{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T12:23:09Z","timestamp":1792844589836,"version":"4.5.0"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Asian J Civ Eng"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s42107-025-01489-3","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T15:38:40Z","timestamp":1754408320000},"page":"39-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adsorption-based distillery effluent treatment: comparative analysis of machine learning models for predicting treatment efficiency"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7843-9124","authenticated-orcid":false,"given":"Chaitali K.","family":"Nikhar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-3001","authenticated-orcid":false,"given":"Gayatri S.","family":"Vyas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rupa S.","family":"Dalvi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dipak Y.","family":"Bhoye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"key":"1489_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-20762-y","volume":"12","author":"J Abdi","year":"2022","unstructured":"Abdi, J., & Mazloom, G. (2022). Machine learning approaches for predicting arsenic adsorption from water using porous metal\u2013organic frameworks. Scientific Reports, 12, 1\u201313. https:\/\/doi.org\/10.1038\/s41598-022-20762-y","journal-title":"Scientific Reports"},{"key":"1489_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/140\/1\/012029","author":"M Adib","year":"2018","unstructured":"Adib, M., Attahirah, M., & Amirza, A. (2018). Phosphoric acid activation of sugarcane bagasse for removal of o-toluidine and benzidine. IOP Conference Series: Earth and Environmental Science. https:\/\/doi.org\/10.1088\/1755-1315\/140\/1\/012029","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"1489_CR3","doi-asserted-by":"publisher","first-page":"41073","DOI":"10.1007\/s11356-022-25066-2","volume":"30","author":"S Agarwal","year":"2023","unstructured":"Agarwal, S., Singh, A., & Mathur, S. (2023). Removal of COD and color from textile industrial wastewater using wheat straw activated carbon: An application of response surface and artificial neural network modeling. Environmental Science and Pollution Research, 30, 41073\u201341094. https:\/\/doi.org\/10.1007\/s11356-022-25066-2","journal-title":"Environmental Science and Pollution Research"},{"key":"1489_CR4","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.aej.2023.03.055","volume":"71","author":"R Ahmad Aftab","year":"2023","unstructured":"Ahmad Aftab, R., Zaidi, S., Khan, A., Usman, M., Khan, A., Chani, M., & Aseri, A. (2023). Removal of congo red from water by adsorption onto activated carbon derived from waste black cardamom peels and machine learning modeling. Alexandria Engineering Journal, 71, 355\u2013369. https:\/\/doi.org\/10.1016\/j.aej.2023.03.055","journal-title":"Alexandria Engineering Journal"},{"key":"1489_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10187-1","author":"H Al-Jamimi","year":"2024","unstructured":"Al-Jamimi, H., BinMakhashen, G., & Saleh, T. (2024). From data to clean water: XGBoost and Bayesian optimization for advanced wastewater treatment with ultrafiltration. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-024-10187-1","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"1489_CR6","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1111\/wej.12552","volume":"34","author":"D Amenorfenyo","year":"2020","unstructured":"Amenorfenyo, D., Huang, X., Li, C., Li, F., Zeng, Q., Zhang, N., Xie, L., & Wang, P. (2020). A review of microalgae and other treatment methods of distillery wastewater. Water and Environment Journal, 34(1), 988\u20131002. https:\/\/doi.org\/10.1111\/wej.12552","journal-title":"Water and Environment Journal"},{"key":"1489_CR7","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.sajce.2024.04.003","volume":"49","author":"J Bwapwa","year":"2024","unstructured":"Bwapwa, J., Mkhize, N., & Seyam, M. (2024). Evaluation of operational efficiency and performance for a water treatment plant. South African Journal of Chemical Engineering, 49, 11\u201334. https:\/\/doi.org\/10.1016\/j.sajce.2024.04.003","journal-title":"South African Journal of Chemical Engineering"},{"key":"1489_CR8","doi-asserted-by":"publisher","first-page":"3959","DOI":"10.1016\/S0043-1354(01)00134-8","volume":"35","author":"D Choi","year":"2001","unstructured":"Choi, D., & Park, H. (2001). A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. Water