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An artificial intelligence model to estimate the fat addition ratio for the mixing process in the animal feed industry

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Abstract

In animal feed pellets, the fat content is obtained either from the feed ingredients or is directly added during processing. Additional fat is required when the fat level in the feed ingredients is less than the desired level. This fat can be added either during the mixing process or after the pelleting process. However, adding fat at different time leads to different results. The addition of an increasing amount of fat during the mixing process decreases the pellet durability but enhances the pellet production rate. To avoid a reduction in the pellet durability, limiting the inclusion of fats in the mixer is suggested. The use of suitable fat addition ratios during mixing and after pelleting can improve the pellet quality and the production capability. Many factors significantly affect the decision of how much fat to add, such as the fiber inclusion content in the feed formulation, pellet die size, required feed durability, total required fat, and required additional fat. Due to frequent changes in the feed mix, anticipating the suitable amount of fat addition during the mixing process becomes a cumbersome task for a mill. In this paper, a model for estimating the amount of fat required in the mixer for each feed formulation is proposed. The model is based on the local linear map (LLM) and the back-propagation neural network (BPNN) methods. The LLM is used to identify which feed formulations require the addition of fat both during mixing and after pelleting, whereas the BPNN is employed for estimating the proper total fat required in the mixer, and the ratio of fat to add during the mixing process is subsequently estimated by subtracting the fat in the raw material from the total fat required in the mixer. The model is developed using data from one the largest feed mills in Thailand. The proposed model provides an accurate prediction and is practical for implementation in the mill that was studied.

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Acknowledgments

This research was funded by The National Research University of Khon Kaen University Fund under project Ph.d.54131 and the Betagro Science Center Co., Ltd. The second author was supported by the Thailand Research Fund (TRF), the Office of the Higher Education Commission (OHEC), Khon Kaen University (grant number MRG5580032). This research was partially supported by the Centre of Excellence in Mathematics, the Commission on Higher Education, Thailand.

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Authors and Affiliations

  1. Supply Chain and Logistics Research Unit, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand

    Mongkon Ittiphalin & Supachai Pathumnakul

  2. Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand

    Banchar Arnonkijpanich

  3. The Centre of Excellence in Mathematics, CHE, Si Ayutthaya Rd., Bangkok, 10400, Thailand

    Banchar Arnonkijpanich

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  1. Mongkon Ittiphalin
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  2. Banchar Arnonkijpanich
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  3. Supachai Pathumnakul
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Correspondence to Supachai Pathumnakul.

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Ittiphalin, M., Arnonkijpanich, B. & Pathumnakul, S. An artificial intelligence model to estimate the fat addition ratio for the mixing process in the animal feed industry. J Intell Manuf 28, 219–228 (2017). https://doi.org/10.1007/s10845-014-0972-x

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  • DOI: https://doi.org/10.1007/s10845-014-0972-x

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