pellet quality characteristics of poultry diets. World's Poultry Science Journal, 67(1): 19-28.
  17. Loar II R, Wamsley K, Evans A, Moritz J and Corzo A (2014) Effects of varying conditioning temperature and mixer-added fat on feed manufacturing efficiency, 28-to 42-day broiler performance, early skeletal effect, and true amino acid digestibility. Journal of Applied Poultry Research, 23(3): 444-455.
  18. Lou W and Nakai S (2001) Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products. Journal of Agricultural and Food Chemistry, 49(4): 1799-1804.
  19. Massuquetto A, Durau J, Schramm V, Netto MT, Krabbe E and Maiorka A (2018) Influence of feed form and conditioning time on pellet quality, performance and ileal nutrient digestibility in broilers. Journal of Applied Poultry Research, 27(1): 51-58.
  20. MATLAB V (2018) 9.0. 0.341360 (R2018b). Natick, Massachusetts: The MathWorks Inc.
  21. Netto MT, Massuquetto A, Krabbe E, Surek D, Oliveira S and Maiorka A (2019) Effect of conditioning temperature on pellet quality, diet digestibility, and broiler performance. Journal of Applied Poultry Research, 28(4): 963-973.
  22. Rueda M, Rubio A, Starkey C, Mussini F and Pacheco W (2022) Effect of conditioning temperature on pellet quality, performance, nutrient digestibility, and processing yield of broilers. Journal of Applied Poultry Research, 31(2): 100235.
  23. Tillman N, Jones M and Pacheco W (2020) Influence of feed ingredients, conditioning temperature, and a dacitic tuff breccia (AZOMITE) on pellet production rate and pellet quality. Journal of Applied Poultry Research, 29(1): 162-170.
  24. Zohair GA, Al-Maktari GA and Amer MM (2012) A comparative effect of mash and pellet feed on broiler performance and ascites at high altitude (field study). Global Veterinaria, 9(2): 154-159.
Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X
Volume 25, Issue 1
Spring 2023
Pages 93-105

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