Research, 35, 3959\u20133967. https:\/\/doi.org\/10.1016\/S0043-1354(01)00134-8","journal-title":"Water Research"},{"key":"1489_CR01","doi-asserted-by":"publisher","unstructured":"Chowdhary, P., Khan, N., & Bharagava, R. (2018). Distillery Wastewater: Its Impact on Environment and Remedies. Environmental\nAnalysis Ecology Studies, 1(2). https:\/\/doi.org\/10.31031\/EAES.2018.01.000507","DOI":"10.31031\/EAES.2018.01.000507"},{"key":"1489_CR9","doi-asserted-by":"publisher","DOI":"10.31031\/eaes.2018.01.000507","author":"P Chowdhary","year":"2018","unstructured":"Chowdhary, P., Khan, N., & Bharagava, R. (2018a). Distillery wastewater: It\u2019s impact on environment and remedies. Environmental Analysis and Ecology Studies. https:\/\/doi.org\/10.31031\/eaes.2018.01.000507","journal-title":"Environmental Analysis and Ecology Studies"},{"key":"1489_CR10","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.chemosphere.2017.11.163","volume":"194","author":"P Chowdhary","year":"2018","unstructured":"Chowdhary, P., Raj, A., & Bharagava, R. (2018b). Environmental pollution and health hazards from distillery wastewater and treatment approaches to combat the environmental threats: A review. Chemosphere, 194, 229\u2013246. https:\/\/doi.org\/10.1016\/j.chemosphere.2017.11.163","journal-title":"Chemosphere"},{"issue":"5","key":"1489_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jece.2022.108449","volume":"10","author":"T Da","year":"2022","unstructured":"Da, T., Ren, H., He, W., Gong, S., & Chen, T. (2022). Prediction of uranium adsorption capacity on biochar by machine learning methods. Journal of Environmental Chemical Engineering, 10(5), Article 108449. https:\/\/doi.org\/10.1016\/j.jece.2022.108449","journal-title":"Journal of Environmental Chemical Engineering"},{"key":"1489_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jece.2020.103952","author":"A de Miranda Ramos Soares","year":"2020","unstructured":"de Miranda Ramos Soares, A., Carvalho, F., Silva, C., Goncalves, A., & Abud, A. (2020). Random Forest as a promising application to predict basic-dye biosorption process using orange waste. Journal of Environmental Chemical Engineering. https:\/\/doi.org\/10.1016\/j.jece.2020.103952","journal-title":"Journal of Environmental Chemical Engineering"},{"issue":"10","key":"1489_CR13","doi-asserted-by":"publisher","first-page":"988","DOI":"10.2166\/washdev.2024.206","volume":"14","author":"R Dhumal","year":"2024","unstructured":"Dhumal, R., & Sadgir, P. (2024a). Adsorption dynamics of chloride removal with activated vetiver root powder: Dynamic modeling perspectives and ANN predictions. Journal of Water, Sanitation and Hygiene for Development, 14(10), 988\u20131002. https:\/\/doi.org\/10.2166\/washdev.2024.206","journal-title":"Journal of Water, Sanitation and Hygiene for Development"},{"issue":"4","key":"1489_CR14","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.2166\/ws.2024.049","volume":"24","author":"R Dhumal","year":"2024","unstructured":"Dhumal, R., & Sadgir, P. (2024b). Chloride ion sequestration by vetiver root biosorption: Isotherm, kinetic, and thermodynamic analyses and ANN prediction. Water Supply, 24(4), 1377\u20131392. https:\/\/doi.org\/10.2166\/ws.2024.049","journal-title":"Water Supply"},{"key":"1489_CR15","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-981-15-1884-3_18","volume":"1101","author":"A Dombe","year":"2020","unstructured":"Dombe, A., Rewale, R., & Swain, D. (2020). A deep learning-based approach for predicting the outcome of H-1B visa application. Advances in Intelligent Systems and Computing, 1101, 193\u2013202. https:\/\/doi.org\/10.1007\/978-981-15-1884-3_18","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"1489_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.119448","volume":"348","author":"E Ekinci","year":"2023","unstructured":"Ekinci, E., \u00d6zbay, B., Omurca, S., Sayin, F., & Ozbay, I. (2023). Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant. Journal of Environmental Management, 348, Article 119448. https:\/\/doi.org\/10.1016\/j.jenvman.2023.119448","journal-title":"Journal of Environmental Management"},{"issue":"19","key":"1489_CR17","doi-asserted-by":"publisher","first-page":"3487","DOI":"10.3390\/w15193487","volume":"15","author":"A Fels","year":"2023","unstructured":"Fels, A., Mandi, L., Kammoun, A., Ouazzani, N., Monga, O., & Hbid, M. (2023). Artificial intelligence and wastewater treatment: A global scientific perspective through text mining. Water, 15(19), 3487. https:\/\/doi.org\/10.3390\/w15193487","journal-title":"Water"},{"key":"1489_CR18","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1016\/j.jiec.2013.08.033","volume":"20","author":"M Ghaedi","year":"2014","unstructured":"Ghaedi, M., Ghaedi, A., Negintaji, E., Ansari, A., Vafaei, A., & Rajabi, M. (2014). Random forest model for removal of bromophenol blue using activated carbon obtained from Astragalus bisulcatus tree. Journal of Industrial and Engineering Chemistry, 20, 1793\u20131803. https:\/\/doi.org\/10.1016\/j.jiec.2013.08.033","journal-title":"Journal of Industrial and Engineering Chemistry"},{"key":"1489_CR19","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s13762-018-1786-8","volume":"16","author":"S Ghosh Ray","year":"2019","unstructured":"Ghosh Ray, S., & Ghangrekar, M. (2019). Comprehensive review on treatment of high-strength distillery wastewater in advanced physico-chemical and biological degradation pathways. International Journal of Environmental Science and Technology, 16, 527\u2013546. https:\/\/doi.org\/10.1007\/s13762-018-1786-8","journal-title":"International Journal of Environmental Science and Technology"},{"key":"1489_CR20","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s12355-020-00934-8","volume":"23","author":"C Gupta","year":"2021","unstructured":"Gupta, C., Sachan, A., & Kumar, R. (2021). Examination of microstructure of sugar cane bagasse ash and sugar cane bagasse ash blended cement mortar. Sugar Tech, 23, 651\u2013660. https:\/\/doi.org\/10.1007\/s12355-020-00934-8","journal-title":"Sugar Tech"},{"key":"1489_CR21","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3901608","author":"M Hamza","year":"2022","unstructured":"Hamza, M., Althobaiti, M., Al-Wesabi, F., Alabdan, R., Mahgoub, H., Hilal, A., Motwakel, A., & Duhayyim, M. (2022). Gaussian process regression and machine learning methods for carbon-based material adsorption. Adsorption Science & Technology. https:\/\/doi.org\/10.1155\/2022\/3901608","journal-title":"Adsorption Science & Technology"},{"key":"1489_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-024-00818-z","volume":"16","author":"J Han","year":"2024","unstructured":"Han, J., Kwon, Y., Choi, Y., & Kang, S. (2024). Improving chemical reaction yield prediction using pre-trained graph neural networks. Journal of Cheminformatics, 16, Article 25. https:\/\/doi.org\/10.1186\/s13321-024-00818-z","journal-title":"Journal of Cheminformatics"},{"key":"1489_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-024-00899-w","author":"C Hart","year":"2024","unstructured":"Hart, C., Preto, A., Chanana, S., Healey, D., Kind, T., & Domingo-Fern\u00e1ndez, D. (2024). Evaluating the generalizability of graph neural networks for predicting collision cross section. Journal of Cheminformatics. https:\/\/doi.org\/10.1186\/s13321-024-00899-w","journal-title":"Journal of Cheminformatics"},{"issue":"3","key":"1489_CR24","first-page":"327","volume":"13","author":"P Hosseini","year":"2023","unstructured":"Hosseini, P., Kaveh, A., & Naghian, A. (2023). The use of artificial neural networks and metaheuristic algorithms to optimize the compressive strength of concrete. International Journal of Optimization in Civil Engineering, 13(3), 327\u2013338.","journal-title":"International Journal of Optimization in Civil Engineering"},{"key":"1489_CR25","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/(SICI)1097-0207(19990920)46:2<297::AID-NME679>3.0.CO;2-C","volume":"46","author":"A Iranmanesh","year":"1999","unstructured":"Iranmanesh, A., & Kaveh, A. (1999). Structural optimization by gradient base neural networks. International Journal of Numerical Methods in Engineering, 46, 297\u2013311.","journal-title":"International Journal of Numerical Methods in Engineering"},{"key":"1489_CR26","unstructured":"Indian Standards (IS) (1993) IS 3025-44 (1993): Methods of sampling and test (physical and chemical) for water and wastewater, part 44: Biochemical oxygen demand (BOD)"},{"key":"1489_CR27","unstructured":"IS 3025-58 (2006) IS 3025-58 (2006): Methods of sampling and test (physical and chemical) for water and wastewater, Part 58: Chemical oxygen demand (COD)"},{"key":"1489_CR28","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1016\/j.biortech.2018.09.004","volume":"271","author":"S Ismail","year":"2019","unstructured":"Ismail, S., Elsamadony, M., Fujii, M., & Tawfik, A. (2019). Evaluation and optimization of anammox baffled