نشریه علمی پژوهشی مهندسی آبیاری و آب ایران

نشریه علمی پژوهشی مهندسی آبیاری و آب ایران

ترکیب الگوریتم های یادگیری ماشین و انتخاب ویژگی در بهینه سازی پیش بینی جذب فسفات از محلول های آبی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی محیط زیست، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران
2 گروه آبیاری و زهکشی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.
10.22125/iwe.2025.518646.1876
چکیده
بهینه‌سازی با استفاده از روش‌‌های هوش مصنوعی، یک رویکرد موثر برای بهبود عملکرد سامانه‌ها و فرایند‌ها است. این روش‌ها امکان یافتن پارامترهای مؤثرتر و برنامه‌ریزی بر روی آن‌ها جهت ارتقای راندمان جذب فسفات را فراهم می‌آورند.

پژوهش حاضر بر توسعه الگوریتم‌های یادگیری ماشین پیش‌بینی‌کننده با رویکرد کاهش ابعاد متمرکز بود. به‌منظور توسعه مدل پیش‌بینی، داده‌های تجربی جذب فسفات به‌وسیله هیدروچار باگاس نیشکر از طریق تنظیم جذب در مقیاس آزمایشگاهی به‌دست آمد. پنج متغیر ورودی مستقل، شامل غلظت اولیه آلاینده، زمان تماس، جرم جاذب، دمای محلول و pH، در فرآیند آموزش در نظر گرفته شدند. علاوه بر این، راندمان جذب فسفات به عنوان خروجی در نظر گرفته شد. از میان الگوریتم‌های به کار گرفته شده، رگرسیور درختان اضافی (ET) با R2 برابر با 922/0 و همچنین با مقادیر پایین‌تر RMSE (074/0) و MAE (048/0) عملکرد نسبتاً بهتری در پیش‌بینی راندمان جذب فسفات ارائه کرد. بر اساس نتایج، دو عامل ورودی که بیشترین تأثیر را بر اثربخشی جذب فسفات دارند زمان تماس و غلظت اولیه فسفات هستند. علاوه بر این، مشخص شد که مقدار جاذب به عنوان پارامتر‌هایی با کمترین تاثیر شناخته شدند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

The Combination of Machine Learning Algorithms and Feature Selection Methods in Optimizing Phosphate Adsorption Prediction from Aqueous Solutions

نویسندگان English

Laleh Divband Hafshejani 1
Hamid Abdolabadi 1
Abd Ali Naseri 2
1 Department of Environmental Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Department of Irrigation and Drainage Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz
چکیده English

Optimization using artificial intelligence methods is an effective approach to improving the performance of systems and processes. These methods enable the identification of more effective parameters and their optimization to enhance phosphate adsorption efficiency.

The present study focused on developing predictive machine learning algorithms with a dimensionality reduction approach. To develop the predictive model, experimental data on phosphate adsorption by sugarcane bagasse hydrochar were obtained through laboratory-scale adsorption experiments. Five independent input variables, including initial pollutant concentration, contact time, adsorbent mass, solution temperature, and pH, were considered in the training process. Additionally, phosphate adsorption efficiency was considered as the output. Among the applied algorithms, the Extra Trees Regressor (ET) demonstrated relatively better performance in predicting phosphate adsorption efficiency, with an R² value of 0.922, as well as lower RMSE (0.074) and MAE (0.048) values. Based on the results, the two input factors with the greatest impact on phosphate adsorption effectiveness were contact time and initial phosphate concentration. Furthermore, the adsorbent mass was identified as the parameter with the least impact.

