Integrated assessment of water quality and sludge characteristics at old kufa water treatment plant using artificial neural network modeling
DOI:
https://doi.org/10.48612/dnitii/2025_57_59-75Keywords:
euphrates River, turbidity, drinking water treatment, artificial neural networks, TSS, COD, Al-Najaf, old Kufa Water Treatment Plant, water treatment residualsAbstract
This study delivers an integrated assessment of water quality and residual sludge characteristics at the Old Kufa drinking-water facility in Iraq. The analysis targets the full treatment train of the municipal plant together with the generated water-treatment residuals. Operational records from 2020–2024 and laboratory measurements collected between June and November 2024 were used to trace the behavior of key indicators—turbidity, total suspended solids, and chemical oxygen demand. We further examine how fluctuations in Euphrates source-water quality and coagulation–flocculation settings condition the specific sludge yield. Measured values are benchmarked against current Iraqi drinking-water and environmental standards, and the operational implications for sustainable sludge management are outlined, including exceedance risk appraisal, reagent-dose optimization planning, and avenues for reuse and resource recovery. The modeling component employs an artificial neural network with inputs pH, Cl⁻, NO₃⁻, NH₄⁺, and temperature, and targets defined as suspended solids and specific sludge production; the model attains high predictive accuracy (R² = 0.991). The novelty lies in coupling field and experimental evidence from an urban WTP with ANN-based forecasting of treated-water indicators and sludge generation under variable river inflow. The outcome is a practical decision-support tool enabling sensitivity analysis to influent attributes and dosing strategies. Turbidity and suspended solids emerge as the dominant determinants of sludge formation; a peak specific yield of 278.6 kg /1,000 m³ was observed, motivating adjustments to chemical conditioning and proactive sludge-handling plans. COD exceedances above the 100 mg/L limit indicate environmental risk and the need to reinforce operational resilience through optimized dosing and development of reuse/recovery pathways.
References
[1] World Health Organization, Guidelines for drinking-water quality: first addendum to the fourth edition. 2017.
[2] T. Ahmad, K. Ahmad, and M. Alam, “Characterization of Water Treatment Plant’s Sludge and its Safe Disposal Options,” Procedia Environ Sci, vol. 35, pp. 950–955, 2016, doi: 10.1016/j.proenv.2016.07.088.
[3] M. D. Nguyen, M. Thomas, A. Surapaneni, E. M. Moon, and N. A. Milne, “Beneficial reuse of water treatment sludge in the context of circular economy,” Nov. 01, 2022, Elsevier B.V. doi: 10.1016/j.eti.2022.102651.
[4] Nelson Belzile and Yu-Wei Chen, “Re-utilization of drinking water treatment residuals (DWTR): a review focused on the adsorption of inorganic and organic contaminants in wastewater and soil,” Environ Sci (Camb), no. 5, pp. 997–1296, May 2024.
[5] L. Pérez, I. Escudero, A. G. Cabado, B. Molinero-Abad, and M. J. Arcos-Martínez, “Study of ceramic membrane behavior for okadaic acid and heavy-metal determination in filtered seawater,” J Environ Manage, vol. 232, pp. 564–573, Feb. 2019, doi: 10.1016/j.jenvman.2018.11.077.
[6] M.S.E. Abdo, K.T. Ewida, and Y. M. Youssef, “Recovery of alum from wasted sludge produced from water treatment plants,” Journal of Environmental Science and Health , vol. 28, no. 6, pp. 1205–1216, 2008.
[7] find out more. P.-L. P. find out more. P. C. and constructive utilization of sludge produced in clari-flocculation unit of water treatment plant T. A. Materials Research Express Inclusive Publishing Trusted Science, Kafeel Ahmad, and Mehtab Alam, “Characterization and constructive utilization of sludge produced in clari-flocculation unit of water treatment plant,” IPO SCIENCE, vol. 5, no. 3, Mar. 2018.