reactor (AnBR) by artificial neural network modeling and economic analysis. Bioresource Technology, 271, 500\u2013506. https:\/\/doi.org\/10.1016\/j.biortech.2018.09.004","journal-title":"Bioresource Technology"},{"key":"1489_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/nano13081402","author":"U Ismail","year":"2023","unstructured":"Ismail, U., Onaizi, S., & Vohra, M. (2023). Aqueous Pb(II) removal using ZIF-60: Adsorption studies, response surface methodology and machine learning predictions. Nanomaterials. https:\/\/doi.org\/10.3390\/nano13081402","journal-title":"Nanomaterials"},{"key":"1489_CR30","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1109\/IWCMC48107.2020.9148431","volume":"2020","author":"D Jalal","year":"2020","unstructured":"Jalal, D., & Ezzedine, T. (2020). Decision tree and support vector machine for anomaly detection in water distribution networks. International Wireless Communications and Mobile Computing, 2020, 1320\u20131323. https:\/\/doi.org\/10.1109\/IWCMC48107.2020.9148431","journal-title":"International Wireless Communications and Mobile Computing"},{"key":"1489_CR31","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1134\/S0097807819010056","volume":"46","author":"F Kamyab-Talesh","year":"2019","unstructured":"Kamyab-Talesh, F., Mousavi, S., Khaledian, M., Yousefi-Falakdehi, O., & Norouzi-Masir, M. (2019). Prediction of water quality index by support vector machine: A case study in the Sefidrud Basin, Northern Iran. Water Resources, 46, 112\u2013116. https:\/\/doi.org\/10.1134\/S0097807819010056","journal-title":"Water Resources"},{"key":"1489_CR32","doi-asserted-by":"publisher","unstructured":"Kaveh, A. (2024). Structural optimization by gradient-based neural networks. In Applications of artificial neural networks and machine learning in civil engineering (Vol. 1168). Studies in Computational Intelligence. https:\/\/doi.org\/10.1007\/978-3-031-66051-1_7","DOI":"10.1007\/978-3-031-66051-1_7"},{"key":"1489_CR33","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.istruc.2023.03.178","volume":"52","author":"A Kaveh","year":"2023","unstructured":"Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256\u2013272. https:\/\/doi.org\/10.1016\/j.istruc.2023.03.178","journal-title":"Structures"},{"issue":"1","key":"1489_CR34","first-page":"15","volume":"34","author":"A Kaveh","year":"2010","unstructured":"Kaveh, A., & Malakoutirad, S. (2010). Hybrid genetic algorithm and particle swarm optimization for the force method-based simultaneous analysis and design. Iranian Journal of Science and Technology Transaction B-Engineering, 34(1), 15\u201334.","journal-title":"Iranian Journal of Science and Technology Transaction B-Engineering"},{"issue":"6","key":"1489_CR35","first-page":"751","volume":"16","author":"A Kaveh","year":"2015","unstructured":"Kaveh, A., & Soleimani, N. (2015). CBO and DPSO for optimum design of reinforced concrete cantilever retaining walls. Asian Journal of Civil Engineering, 16(6), 751\u2013774.","journal-title":"Asian Journal of Civil Engineering"},{"issue":"9","key":"1489_CR36","doi-asserted-by":"publisher","first-page":"412","DOI":"10.35629\/5252-0309412425","volume":"3","author":"P Khot","year":"2021","unstructured":"Khot, P., Deshmukh, S., Pujari, S., et al. (2021). Distillery wastewater: Characteristics and treatment methods. International Journal of Advances in Engineering and Management, 3(9), 412\u2013425. https:\/\/doi.org\/10.35629\/5252-0309412425","journal-title":"International Journal of Advances in Engineering and Management"},{"issue":"12","key":"1489_CR37","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.2166\/wst.2020.382","volume":"82","author":"Y Koh","year":"2020","unstructured":"Koh, Y., Chan, W., Tao, G., Icke, O., Es, D., Koning, M., Wuister, J., Ng, J., Phua, K., Koh, Y., Chan, W., & Tao, G. (2020). Performance improvement of wastewater treatment processes by application of machine learning. Water Science and Technology, 82(12), 2671\u20132680. https:\/\/doi.org\/10.2166\/wst.2020.382","journal-title":"Water Science and Technology"},{"key":"1489_CR38","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s11269-012-0225-8","volume":"27","author":"A Kumar","year":"2013","unstructured":"Kumar, A., Goyal, M., Ojha, C., Singh, R., Swamee, P., & Nema, R. (2013). Application of ANN, fuzzy logic and decision tree algorithms for the development of reservoir operating rules. Water Resources Management, 27, 