کلیدواژه‌ها English

Hydrochar
Contact time
Initial concentration
Algorithm
Abdi, J., & Mazloom, G. (2022). Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks. Scientific Reports, 12(1), 16458.
Almanassra, I. W., Mckay, G., Kochkodan, V., Atieh, M. A., & Al-Ansari, T. (2021). A state of the art review on phosphate removal from water by biochars. Chemical Engineering Journal, 409, 128211.
Awolusi, T. F., Oke, O. L., Akinkurolere, O. O., & Atoyebi, O. D. (2019). Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres. Cogent engineering, 6(1), 1649852.
Büyükkeçeci, M., & Okur, M. C. (2022). A comprehensive review of feature selection and feature selection stability in machine learning. Gazi University Journal of Science, 36(4), 1506-1520.
Crini, G. (2008). Kinetic and equilibrium studies on the removal of cationic dyes from aqueous solution by adsorption onto a cyclodextrin polymer. Dyes and Pigments, 77(2), 415-426.
da Silva Bruckmann, F., Schnorr, C. E., da Rosa Salles, T., Nunes, F. B., Baumann, L., Müller, E. I., Silva, L. F., Dotto, G. L., & Bohn Rhoden, C. R. (2022). Highly efficient adsorption of tetracycline using chitosan-based magnetic adsorbent. Polymers, 14(22), 4854.
Divband Hafshejani, L., Naseri, A. A., Moradzadeh, M., Daneshvar, E., & Bhatnagar, A. (2022). Applications of soft computing techniques for prediction of pollutant removal by environmentally friendly adsorbents (case study: the nitrate adsorption on modified hydrochar). Water Science & Technology, 86(5), 1066-1082.
El Touati, Y., Slimane, J. B., & Saidani, T. (2024). Adaptive Method for Feature Selection in the Machine Learning Context. Engineering, Technology & Applied Science Research, 14(3), 14295-14300.
Elshishini, H. M., El-Subruiti, G. M., Ghatass, Z. F., Farag, N. H., & Eltaweil, A. S. (2025). Assessing decision-based machine learning algorithms for predicting adsorption efficiency: a detailed study of MnFe LDH functionalized La (OH) ₃@ AC chitosan beads. Journal of Water Process Engineering, 69, 106678.
Fu, W., Feng, M., Guo, C., Zhou, J., Zhang, X., Lv, S., Huo, Y., & Wang, F. (2024). Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives. Bioresource Technology, 130861.
Guo, Y., Ali, W., Schneider, A., Salma, A., Mayer‐Gall, T., Gutmann, J. S., & Fernandez Lahore, H. M. (2022). Megaporous monolithic adsorbents for bioproduct recovery as prepared on the basis of nonwoven fabrics. Electrophoresis, 43(13-14), 1387-1398.
Hafshejani, L. D., Hooshmand, A., Naseri, A. A., Mohammadi, A. S., Abbasi, F., & Bhatnagar, A. (2016). Removal of nitrate from aqueous solution by modified sugarcane bagasse biochar. Ecological Engineering, 95, 101-111.
Iftikhar, S., Ishtiaq, R., Zahra, N., Ruba, F., Lam, S.-M., Abbas, A., & Jaffari, Z. H. (2025). Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment. Chemosphere, 370, 144031.
Lyu, H., Xu, Z., Zhong, J., Gao, W., Liu, J., & Duan, M. (2024). Machine learning-driven prediction of phosphorus adsorption capacity of Biochar: insights for adsorbent design and process optimization. Journal of environmental management, 369, 122405.
Manyatshe, A., Cele, Z. E., Balogun, M. O., Nkambule, T. T., & Msagati, T. A. (2022). Chitosan modified sugarcane bagasse biochar for the adsorption of inorganic phosphate ions from aqueous solution. Journal of Environmental Chemical Engineering, 10(5), 108243.
Nouaa, S., Aziam, R., Benhiti, R., Carja, G., Zerbet, M., & Chiban, M. (2024). Exploiting RSM and ANN modeling methods to optimize phosphate ions removal using LDH/alginate composite beads. Journal of Water Process Engineering, 68, 106333.
