APPLICATION OF ARTIFICIAL INTELLIGENCE FOR MODELLING AND OPTIMIZATION OF CO2 ABSORPTION BY NAOH TO FORM Na2CO3

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Published: 2025-12-05
DOI: https://doi.org/10.66073/ReFocus-Conf/474
Keywords:
CO2 absorption, sodium hydroxide, sodium carbonate, artificial intelligence, machine learning, mass transfer, process optimization, chemical engineering innovation

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Eshkulov Bekzod
1st year master student, Karshi State Technical University
Sattorov Xurshid Golib ugli
1st year master student, Karshi State Technical University

Abstract

The growing need to reduce anthropogenic CO2 emissions has intensified research on
carbon capture and utilization (CCU). Chemical absorption of CO2 in sodium hydroxide (NaOH) solutions
forming sodium carbonate (Na₂CO₃) is a well-established method, yet traditional models often fail to
describe the complex nonlinear effects of variables such as concentration, flow rate, and temperature. This
study examines the use of artificial intelligence (AI) and machine learning (ML) for modeling and
optimizing CO2–NaOH absorption. A conceptual ML framework is proposed, outlining key input
parameters and algorithm selection. AI-driven modelling promises improved predictive accuracy, computational efficiency, and adaptive optimization, advancing digital transformation in chemical
engineering 

Article Details

Issue

Vol. 1 No. 1 (2025): Republican scientific-practical conference on “New Paradigms in Science, Technology and Education”

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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Eshkulov Bekzod, & Sattorov Xurshid Golib ugli. (2025). APPLICATION OF ARTIFICIAL INTELLIGENCE FOR MODELLING AND OPTIMIZATION OF CO2 ABSORPTION BY NAOH TO FORM Na2CO3. Partner Conferences of the International Scientific Journal Research Focus, 1(1), 687-692. https://doi.org/10.66073/ReFocus-Conf/474

References

1. Dahlan, I., Suhaimi, M.H.M., 2025. Adsorption of CO2 Using NaOH-Modified Nanoclay Montmorillonite Adsorbent: Comparative Analysis of RSM-Based Central Composite Design and ANNBased Models in Modelling and Optimization. Arab. J. Sci. Eng. https://doi.org/10.1007/s13369-025-

2. Dinul, F.I., Nurdin, H., Rahmadiawan, D., Nasruddin, Laghari, I.A., Elshaarani, T., 2023. Comparison of NaOH and Na2CO3 as absorbents for CO2 absorption in carbon capture and storage technology. J. Eng. Res. Lect. 2, 28–34. https://doi.org/10.58712/jerel.v2i1.23

3. Liu, Z., Esmaeili, A., Zhang, H., Wang, D., Lu, Y., Shao, L., 2022. Modeling and Experimental Studies on Carbon Dioxide Absorption with Sodium Hydroxide Solution in a Rotating Zigzag Bed. Processes 10, 614. https://doi.org/10.3390/pr10030614

4. Liu, Z., Masel, R.I., Chen, Q., Kutz, R., Yang, H., Lewinski, K., Kaplun, M., Luopa, S., Lutz, D.R., 2016. Electrochemical generation of syngas from water and carbon dioxide at industrially important rates. J. CO2 Util. 15, 50–56. https://doi.org/10.1016/j.jcou.2016.04.011

5. M Aldi Nugroho, n.d. Carbon Dioxide (CO2) Absorption Process Using Sodium Hydroxide

6. Nassef, A.M., 2023. Improving CO2 Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment. Sustainability 15, 9512. https://doi.org/10.3390/su15129512

7. Zheng, H., Mirlekar, G., Nord, L.O., 2022. Machine learning techniques for modeling chemical absorption in CO2 capture process. Presented at the 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022, pp. 72–79. https://doi.org/10.3384/ecp192011

8.O‘rmonjonov Azizbek Muhammadjon o‘g‘li. (2025). MAKTABGACHA TA’LIM YOSHIDAGI IMKONIYATI CHEKLANGAN BOLALAR UCHUN INKLYUZIV TA’LIM SHAROITIDA MUSIQIY TA’LIMNI TASHKIL ETISH METODIKASI. Kelajak o’qituvchisi - Kelajak Bunyodkori, 1(1), 470-475.

9.Ramziddin Tursunov. (2025). IJTIMOIY ONG VA MA’NAVIYAT TUSHUNCHASI HAMDA UNING MAZMUN-MOHIYATI. Kelajak o’qituvchisi - Kelajak Bunyodkori, 1(2), 36-40