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This study presents the design and evaluation of a model-based predictive control (MPC) system for precise temperature regulation during the mashing stage of beer production. The mashing process requires a strict temperature 
profile to ensure optimal enzymatic activity, maximize sugar yield, and maintain consistent product quality. A dynamic mathematical model of the mash tun, derived from energy balance equations that account for heat input and thermal 
losses, was employed as the predictive core of the controller. The MPC algorithm forecasts future temperature trajectories over a finite horizon and determines optimal heating inputs while enforcing operational constraints on temperature and energy usage. Numerical simulations were carried out in Python with the NumPy, Matplotlib, and Control libraries, enabling accurate process modeling, optimization, and visualization of control performance. Results show that MPC achieves a maximum temperature deviation of ±0.2°C, reduces total heating energy consumption by approximately 15%, and demonstrates significantly faster recovery from disturbances. These findings demonstrate that MPC offers a robust and energy-efficient solution for industrial mashing control, with potential benefits for improving beer quality and reducing production costs.

  • Ссылка в интернете
  • DOI
  • Дата создание в систему UzSCI 23-10-2025
  • Количество прочтений 14
  • Дата публикации 17-10-2025
  • Язык статьиIngliz
  • Страницы65-75
English

This study presents the design and evaluation of a model-based predictive control (MPC) system for precise temperature regulation during the mashing stage of beer production. The mashing process requires a strict temperature 
profile to ensure optimal enzymatic activity, maximize sugar yield, and maintain consistent product quality. A dynamic mathematical model of the mash tun, derived from energy balance equations that account for heat input and thermal 
losses, was employed as the predictive core of the controller. The MPC algorithm forecasts future temperature trajectories over a finite horizon and determines optimal heating inputs while enforcing operational constraints on temperature and energy usage. Numerical simulations were carried out in Python with the NumPy, Matplotlib, and Control libraries, enabling accurate process modeling, optimization, and visualization of control performance. Results show that MPC achieves a maximum temperature deviation of ±0.2°C, reduces total heating energy consumption by approximately 15%, and demonstrates significantly faster recovery from disturbances. These findings demonstrate that MPC offers a robust and energy-efficient solution for industrial mashing control, with potential benefits for improving beer quality and reducing production costs.

Ўзбек

Ushbu ishda pivo ishlab chiqarishda zator tayyorlash jarayoni bosqichida haroratni aniq boshqarish uchun model asosidagi bashoratli boshqaruv (MPC) tizimini ishlab chiqish va baholash taqdim etilgan. Zator tayyorlash jarayoni fermentlar faolligini optimal darajada ta’minlash, maksimal shakar hosil bo‘lishi va mahsulot sifati barqarorligini saqlash uchun qat’iy harorat profilini talab qiladi. Zator tayyorlash uskunasidagi jarayonning dinamik matematik modeli issiqlik kirishi va issiqlik yo‘qotishlarini hisobga olgan holda tuzilgan issiqlik balansi tenglamalariga asoslanadi hamda  boshqaruvchi algoritmning prognozlash yadrosi sifatida xizmat qiladi. MPC algoritmi ma’lum vaqt oralig‘ida kelajakdagi harorat o‘zgarishlarini bashorat qiladi va harorat hamda energiya sarfi bo‘yicha texnologik cheklovlarga rioya qilgan holda optimal isitish qiymatlarini aniqlaydi. Jarayonni aniq modellashtirish, optimallashtirish va boshqaruv natijalarini vizuallashtirish maqsadida hisoblash tajribalari Python dasturlash tilida NumPy, Matplotlib va Control kutubxonalaridan foydalangan holda o‘tkazildi. Natijalar MPC tizimi maksimal ±0,2 °C harorat og‘ishini ta’minlagani, umumiy issiqlik energiyasi sarfini taxminan 15 %ga kamaytirgani va buzilishlardan keyin tezroq tiklanishini ko‘rsatdi. Ushbu natijalar MPC tizimi sanoat zator tayyorlash jarayonida barqaror va energiya samarali yechim ekanligi, pivo sifatini oshirish va ishlab chiqarish xarajatlarini kamaytirishga xizmat qilishini tasdiqlaydi.

Русский

В исследовании представлена разработка и оценка системы предиктивного управления на основе модели (MPC) для точного регулирования температуры на стадии затирания при производстве пива. Процесс затирания требует строгого температурного профиля для обеспечения оптимальной ферментативной активности, 
максимального выхода сахаров и стабильного качества продукции. В качестве прогностического ядра контроллера использована динамическая математическая модель заторного чана, выведенная на основе уравнений теплового баланса с учётом тепловых потерь и притока тепла. Алгоритм MPC прогнозирует будущие траектории температуры на конечном горизонте и определяет оптимальные режимы нагрева при соблюдении 
эксплуатационных ограничений по температуре и энергопотреблению. Численные моделирования выполнены в среде Python с использованием библиотек NumPy, Matplotlib и Control, что позволило обеспечить точное 
моделирование процесса, оптимизацию и визуализацию эффективности управления. Результаты показали, что MPC обеспечивает максимальное отклонение температуры не более ±0,2 °C, снижает суммарное энергопотребление на нагрев примерно на 15 % и демонстрирует значительно более быстрое восстановление после возмущений. Эти выводы подтверждают, что MPC является надёжным и энергоэффективным решением для промышленного управления процессом затирания, способным повысить качество пива и снизить производственные затраты.

№ Имя автора Должность Наименование организации
1 Yusupov M.S. PhD student, Department of Automation of Production Procesess Tashkent State Technical University
№ Название ссылки
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13 Pogo, S. I., Arias, J. F., & Andaluz, V. H. (2021). Control of the malt mashing and boiling process in craft beer production: Hardware-in-the-loop technique. In L. T. De Paolis, P. Arpaia, & P. Bourdot (Eds.), Augmented Reality, Virtual Reality, and Computer Graphics (Vol. 12980, pp. 701–716). Springer. https://doi.org/10.1007/978-3-030-87595-4_51
14 Schwenzer, M., Ay, M., Bergs, T., & Abel, D. (2021). Review on model predictive control: An engineering perspective. The International Journal of Advanced Manufacturing Technology, 117(5–6), 1327–1349. https://doi.org/10.1007/s00170-021-07682-3
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20 Yusupbekov, A., & Yusupov, M. (2025). Pivo ishlab chiqarishda fermentlash jarayonini modellashtirish [Modeling of the fermentation process in beer production]. Development of Science, 3(5), 342–346.
21 Yusupbekov, N. R., Gulyamov, Sh. M., & Doshchanova, M. Yu. (2020). Neuro-fuzzy modeling for predictive control systems with complex technological processes and production. International Journal of Engineering Research, 1, 73–83. https://doi.org/10.34920/2020.1.73-83
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