Veštačka inteligencija u procesnim tehnologijama kroz modelovanje, optimizaciju i održivi razvoj
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
http://orcid.org/0009-0000-1958-6388
Apstrakt
Veštačka inteligencija (VI) postaje ključni faktor u transformaciji industrijskih sektora, pružajući brojne prednosti u oblasti modelovanja, optimizacije i održivog razvoja. Ovaj rad istražuje primenu VI u procesnim tehnologijama u različitim industrijama, uključujući hemijsku, energetsku, automobilsku, poljoprivrednu i prehrambenu industriju. Kroz analizu tehnika VI kao što su prediktivno modelovanje, optimizacija energetske efikasnosti i smanjenje otpada, rad pokazuje kako VI može poboljšati efikasnost, smanjiti troškove i povećati konkurentnost. Poseban fokus je na doprinosu VI održivom razvoju, smanjenju emisije CO₂, optimizaciji resursa i smanjenju negativnog uticaja na životnu sredinu [1]. Takođe, istražuju se izazovi i prepreke u implementaciji VI, kao i preporuke za efikasno usvajanje tehnologija [2]. Rezultati istraživanja ukazuju na to da je VI ključna za postizanje održivog razvoja i unapređenje industrijskih praksi, čineći ih ekološki odgovornijima i ekonomičnijima [6].
##plugins.themes.bootstrap3.article.details##
Reference
[2] Smith, J. A., & Brown, L., Artificial Intelligence in Process Engineering. Journal of Sustai-nable Development, (2023), 15(4), pp. 205-220.
[3] Gupta, R., & Kumar, S., Optimization Techniques in Process Technologies. Industrial Engi-neering Review, (2022), 10(2), pp. 134-150.
[4] Lee, H., & Chen, P., Machine Learning Applications in Industrial Processes. AI and Society, (2021), 36(1), pp. 50-65.
[5] Smith, J., Artificial Intelligence in Industrial Processes: Optimization and Sustainability. Cambridge University Press., (2021).
[6] Zhang, H., & Liu, F., Sustainable Industrial Practices Using AI and Machine Learning. Else-vier., (2019).
[7] Chen, X., & Wang, L., “Optimization of Energy Consumption in Manufacturing Processes Using AI Algorithms, ”Journal of Cleaner Production, (2020), 243(1), pp. 118-132.
[8] Patel, R., & Kumar, M., “Predictive Maintenance in Chemical Industries Using Machine Learning Models,” Industrial Engineering Journal, (2018), 45(2), pp. 456-470.
[9] International Energy Agency (IEA), Digitalization and Artificial Intelligence in Energy Systems: Opportunities and Challenges. IEA Report., (2020).
[10] World Economic Forum, AI for Sustainable Development: Case Studies from the Manu-facturing Sector. WEF Report., (2021).
[11] European Commission, Artificial Intelligence for Green Growth: Policy Guidelines and Stra-tegic Approaches. EC Report., (2021).
[12] UN Sustainable Development Goals (SDGs) Report., Artificial Intelligence and Industry 4.0: Achieving SDGs through Technological Innovation., (2020).
[13] McKinsey & Company., AI in the Industrial Sector: Driving Efficiency and Sustainability. McKinsey Global Report., (2020).
[14] PwC., The Future of Artificial Intelligence in the Manufacturing Industry. PwC Industry Insights., (2019).
[15] M. J. G. Weijnen et al., “Artificial Intelligence in Chemical Engineering: Challenges and Opportunities,” Chemical Engineering Research and Design, (2020)., 160, pp. 5-12.
[16] Y. R. He & M. X. Chen, “Application of AI in Precision Agriculture: A Review,” Computers and Electronics in Agriculture, 180, 105973., (2021).
[17] McKinsey & Company., Artificial Intelligence and Sustainability in Energy and Manufactu-ring., (2020).
[18] A. Brown, M. Williams, et al. “Optimization of Manufacturing Processes Using Artificial Intelligence,” Manufacturing Technology Review., (2019)., 39(3), pp. 235-246.
[19] International Renewable Energy Agency (IRENA)., Artificial Intelligence in Energy Systems. IRENA Report., (2021).
[20] Aničić O., Jović S., Skrijelj H., Nedić B., Prediction of laser cutting heat affected zone by extreme learning machine, Optics and Lasers in Engineering, (2017): 1-4. doi: https://dx.doi.org/10.1016/j.optlaseng.2016.07.005