[8] D. Fytili and A. Zabaniotou, “Utilization of sewage sludge in EU application of old and new methods—A review,” Renewable and Sustainable Energy Reviews, pp. 116–140, Aug. 2008.
[9] M. K. Karnena and V. Saritha, “Contemplations and investigations on green coagulants in treatment of surface water: a critical review,” Jul. 01, 2022, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s13201-022-01670-y.
[10] R. Q. Syed and M. M. Edward, Wastewater treatment plants: Planning, Design and Operation. New York, 1999.
[11] Pumipat K. Pachana, Ubolluk Rattanasak, Kamchai Nuithitikul, Peerapong Jitsangiam, and Prinya Chindaprasirt, “Sustainable utilization of water treatment residue as a porous geopolymer for iron and manganese removals from groundwater,” J Environ Manage, vol. 302, Jan. 2022.
[12] Robeam S. Melaku and Daba S. Gedafa, “Impact of Wastewater Treatment Sludge on Cracking Resistance of Hot Mix Asphalt Mixes at Lower Mixing Temperature,” Journal of Materials in Civil Engineering, vol. 32, no. 12, Dec. 2020.
[13] A. S. Kote and D. V Wadkar, “Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks,” 2019. [Online]. Available: www.etasr.com
[14] A. B. Sengul and Z. Gormez, “Prediction of Optimal Coagulant Dosage in Drinking Water Treatment by Artificial Neural Network,” in Improving Efficiency of Water Systems in a Changing natural and financial Environment, Kocaeli, Turkey , Apr. 2013, pp. 11–13.
[15] M. Solaimany, ----Aminabad Aminabad, A. Aminabad, A. Maleki, and M. Hadi, “Application of artificial neural network (ANN) for the prediction of water treatment plant influent characteristics Introduction 1,” 2013. [Online]. Available: http://jaehr.muk.ac.ir
[16] American Public Health Association and American Water Works Association; Water Environment, Standard Methods for the Examination of Water and Wastewater, 22nd ed. Washington, D.C: APHA/AWWA/WEF, 2012.
[17] IBM Software Group, IBM SPSS Neural Networks 20, Guide. Armonk, NY, USA: IBM Corporation, 2011.
[18] M. Serajuddin, M. A. I. Chowdhury, M. M. Haque, and M. E. Haque, “Using Turbidity to Determine Total Suspended Solids in an Urban Stream: A Case Study,” International Journal of Engineering Trends and Technology, vol. 67, no. 9, pp. 83–88, Sep. 2019, doi: 10.14445/22315381/IJETT-V67I9P214.
[19] Z. H. Abed and K. M. Khudair, “MODELING OF TURBIDITY DISTRIBUTION IN WATER NETWORKS USING PMS MODEL-AL-SARAY SECTOR IN KUFA CITY AS A CASE STUDY,” Kufa Journal of Engineering, vol. 14, no. 3, pp. 48–68, Jul. 2023, doi: 10.30572/2018/KJE/140304.
[20] J. C. Crittenden, R. R. Trussell, D. W. Hand, K. J. Howe, and G. Tchobanoglous, MWH’s Water Treatment: Principles and Design, 3rd ed. Oboken, New Jersey: John Wiley & Sons Inc, 2012.
[21] G. Farooq Jumaah, “Impact of saline wastewater on the behaviour of aerobic granular sludge (AGS),” Dijlah Journal of Engineering Science (DJES), vol. 2, no. 3, pp. 248–257, 2025.
[22] Yu-chen Wu and Jun-wen Feng, “Development and Application of Artificial Neural Network,” Wireless Personal Communications, vol. 102, pp. 1645–1656, Dec. 2017.
[23] R. Mohammed and B. Al-Obaidi, “Treatability influence of municipal sewage effluent on surface water quality assessment based on Nemerow pollution index using an artificial neural network,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Nov. 2021. doi: 10.1088/1755-1315/877/1/012008.