911\u2013925. https:\/\/doi.org\/10.1007\/s11269-012-0225-8","journal-title":"Water Resources Management"},{"issue":"5","key":"1489_CR39","first-page":"621","volume":"16","author":"K Laknejadi","year":"2015","unstructured":"Laknejadi, K., & Kaveh, A. (2015). A swarm-based memetic evolutionary algorithm for multi-objective optimization of large structures. Asian Journal of Civil Engineering, 16(5), 621\u2013649.","journal-title":"Asian Journal of Civil Engineering"},{"issue":"12","key":"1489_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2196\/20767","volume":"5","author":"D Liu","year":"2021","unstructured":"Liu, D., Zheng, M., & Sepulveda, N. (2021). Using artificial neural network condensation to facilitate adaptation of machine learning in medical settings by reducing computational burden: Model design and evaluation study. JMIR Formative Research, 5(12), 1\u20139. https:\/\/doi.org\/10.2196\/20767","journal-title":"JMIR Formative Research"},{"key":"1489_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/w14091384","author":"M Lowe","year":"2022","unstructured":"Lowe, M., Qin, R., & Mao, X. (2022). A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water (Basel). https:\/\/doi.org\/10.3390\/w14091384","journal-title":"Water (Basel)"},{"key":"1489_CR42","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1080\/00986445.2013.809710","volume":"201","author":"P Manokaran","year":"2014","unstructured":"Manokaran, P., Saravanathamizhan, R., Basha, C., & Kannadasan, T. (2014). Feed-forward back-propagation neural network for the electro-oxidation of distillery effluent. Chemical Engineering Communications, 201, 1404\u20131416. https:\/\/doi.org\/10.1080\/00986445.2013.809710","journal-title":"Chemical Engineering Communications"},{"key":"1489_CR43","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1007\/s12010-020-03343-5","volume":"192","author":"W Mikucka","year":"2020","unstructured":"Mikucka, W., & Zieli\u0144ska, M. (2020). Distillery stillage: Characteristics, treatment, and valorization. Applied Biochemistry and Biotechnology, 192, 770\u2013793. https:\/\/doi.org\/10.1007\/s12010-020-03343-5","journal-title":"Applied Biochemistry and Biotechnology"},{"key":"1489_CR44","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.3906\/kim-1806-71","volume":"42","author":"S Mohtashami","year":"2018","unstructured":"Mohtashami, S., Kolur, N., Kaghazchi, T., & Soleimani, M. (2018). Optimization of sugarcane bagasse activation to achieve adsorbent with high affinity towards phenol. Turkish Journal of Chemistry, 42, 1720\u20131735. https:\/\/doi.org\/10.3906\/kim-1806-71","journal-title":"Turkish Journal of Chemistry"},{"issue":"10","key":"1489_CR45","doi-asserted-by":"publisher","first-page":"2734","DOI":"10.3390\/nano11102734","volume":"11","author":"S Moosavi","year":"2021","unstructured":"Moosavi, S., Manta, O., El-Badry, Y., Hussein, E., El-Bahy, Z., Fawzi, N., Urbonavicius, J., & Moosavi, S. (2021). A study on machine learning methods\u2019 application for dye adsorption prediction onto agricultural waste activated carbon. Nanomaterials, 11(10), 2734. https:\/\/doi.org\/10.3390\/nano11102734","journal-title":"Nanomaterials"},{"key":"1489_CR46","doi-asserted-by":"publisher","unstructured":"Mothilal, R., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. In FAT* 2020\u2014Proceedings of the 2020 conference fairness, accountability, transparancy (pp. 607\u2013617). https:\/\/doi.org\/10.1145\/3351095.3372850","DOI":"10.1145\/3351095.3372850"},{"key":"1489_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2021.132203","author":"X Nguyen","year":"2022","unstructured":"Nguyen, X., Ly, Q., Nguyen, T., Ngo, H., Hu, Y., & Zhang, Z. (2022). Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars. Chemosphere. https:\/\/doi.org\/10.1016\/j.chemosphere.2021.132203","journal-title":"Chemosphere"},{"issue":"1","key":"1489_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2166\/wqrj.2024.011","volume":"60","author":"C Nikhar","year":"2025","unstructured":"Nikhar, C., Vyas, G., Dalvi, R., & Bhoye, D. (2025). A critical review on applications of machine learning in wastewater treatment: Insights and implications for distillery wastewater. Water Quality Research Journal, 60(1), 1\u201318. https:\/\/doi.org\/10.2166\/wqrj.2024.011","journal-title":"Water Quality Research Journal"},{"key":"1489_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41101-023-00211-1","volume":"8","author":"C Nikhar","year":"2023","unstructured":"Nikhar, C., Vyas, G., Dalvi, R., & Dalvi, S. (2023). Unlocking