Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant
Optimizing coagulant dosages in Drinking Water Treatment Plants (DWTPs) is critical for reducing operational costs, minimizing chemical waste, mitigating environmental impacts, and ensuring consistent water quality, particularly in resource-constrained settings where conventional jar tests are labor...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2025
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| _version_ | 1881508904034107392 |
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| author | Gonzales Medina, Ronny Ivan Carlos Mendoza, Juan Adriel Zuñiga Goyzueta, Eduardo José Morán-Silva, Rosa María López Gonzales, Javier Linkolk |
| author_browse | Carlos Mendoza, Juan Adriel Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk Morán-Silva, Rosa María Zuñiga Goyzueta, Eduardo José |
| author_facet | Gonzales Medina, Ronny Ivan Carlos Mendoza, Juan Adriel Zuñiga Goyzueta, Eduardo José Morán-Silva, Rosa María López Gonzales, Javier Linkolk |
| author_sort | Gonzales Medina, Ronny Ivan |
| collection | Producción científica |
| description | Optimizing coagulant dosages in Drinking Water Treatment Plants (DWTPs) is critical for reducing operational costs, minimizing chemical waste, mitigating environmental impacts, and ensuring consistent water quality, particularly in resource-constrained settings where conventional jar tests are labor-intensive and poorly suited to real-time demands. This study develops and validates a Random Forest (RF) machine learning model to predict optimal dosages of aluminum sulfate, polyaluminum chloride, and a polymer flocculant at the Miguel de la Cuba Ibarra DWTP in Peru, addressing the need for an efficient, real-time decision support system. Using a historical dataset of 2556 jar tests, a univariate RF model was developed to predict settled water turbidity, tailored to the plant’s typical operational range. The model demonstrated robust predictive performance, achieving a coefficient of determination (R2) of 0.92 during training and 0.76 during validation with unseen data, alongside a Root Mean Square Error (RMSE) of 0.11 NTU and a Mean Absolute Percentage Error (MAPE) of 0.11 in the training phase. Integrated into a digital platform, the model generates real-time NTU ppm dosing curves, providing a practical and responsive tool to enhance operational efficiency for DWTP operators. This work offers a scalable, data-driven solution to improve water treatment processes in resource-limited contexts. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/397 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3972026-11-02T22:00:04ZRandom Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra PlantGonzales Medina, Ronny IvanCarlos Mendoza, Juan AdrielZuñiga Goyzueta, Eduardo JoséMorán-Silva, Rosa MaríaLópez Gonzales, Javier LinkolkOptimizing coagulant dosages in Drinking Water Treatment Plants (DWTPs) is critical for reducing operational costs, minimizing chemical waste, mitigating environmental impacts, and ensuring consistent water quality, particularly in resource-constrained settings where conventional jar tests are labor-intensive and poorly suited to real-time demands. This study develops and validates a Random Forest (RF) machine learning model to predict optimal dosages of aluminum sulfate, polyaluminum chloride, and a polymer flocculant at the Miguel de la Cuba Ibarra DWTP in Peru, addressing the need for an efficient, real-time decision support system. Using a historical dataset of 2556 jar tests, a univariate RF model was developed to predict settled water turbidity, tailored to the plant’s typical operational range. The model demonstrated robust predictive performance, achieving a coefficient of determination (R2) of 0.92 during training and 0.76 during validation with unseen data, alongside a Root Mean Square Error (RMSE) of 0.11 NTU and a Mean Absolute Percentage Error (MAPE) of 0.11 in the training phase. Integrated into a digital platform, the model generates real-time NTU ppm dosing curves, providing a practical and responsive tool to enhance operational efficiency for DWTP operators. This work offers a scalable, data-driven solution to improve water treatment processes in resource-limited contexts.Articlehttps://doi.org/10.3390/environments1301001710.3390/environments13010017https://hdl.handle.net/20.500.15037/397W7117748751enEnvironments2076-32982025-12-302026-06-13T08:12:40Z2026-06-13T08:12:40Z |
| spellingShingle | Gonzales Medina, Ronny Ivan Carlos Mendoza, Juan Adriel Zuñiga Goyzueta, Eduardo José Morán-Silva, Rosa María López Gonzales, Javier Linkolk Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant |
| title | Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant |
| title_full | Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant |
| title_fullStr | Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant |
| title_full_unstemmed | Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant |
| title_short | Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant |
| title_sort | random forest model for optimizing coagulant doses in drinking water treatment application at the miguel de la cuba ibarra plant |
| url | https://doi.org/10.3390/environments13010017 https://hdl.handle.net/20.500.15037/397 |
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