Интеграция традиционных и компьютерных методов в оптимизацию процессов увлажнения пшеничного зерна
Математическое моделирование и программная реализация для повышения эффективности увлажнения зерна
Ключевые слова:
гидротермическая обработка,, влажность зерна, компьютерное моделирование, характеристики зерна, технолологические процессыАннотация
В данном исследовании рассматривается процесс гидротермической обработки пшеничного зерна с целью оптимизации условий его увлажнения для повышения качества муки. Используя как традиционные методы анализа, так и современные компьютерные технологии моделирования, в частности программу COMSOL Multiphysics, были изучены динамика распределения влаги в различных слоях зерна (оболочка, эндосперм, зародыш) и влияние этих процессов на эффективность помола. Экспериментальные результаты показали, что холодное и скоростное кондиционирование имеют различные преимущества в зависимости от физико-химических характеристик зерна.
Библиографические ссылки
. Chateigner-Boutin, A.-L., Lapierre, C., Alvarado, C., Yoshinaga, A., Barron, C., Bouchet, B., Bakan, B., Saulnier, L., Devaux, M.-F., Girousse, C., & Guillon, F. (2018). Ferulate and lignin cross-links increase in cell walls of wheat grain outer layers during late development. Plant Science, 276, 199-207. https://doi.org/10.1016/j.plantsci.2018.08.022
. James, C., Smith, D., He, W., Chandra, S. S., & Chapman, S. C. (2024). GrainPointNet: A deep-learning framework for non-invasive sorghum panicle grain count phenotyping. Computers and Electronics in Agriculture, 217, 108485. https://doi.org/10.1016/j.compag.2023.108485
. Shi, J., Ding, Z., Ge, X., Qiu, X., Xu, J., Xiao, L., Liu, L., Tang, L., Cao, W., Zhu, Y., & Liu, B. (2024). Compound extreme heat and drought stress alter the spatial gradients of protein and starch in wheat grains. Agricultural Water Management, 303, 109049. https://doi.org/10.1016/j.agwat.2024.109049
. Kang, S., Kim, Y., Ajani, O. S., Mallipeddi, R., & Ha, Y. (2024). Predicting the properties of wheat flour from grains during debranning: A machine learning approach. Heliyon, 10(17), e36472. https://doi.org/10.1016/j.heliyon.2024.e36472
. Shi, T., Gao, Y., Song, J., Ao, M., Hu, X., Yang, W., Chen, W., Liu, Y., & Feng, H. (2024). Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains. Food Chemistry, 461, 140651. https://doi.org/10.1016/j.foodchem.2024.140651
. Wang, Y., Ou, X., He, H.-J., & Kamruzzaman, M. (2024). Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chemistry: X, 21, 101235. https://doi.org/10.1016/j.fochx.2024.101235
. Zhang, C., Yi, Y., Wang, L., Chen, S., Li, P., Zhang, S., & Xue, Y. (2024). Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery. Smart Agricultural Technology, 9, 100581. https://doi.org/10.1016/j.atech.2024.100581
. Wang, W., Huang, Z., Fu, Z., Jia, L., Li, Q., & Song, J. (2024). Impact of digital technology adoption on technological innovation in grain production. Journal of Innovation & Knowledge, 9(3), 100520. https://doi.org/10.1016/j.jik.2024.100520
. Shah, S. A. A., Luo, H., Pickupana, P. D., Ekeze, A., Sohel, F., Laga, H., Li, C., Paynter, B., & Wang, P. (2022). Automatic and fast classification of barley grains from images: A deep learning approach. Smart Agricultural Technology, 2, 100036. https://doi.org/10.1016/j.atech.2022.100036
. Kumar, G., Le, D. T., Durco, J., Cianciosi, S., Devkota, L., & Dhital, S. (2023). Innovations in legume processing: Ultrasound-based strategies for enhanced legume hydration and processing. Trends in Food Science & Technology, 139, 104122. https://doi.org/10.1016/j.tifs.2023.104122
. Guo, Y., Xiao, Y., Hao, F., Zhang, X., Chen, J., de Beurs, K., He, Y., & Fu, Y. H. (2023). Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 124, 103528. https://doi.org/10.1016/j.jag.2023.103528
. Wu, W., Yang, T.-l., Li, R., Chen, C., Liu, T., Zhou, K., Sun, C.-m., Li, C.-y., Zhu, X.-k., & Guo, W.-s. (2020). Detection and enumeration of wheat grains based on a deep learning method under various scenarios and scales. Journal of Integrative Agriculture, 19(8), 1998-2008. https://doi.org/10.1016/S2095-3119(19)62803-0
. Shafaei, S. M., Nourmohamadi-Moghadami, A., Rahmanian-Koushkaki, H., & Kamgar, S. (2019). Neural computing efforts for integrated simulation of ultrasound-assisted hydration kinetics of wheat. Information Processing in Agriculture, 6(3), 357-374. https://doi.org/10.1016/j.inpa.2019.01.001
. Naik, N. K., Sethy, P. K., Behera, S. K., & Amat, R. (2024). A methodical analysis of deep learning techniques for detecting Indian lentils. Journal of Agriculture and Food Research, 15, 100943. https://doi.org/10.1016/j.jafr.2023.100943
. Asefa, B. G., Tsige, F., Mehdi, M., Kore, T., & Lakew, A. (2023). Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique. Smart Agricultural Technology, 3, 100097. https://doi.org/10.1016/j.atech.2022.100097
. Agarwal, D., Sweta, & Bachan, P. (2023). Machine learning approach for the classification of wheat grains. Smart Agricultural Technology, 3, 100136. https://doi.org/10.1016/j.atech.2022.100136
. Laabassi, K., Belarbi, M. A., Mahmoudi, S., Mahmoudi, S. A., & Ferhat, K. (2021). Wheat varieties identification based on a deep learning approach. Journal of the Saudi Society of Agricultural Sciences, 20(5), 281-289. https://doi.org/10.1016/j.jssas.2021.02.008
. Steinberg, T. S., Morozova, O. V., & Semikina, L. I. (2014). Storage and processing of agricultural raw materials. VNIIZ Grain and Products of Its Processing, 310, 47-51. https://vniiz.org/science/publication/article-60
. Wu W, Zhao Y, Wang H, Yang T, Hu Y, Zhong X, Liu T, Sun C, Sun T, Liu S. WG-3D: A Low-Cost Platform for High-Throughput Acquisition of 3D Information on Wheat Grain. Agriculture. 2022; 12(11):1861. https://doi.org/10.3390/agriculture12111861
. Webpupil. (01.2014). Web tutorial. Retrieved October 1, 2024, from https://www.webpupil.ru/article.php?id=14
. Urinboev, A. A., & Ismailov, B. R. (2022). On the problems of developing automated control systems in grain preparation at flour mills. In Proceedings of the International Scientific and Practical Conference "Auezov Readings - 22: Academician Kanysh Satpaev - Founder of Kazakh Science" (pp. 5-7). Shymkent, Republic of Kazakhstan.
Загрузки
Дополнительные файлы
Опубликован
Как цитировать
Выпуск
Раздел
Категории
Лицензия
Copyright (c) 2025 Abdushukur Urinboev, Б.Р. Исмаилов

Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.