Aripdjanov, O. Y., Turobjonov, S. M., Nurullaev, S. P., & Azimova, S. A. (2023). Absorbent composites based on DEA and MDEA and using water-soluble polyelectrolytes. EBSCOhost content item. Retrieved June 3, 2025. https://openurl.ebsco.com/contentitem/ gcd:173505949?sid=ebsco:plink:crawler&id=ebsco:gcd:173505949

Akmalxon, K., Suvankul, N., & Jamshidbek, X. (2024). Cleaning of methyldiethanolamine and diethanolamine solutions used in natural gas cleaning. Universum: Technical Sciences, 10(5[122]), Article 5(122). Retrieved June 3, 2025. https://cyberleninka.ru/article/n/cleaning-of- methyldiethanolamine-and-diethanolamine-solutions-used-in-natural-gas-cleaning 7universum.com

Turayev, T. B., Igamkulova, N. A., Mengliyev, S. S., & Rakhimov, H. N. (2024). Recovery of spent methyldiethanolamines and reduced environmental impact. E3S Web of Conferences, 491, 04010. https://doi.org/10.1051/e3sconf/202449104010 e3s-conferences.org+1

Verma, N., & Verma, A. (2009). Amine system problems arising from heat-stable salts and solutions to improve system performance. Fuel Processing Technology, 90(4), 483–489. https://doi. org/10.1016/j.fuproc.2009.02.002

Grushevenko, E. A., Bazhenov, S. D., Vasilevskii, V. P., Novitskii, E. G., & Volkov, A. V. (2018). Two- step electrodialysis treatment of monoethanolamine to remove heat-stable salts. Russian Journal of Applied Chemistry, 91(4), 602–610. https://doi.org/10.1134/S1070427218040110

Meng, H., Zhang, S., Li, C., & Li, L. (2008). Removal of heat-stable salts from aqueous solutions of N-methyldiethanolamine using a specially designed three-compartment configuration electrodialyzer. Journal of Membrane Science, 322(2), 436–440. https://doi.org/10.1016/j.memsci.2008.05.072

Kikhavani, T., Mehdizadeh, H., Van der Bruggen, B., & Bayati, B. (2021). Removal of heat-stable salts from lean amine of a gas refinery via electrodialysis. Chemical Engineering & Technology, 44(2), 318–328. https://doi.org/10.1002/ceat.202000375

Chen, F., et al. (2020). Removal of heat-stable salts from N-methyldiethanolamine wastewater by anion exchange resin coupled three-compartment electrodialysis. Separation and Purification Technology, 242, 116777. https://doi.org/10.1016/j.seppur.2020.116777

Ghorbani, A., et al. (2020). Application of NF polymeric membranes for removal of multicomponent heat-stable salts (HSS) ions from methyl diethanolamine (MDEA) solutions. Molecules, 25(21), Article 4911. https://doi.org/10.3390/molecules25214911

Mansoori, S. A. A., et al. (2022). HSS anions reduction combined with the analytical test of aqueous MDEA in South Pars gas complex. Natural Gas Industry B, 9(3), 318–324. https://doi. org/10.1016/j.ngib.2022.06.004

Afan, H. A., El-Shafie, A., Yaseen, Z. M., Hameed, M. M., Wan Mohtar, W. H. M., & Hussain, A. (2015). ANN-based sediment prediction model utilizing different input scenarios. Water Resources Management, 29(4), 1231–1245. https://doi.org/10.1007/s11269-014-0870-1

Afram, A., Janabi-Sharifi, F., Fung, A. S., & Raahemifar, K. (2017). Artificial neural network (ANN)-based model predictive control (MPC) and optimization of HVAC systems: A state-of-the-art review and case study of a residential HVAC system. Energy and Buildings, 141, 96–113. https://doi. org/10.1016/j.enbuild.2017.02.012

Moon, J. W. (2012). Performance of ANN-based predictive and adaptive thermal-control methods for disturbances in and around residential buildings. Building and Environment, 48, 15–26. https://doi.org/10.1016/j.buildenv.2011.06.005

Eshbobaev, J., Norkobilov, A., Usmanov, K., Khamidov, B., Kodirov, O., & Avezov, T. (2024, September). Control of wastewater treatment processes using a fuzzy logic approach. In The 3rd International Electronic Conference on Processes (p. 39). MDPI. https://doi.org/10.3390/engproc2024067039

Pappada, S. M., et al. (2020). An artificial neural network-based predictive model to support optimization of inpatient glycemic control. Diabetes Technology & Therapeutics, 22(5), 383–394. https://doi.org/10.1089/dia.2019.0252

Meenal, R., & Selvakumar, A. I. (2018). Assessment of SVM, empirical and ANN-based solar radiation prediction models with most influencing input parameters. Renewable Energy, 121, 324–343. https://doi.org/10.1016/j.renene.2017.12.005

Crystals Editorial Office. (2025). Data-driven ANN-based predictive modeling of mechanical properties of 5Cr-0.5Mo steel: Impact of composition and service temperature. Crystals, 15(3), 213. Retrieved June 3, 2025. https://www.mdpi.com/2073-4352/15/3/213

Society of Petroleum Engineers. (2019). Development of ANN-based predictive model for miscible CO₂ flooding in sandstone reservoir. In SPE Middle East Oil and Gas Show and Conference (MEOS 2019). Retrieved June 3, 2025. https://onepetro.org/SPEMEOS/proceedings- abstract/19MEOS/3-19MEOS/D032S086R002/218309

Lee, J., Lim, J., Cho, H., & Kim, J. (2020). Optimization strategies for amine regeneration process with heat-stable salt removal unit. Applied Chemical Engineering, 31(5), 575–580. https://doi. org/10.14478/ace.2020.1073 koreascience.kr+1

Pal, P., AbuKashabeh, A., Al-Asheh, S., & Banat, F. (2014). Accumulation of heat-stable salts and degraded products during thermal degradation of aqueous methyldiethanolamine (MDEA) using microwave digester and high-pressure reactor. Journal of Natural Gas Science and Engineering, 21, 1043–1047. https://doi.org/10.1016/j.jngse.2014.11.007

Eshbobaev, J., Khamidov, B., & Fallanza, M. (2025). Application of an adaptive neuro-fuzzy inference system to control the wastewater treatment process. Chemical Technology, Control and Management, 2025(1), 52–60. https://doi.org/10.59048/2181-1105.1654