Ogata, F., Nagai, N., Iijima, S., Toda, M., Otani, M., Nakamura, T., & Kawasaki, N. (2021). Exploiting the different parameters on the adsorption of phosphate ions and its subsequent recovery using complex nickel–aluminum–zirconium hydroxide. Chemical and Pharmaceutical Bulletin, 69(8), 789-795.
Pannu, M. W., Huang, A., & Plumlee, M. H. (2024). Variable PFAS removal by adsorbent media with sufficient prediction of breakthrough despite reduced contact time at pilot scale. Water Environment Research, 96(5), e11035.
Rahdar, S., Rahdar, A., Sattari, M., Hafshejani, L. D., Tolkou, A. K., & Kyzas, G. Z. (2021). Barium/cobalt@ polyethylene glycol nanocomposites for dye removal from aqueous solutions. Polymers, 13(7), 1161.
Suresh, S., Newton, D. T., Everett IV, T. H., Lin, G., & Duerstock, B. S. (2022). Feature selection techniques for a machine learning model to detect autonomic dysreflexia. Frontiers in Neuroinformatics, 16, 901428.
Tee, G. T., Gok, X. Y., & Yong, W. F. (2022). Adsorption of pollutants in wastewater via biosorbents, nanoparticles and magnetic biosorbents: A review. Environmental Research, 212, 113248.
Thawornchaisit, U., Onlamai, T., Phurkphong, N., & Sukharom, R. (2021). Sugarcane Bagasse-derived Hydrochar: Modification with Cations to Enhance Phosphate Removal: 10.32526/ennrj/19/202100036. Environment and Natural Resources Journal, 19(5), 371-380.
Tran, H. N. (2023). Applying linear forms of pseudo-second-order kinetic model for feasibly identifying errors in the initial periods of time-dependent adsorption datasets. Water, 15(6), 1231.
Vujić, M., Vasiljević, S., Nikić, J., Kordić, B., Agbaba, J., & Tubić, A. (2023). Sorption Behavior of Organic Pollutants on Biodegradable and Nondegradable Microplastics: pH Effects. Applied Sciences, 13(23), 12835.
Wu, Y., Li, Y., Jiang, Z., Xu, Z., Yang, M., Ding, J., & Zhang, C. (2023). Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents. ACS ES&T Engineering, 3(8), 1135-1146.
Yang, K., Yan, L.-g., Yang, Y.-m., Yu, S.-j., Shan, R.-r., Yu, H.-q., Zhu, B.-c., & Du, B. (2014). Adsorptive removal of phosphate by Mg–Al and Zn–Al layered double hydroxides: kinetics, isotherms and mechanisms. Separation and Purification Technology, 124, 36-42.
Ye, J., Cong, X., Zhang, P., Zeng, G., Hoffmann, E., Wu, Y., Zhang, H., & Fang, W. (2016). Operational parameter impact and back propagation artificial neural network modeling for phosphate adsorption onto acid-activated neutralized red mud. Journal of molecular liquids, 216, 35-41.
Zhang, J., Shan, W., Ge, J., Shen, Z., Lei, Y., & Wang, W. (2012). Kinetic and equilibrium studies of liquid-phase adsorption of phosphate on modified sugarcane bagasse. Journal of Environmental Engineering, 138(3), 252-258.
Zhang, W., Huang, W., Tan, J., Huang, D., Ma, J., & Wu, B. (2023). Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. Chemosphere, 311, 137044.
Zhang, Y., & Pan, B. (2014). Modeling batch and column phosphate removal by hydrated ferric oxide-based nanocomposite using response surface methodology and artificial neural network. Chemical Engineering Journal, 249, 111-120.
Zhou, Y., Lu, J., Zhou, Y., & Liu, Y. (2019). Recent advances for dyes removal using novel adsorbents: a review. Environmental pollution, 252, 352-365.
Zhu, X., & Ma, J. (2020). Recent advances in the determination of phosphate in environmental water samples: Insights from practical perspectives. TRAC Trends in Analytical Chemistry, 127, 115908.