the potential of adsorption in distillery wastewater treatment: A comprehensive review. Water Conservation Science and Engineering, 8, 1\u201316. https:\/\/doi.org\/10.1007\/s41101-023-00211-1","journal-title":"Water Conservation Science and Engineering"},{"key":"1489_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.seppur.2023.123712","volume":"315","author":"L Oviedo","year":"2023","unstructured":"Oviedo, L., Oviedo, V., Nora, L., & Silva, W. (2023). Adsorption of organic dyes onto nanozeolites: A machine learning study. Separation and Purification Technology, 315, Article 123712. https:\/\/doi.org\/10.1016\/j.seppur.2023.123712","journal-title":"Separation and Purification Technology"},{"key":"1489_CR51","doi-asserted-by":"publisher","first-page":"5521","DOI":"10.1007\/s13369-021-05497-8","volume":"47","author":"H Patel","year":"2022","unstructured":"Patel, H. (2022). Characterization and adsorptive treatment of distillery spent wash using bagasse fly ash. Arabian Journal for Science and Engineering, 47, 5521\u20135531. https:\/\/doi.org\/10.1007\/s13369-021-05497-8","journal-title":"Arabian Journal for Science and Engineering"},{"key":"1489_CR52","doi-asserted-by":"publisher","first-page":"4649","DOI":"10.1007\/s11696-021-01675-y","volume":"75","author":"S Rashidi","year":"2021","unstructured":"Rashidi, S., & Moghaddam, A. (2021). Investigation and optimization of anaerobic system for treatment of seafood processing wastewater. Chemical Papers, 75, 4649\u20134660. https:\/\/doi.org\/10.1007\/s11696-021-01675-y","journal-title":"Chemical Papers"},{"key":"1489_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2021.116995","author":"B Rathi","year":"2021","unstructured":"Rathi, B., & Kumar, P. (2021). Application of adsorption process for effective removal of emerging contaminants from water and wastewater. Environmental Pollution. https:\/\/doi.org\/10.1016\/j.envpol.2021.116995","journal-title":"Environmental Pollution"},{"key":"1489_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2021.112544","author":"S Ratna","year":"2021","unstructured":"Ratna, S., Rastogi, S., & Kumar, R. (2021). Current trends for distillery wastewater management and its emerging applications for sustainable environment. Journal of Environmental Management. https:\/\/doi.org\/10.1016\/j.jenvman.2021.112544","journal-title":"Journal of Environmental Management"},{"issue":"PA","key":"1489_CR55","doi-asserted-by":"publisher","first-page":"111443","DOI":"10.1016\/j.engappai.2025.111443","volume":"158","author":"S Rautmare","year":"2025","unstructured":"Rautmare, S., Dalvi, R., & Vyas, G. (2025a). A survey on potential of hybrid soft computing techniques using for assessing the geotechnical properties of soil. Engineering Applications of Artificial Intelligence, 158(PA), 111443. https:\/\/doi.org\/10.1016\/j.engappai.2025.111443","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"1489_CR56","doi-asserted-by":"publisher","DOI":"10.1007\/s41939-024-00717-8","author":"S Rautmare","year":"2025","unstructured":"Rautmare, S., Dalvi, R., & Vyas, G. (2025b). Artificial neural network modeling for mineralogical and strength analysis of clayey soils. Multiscale and Multidisciplinary Modeling, Experiments and Design. https:\/\/doi.org\/10.1007\/s41939-024-00717-8","journal-title":"Multiscale and Multidisciplinary Modeling, Experiments and Design"},{"key":"1489_CR57","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.scitotenv.2014.01.001","volume":"476\u2013477","author":"V Rodriguez-Galiano","year":"2014","unstructured":"Rodriguez-Galiano, V., Mendes, M., Garcia-Soldado, M., Chica-Olmo, M., & Ribeiro, L. (2014). Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of Total Environment, 476\u2013477, 189\u2013206. https:\/\/doi.org\/10.1016\/j.scitotenv.2014.01.001","journal-title":"Science of Total Environment"},{"issue":"3","key":"1489_CR58","first-page":"433","volume":"1","author":"F Rofooei","year":"2011","unstructured":"Rofooei, F., Kaveh, A., & Masteri Farahani, F. (2011). Estimating the vulnerability of concrete moment resisting frame structures using artificial neural networks. International Journal of Operational Research, 1(3), 433\u2013448.","journal-title":"International Journal of Operational Research"},{"key":"1489_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.101930","volume":"21","author":"M Saleem","year":"2024","unstructured":"Saleem, M., Harrou, F., & Sun, Y. (2024). Explainable machine learning methods for predicting water treatment plant features under varying weather conditions. Results in Engineering, 21, Article 101930. https:\/\/doi.org\/10.1016\/j.rineng.2024.101930","journal-title":"Results in Engineering"},{"key":"1489_CR60","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s11705-013-1336-3","volume":"7","author":"J Sargolzaei","year":"2013","unstructured":"Sargolzaei, J., & Moghaddam, A. (2013). Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system. Frontiers of Chemical Science and Engineering, 7, 357\u2013365. https:\/\/doi.org\/10.1007\/s11705-013-1336-3","journal-title":"Frontiers of Chemical Science and Engineering"},{"key":"1489_CR61","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.jenvman.2006.12.024","volume":"86","author":"Y Satyawali","year":"2008","unstructured":"Satyawali, Y., & Balakrishnan, M. (2008). Wastewater treatment in molasses-based alcohol distilleries for COD and color removal: A review. Journal of Environmental Management, 86, 481\u2013497. https:\/\/doi.org\/10.1016\/j.jenvman.2006.12.024","journal-title":"Journal of Environmental Management"},{"key":"1489_CR62","doi-asserted-by":"publisher","first-page":"3073","DOI":"10.1007\/s00231-017-2047-y","volume":"53","author":"M Savari","year":"2017","unstructured":"Savari, M., Moghaddam, A., Amiri, A., Shanbedi, M., & Ayub, M. (2017). Comprehensive heat transfer correlation for water\/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Heat and Mass Transfer, 53, 3073\u20133083. https:\/\/doi.org\/10.1007\/s00231-017-2047-y","journal-title":"Heat and Mass Transfer"},{"issue":"18","key":"1489_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/su151813380","volume":"15","author":"S Shao","year":"2023","unstructured":"Shao, S., Fu, D., Yang, T., Mu, H., Gao, Q., & Zhang, Y. (2023). Analysis of machine learning models for wastewater treatment plant sludge output prediction. Sustainability, 15(18), 1\u201317. https:\/\/doi.org\/10.3390\/su151813380","journal-title":"Sustainability"},{"key":"1489_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics10040214","volume":"10","author":"Y Shichkina","year":"2020","unstructured":"Shichkina, Y., Stanevich, E., & Irishina, Y. (2020). Assessment of the status of patients with Parkinson\u2019s disease using neural networks and mobile phone sensors. Diagnostics, 10, 1\u201313. https:\/\/doi.org\/10.3390\/diagnostics10040214","journal-title":"Diagnostics"},{"issue":"7","key":"1489_CR65","doi-asserted-by":"publisher","first-page":"4583","DOI":"10.1021\/acs.est.9b06287","volume":"54","author":"G Sigmund","year":"2020","unstructured":"Sigmund, G., Gharasoo, M., Huffer, T., & Hofmann, T. (2020). Deep learning neural network approach for predicting the sorption of ionizable and polar organic pollutants to a wide range of carbonaceous materials. Environmental Science & Technology, 54(7), 4583\u20134591. https:\/\/doi.org\/10.1021\/acs.est.9b06287","journal-title":"Environmental Science & Technology"},{"key":"1489_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2022.128486","volume":"369","author":"N Singh","year":"2023","unstructured":"Singh, N., Yadav, M., Singh, V., Padhiyar, H., Kumar, V., Bhatia, S., & Show, P. (2023). Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. Bioresource Technology, 369, Article 128486. https:\/\/doi.org\/10.1016\/j.biortech.2022.128486","journal-title":"Bioresource Technology"},{"issue":"11","key":"1489_CR67","doi-asserted-by":"publisher","first-page":"02810","DOI":"10.1016\/j.heliyon.2019.e02810","volume":"5","author":"J Solano-Meza","year":"2019","unstructured":"Solano-Meza, J., Orjuela-Yepes, D., Rodrigo-Ilarri, J., & Cassiraga, E. (2019). Predictive analysis of urban waste generation for the city of Bogot\u00e1, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 5(11), 02810. https:\/\/doi.org\/10.1016\/j.heliyon.2019.e02810","journal-title":"Heliyon"},{"key":"1489_CR68","doi-asserted-by":"publisher","DOI":"10.1007\/s44246-023-00061-5","volume":"2","author":"H Ullah","year":"2023","unstructured":"Ullah, H., Khan, S., Chen, B., Shahab, A., Riaz, L., Lun, L., & Wu, N. (2023). Machine learning approach to predict adsorption capacity of Fe-modified biochar for selenium. Carbon Research, 2, Article 29. https:\/\/doi.org\/10.1007\/s44246-023-00061-5","journal-title":"Carbon Research"},{"key":"1489_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2021.147138","volume":"784","author":"D Wang","year":"2021","unstructured":"Wang, D., Thun\u00e9ll, S., Lindberg, U., Jiang, L., Johan, T., Tysklind, M., & Souihi, N. (2021). A machine learning framework to improve effluent quality control in wastewater treatment plants. Science of Total Environment, 784, Article 147138. https:\/\/doi.org\/10.1016\/j.scitotenv.2021.147138","journal-title":"Science of Total Environment"},{"key":"1489_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2024.175411","volume":"951","author":"X Wu","year":"2024","unstructured":"Wu, X., Chen, M., Zhu, T., Chen, D., & Xiong, J. (2024). Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors. Science of Total Environment, 951, Article 175411. https:\/\/doi.org\/10.1016\/j.scitotenv.2024.175411","journal-title":"Science of Total Environment"},{"issue":"3","key":"1489_CR71","doi-asserted-by":"publisher","first-page":"447","DOI":"10.3390\/pr10030447","volume":"10","author":"M Zafar","year":"2022","unstructured":"Zafar, M., Aggarwal, A., Rene, E., Barbusinski, K., Mahanty, B., & Behera, S. (2022). Data-driven machine learning intelligent tools for predicting chromium removal in an adsorption system. Processes, 10(3), 447. https:\/\/doi.org\/10.3390\/pr10030447","journal-title":"Processes"},{"key":"1489_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2196\/18689","volume":"8","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Qu, Y., Jin, B., Jing, L., Gao, Z., & Liang, Z. (2020). An intelligent mobile-enabled system for diagnosing Parkinson disease: Development and validation of a speech impairment detection system. JMIR Medical Informatics, 8, 1\u201316. https:\/\/doi.org\/10.2196\/18689","journal-title":"JMIR Medical Informatics"},{"key":"1489_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2023.101487","volume":"49","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Omar, A., Hashim, A., Alam, T., Khalifa, H., & Elkotb, M. (2023). Enhancing waste management and prediction of water quality in the sustainable urban environment using optimized algorithm of least square support vector machine and deep learning techniques. Urban Climate, 49, Article 101487. https:\/\/doi.org\/10.1016\/j.uclim.2023.101487","journal-title":"Urban Climate"},{"key":"1489_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2021.127060","volume":"423","author":"X Zhu","year":"2022","unstructured":"Zhu, X., He, M., Sun, Y., Xu, Z., Wan, Z., Hou, D., Alessi, D., & Tsang, D. (2022). Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning. Journal of Hazardous Materials, 423, Article 127060. https:\/\/doi.org\/10.1016\/j.jhazmat.2021.127060","journal-title":"Journal of Hazardous Materials"},{"key":"1489_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2020.126782","volume":"406","author":"X Zhu","year":"2021","unstructured":"Zhu, X., Wan, Z., Tsang, D., He, M., Hou, D., Su, Z., & Shang, J. (2021). Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption. Chemical Engineering Journal, 406, Article 126782. https:\/\/doi.org\/10.1016\/j.cej.2020.126782","journal-title":"Chemical Engineering Journal"},{"key":"1489_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2019.06.004","volume":"378","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Wang, X., & Ok, Y. (2019). The application of machine learning methods for prediction of metal sorption onto biochars. Journal of Hazardous Materials, 378, Article 120727. https:\/\/doi.org\/10.1016\/j.jhazmat.2019.06.004","journal-title":"Journal of Hazardous Materials"}],"container-title":["Asian Journal of Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42107-025-01489-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42107-025-01489-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42107-025-01489-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T09:07:32Z","timestamp":1769418452000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42107-025-01489-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,5]]},"references-count":77,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["1489"],"URL":"https:\/\/doi.org\/10.1007\/s42107-025-01489-3","relation":{},"ISSN":["1563-0854","2522-011X"],"issn-type":[{"value":"1563-0854","type":"print"},{"value":"2522-011X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,5]]},"assertion":[{"value":"9 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve human participants, human material, or human data, so it does not need an ethical approval document\n                      .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}