SANOAT TEXNOLOGIK TIZIMLARINI INTELLEKTUAL MODELLASHTIRISH VA REAL VAQTLI BOSHQARUV STRATEGIYALARINI OPTIMALLASHTIRISH USULLARI

Mualliflar

  • O‘rinjon Choriyev

Kalit so'zlar

https://doi.org/10.47390/ts-v3i12y2025N04

Kalit so'zlar

sun’iy intellekt, IoT, Industry 4.0, intellektual modellashtirish, real vaqt rejimidagi boshqaruv, Model Predictive Control, neyron tarmoqlar, raqamli egizak, kimyo sanoati, neft va gaz sanoati, bashoratlash, optimallashtirish, ma’lumotlarga asoslangan model.

Annotasiya

Ushbu maqolada sanoat texnologik tizimlarini intellektual modellashtirish usullari va real vaqt rejimidagi boshqaruv strategiyalarini optimallashtirish masalalari yoritiladi. “Industry 4.0” konsepsiyasi doirasida sun’iy intellekt, IoT, raqamli egizaklar, neyron tarmoqlar va Model Predictive Control (MPC) kabi ilg‘or texnologiyalarni sanoat jarayonlariga integratsiya qilishning nazariy va amaliy asoslari bayon etiladi. Murakkab kimyoviy va neft-kimyo tizimlarini fizik qonunlarga asoslangan va ma’lumotlarga asoslangan yondashuvlar orqali modellashtirishga alohida e’tibor qaratiladi. Maqolada Python dastur kodi, graflar va matematik modellar yordamida silliqlash, bashoratlash va optimallashtirish algoritmlarini amalga oshirish misollari keltiriladi. Shuningdek, real vaqt rejimidagi optimallashtirish, raqamli egizaklarning o‘rni hamda mukofotga asoslangan o‘rganish texnologiyalari sanoatning avtonom boshqaruv tizimlariga o‘tishidagi muhim bosqichlar sifatida tahlil qilinadi.

Manbalar

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2. Lu, Y. (2017). Cyber–physical system (CPS)-based Industry 4.0: A survey. Journal of Industrial Information Integration, 6, 1–10. https://doi.org/10.1016/j.jii.2016.08.001

3. Oks, S. J., Jalowski, M., Lechner, M., Mirschberger, S., Merklein, M., Vogel-Heuser, B., & Möslein, K. M. (2024). Cyber-Physical Systems in the Context of Industry 4.0: A Review, Categorization and Outlook. Information Systems Frontiers, 26(5), 1731–1772. https://doi.org/10.1007/s10796-022-10252-x

4. Perno, M., Hvam, L., Haug, A., & Mortensen, N. H. (2022). Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers. Computers & Chemical Engineering, 163, 107801. https://doi.org/10.1016/j.compchemeng.2022.107801

5. Mane, S., Mulik, A., Patwardhan, S. C., & Narasimhan, S. (2024). Digital twin in the chemical industry: A review. IET Digital Twin, 2(2), 57–80. https://doi.org/10.1049/dtw2.12020

6. Ai, H., Zhou, Y., Wang, L., & Huang, G. Q. (2025). Advances in digital twin technology in industry: A review of recent developments and future prospects. Journal of Manufacturing Systems. (In press).

7. Forbes, M. G., Patwardhan, R. S., Hamadah, H., & Gopaluni, B. (2015). Model predictive control in industry: Challenges and opportunities. IFAC-PapersOnLine, 48(8), 531–538. https://doi.org/10.1016/j.ifacol.2015.09.022

8. Rawlings, J. B., Mayne, D. Q., & Diehl, M. (2017). Model Predictive Control: Theory, Computation, and Design (2nd ed.). Nob Hill Publishing.

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Yuborilgan

2025-12-26

Nashr qilingan

2025-12-27

Qanday ko'rsatish

Choriyev , O. (2025). SANOAT TEXNOLOGIK TIZIMLARINI INTELLEKTUAL MODELLASHTIRISH VA REAL VAQTLI BOSHQARUV STRATEGIYALARINI OPTIMALLASHTIRISH USULLARI. Techscience Uz - Topical Issues of Technical Sciences, 3(12), 25–33. https://doi.org/10.47390/ts-v3i12y2025N04

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