Non Linear Modeling of the Relationship Between Raw Water Turbidity, Accelerator Unit Turbidity, and Filtration Turbidity Using a Quadratic Interaction Model in a Water Treatment Plant
DOI:
https://doi.org/10.62671/jowim.v3i1.237Keywords:
Quadratic Interaction Model, filtration, turbidity, non-linear modelling, Gunung Pangilun WTPAbstract
This study aims to analyze the non-linear relationship between raw water turbidity, turbidity after the accelerator unit, and turbidity after the filtration unit at the Gunung Pangilun Water Treatment Plant (WTP) in Padang City using the Quadratic Interaction Model (QIM) approach. Turbidity data were collected from 1–5 June 2025 at one-hour intervals, resulting in 50 data points for each sampling location. The data processing stages included missing value inspection, outlier detection using the Interquartile Range (IQR) method, data consistency checking, relationship visualization among variables, and QIM-based modeling using Python. The results show that the QIM was unable to adequately represent the relationships among the variables, as indicated by an R-squared value of 0.145 and an adjusted R-squared of 0.048. All model parameters exhibited p-values greater than 0.05, indicating no statistically significant influence on turbidity after filtration. Model evaluation yielded an RMSE of 0.496 NTU and a MAPE of 19.34%, suggesting a moderate level of prediction accuracy. Additionally, the analysis identified 14% outliers and 9 inconsistent data points for each sampling location.
Downloads
References
[1] F. K. K. A, A. D. K. B, and C. A. P. b , V.G. Papadakis a, “Revisiting of coagulation-flocculation processes in the production of potable water,” J. Water Process Eng., vol. 27, no. February 2019, pp. 193–204, 2019, doi: https://doi.org/10.1016/j.jwpe.2018.12.007.
[2] K. S. Nemani, S. Peldszus, and P. M. Huck, “Practical Framework for Evaluation and Improvement of Drinking Water Treatment Robustness in Preparation for Extreme-Weather-Related Adverse Water Quality Events,” ACS ES T Water, vol. 3, no. 5, pp. 1305–1313, 2023, doi: 10.1021/acsestwater.2c00627.
[3] A. Chiavola, C. Di Marcantonio, M. D’Agostini, S. Leoni, and M. Lazzazzara, “A combined experimental-modeling approach for turbidity removal optimization in a coagulation–flocculation unit of a drinking water treatment plant,” J. Process Control, vol. 130, 2023, doi: 10.1016/j.jprocont.2023.103068.
[4] S. Safeer et al., “A review of artificial intelligence in water purification and wastewater treatment: Recent advancements,” J. Water Process Eng., vol. 49, no. October 2022, p. 102974, 2022, doi: https://doi.org/10.1016/j.jwpe.2022.102974.
[5] O. Bello, Y. Hamam, and K. Djouani, “Coagulation process control in water treatment plants using multiple model predictive control,” Alexandria Eng. J., vol. 53, no. 4, pp. 939–948, 2014, doi: 10.1016/j.aej.2014.08.002.
[6] C. W. K. Khedher, M., Awad, J., Donner, E., Drigo, B., Fabris, R., Harris, M., Braun, K., & Chow, “Using the Flocculation Index to optimise coagulant dosing during drinking water treatment,” J. Water Process Eng., vol. 51, no. February 2023, 103394, 2023, doi: https://doi.org/10.1016/j.jwpe.2022.103394.
[7] K. Saxena, U. Brighu, and A. Choudhary, “Experimental investigation and modelling the effect of humic acid on coagulation efficiency for sludge blanket clarifier,” Chemosphere, vol. 266, no. March 2021, p. 128958, 2021, doi: https://doi.org/10.1016/j.chemosphere.2020.128958.
[8] Z. Gao, Z. Ding, J. Liu, and W. Liu, “Experimental study on the influencing factors of permeability of sand media,” Hydrogeol. Eng. Geol., vol. 51, no. 6, pp. 8–17, 2024, doi: 10.16030/j.cnki.issn.1000-3665.202404025.
[9] J. An et al., “Machine Learning-Based Prediction of Coagulant Dosing in Drinking Water Treatment Plants Using Polynomial Regression with Lasso Regularization,” pp. 1–17, 2025.
[10] M. Yateh, G. Lartey-Young, F. Li, M. Li, and Y. Tang, “Application of Response Surface Methodology to Optimize Coagulation Treatment Process of Urban Drinking Water Using Polyaluminium Chloride,” Water (Switzerland), vol. 15, no. 5, pp. 1–13, 2023, doi: 10.3390/w15050853.
[11] X. Ji, Z. Li, M. Wang, Z. Yuan, and L. Jin, “Response Surface Methodology Approach to Optimize Parameters for Coagulation Process Using Polyaluminum Chloride (PAC),” Water (Switzerland), vol. 16, no. 11, 2024, doi: 10.3390/w16111470.
[12] S. Moradi et al., “Forecasting and Optimizing Dual Media Filter Performance via Machine Learning,” Water Res., vol. 235, no. December 2022, p. 119874, 2023, doi: 10.1016/j.watres.2023.119874.
[13] A. A. K. Dhrubo, M. Jannat, and M. S. Hossain, “Enhancing the performance of coagulants for wastewater treatment by varying and optimizing the experimental parameters,” J. Water Process Eng., vol. 55, no. October 2023,104144, 2023, doi: https://doi.org/10.1016/j.jwpe.2023.104144.
[14] D. R. Roderick Little, Statistical Analysis with Missing Data, Third Edition. WILEY, 2019. doi: 10.1002/9781119482260.
[15] I. F. Ilyas and X. Chu, Data Cleaning. ACM Books, 2019.
[16] I. Raza et al., “Geospatial interpolation and hydrogeochemical characterization of alluvial aquifers in the Thal Desert, Punjab, Pakistan,” PLoS One, vol. 19, no. 8, 2024, doi: 10.1371/journal.pone.0307025.
[17] R. P. Duncan and B. J. Kefford, “Interactions in statistical models: Three things to know,” Methods Ecol. Evol., vol. 12, no. 12, pp. 2287–2297, 2021, doi: 10.1111/2041-210X.13714.
[18] H. Khoshvaght, R. R. Permala, A. Razmjou, and M. Khiadani, “A critical review on selecting performance evaluation metrics for supervised machine learning models in wastewater quality prediction,” J. Environ. Chem. Eng., vol. 13, no. 6, p. 119675, 2025, doi: 10.1016/j.jece.2025.119675.
[19] S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” Int. J. Forecast., vol. 32, no. 3, pp. 669–679, 2016, doi: 10.1016/j.ijforecast.2015.12.003.
[20] A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean Absolute Percentage Error for regression models,” Neurocomputing, vol. 192, no. 5 June 2016, pp. 38–48, 2016, doi: https://doi.org/10.1016/j.neucom.2015.12.114.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nelvidawati, Firdaus, M Andre Kurnia Festa (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
