The drinking water treatment plant plays a key role in providing the consumers with a safe water, in compliance with the United Nations Sustainable Development Goal n. 6 (Clean water and sanitation). The typical drinking water treatment plant includes: coagulation–flocculation, sedimentation, filtration and disinfection. Turbidity removal is among the more common pollutants to remove; this goal is mainly achieved by the coagulation–flocculation process. The present paper shows the results of the application of a combined experimental-modeling approach for turbidity removal optimization in a coagulation–flocculation unit of a full-scale drinking water treatment plant. The applied approach consisted of a laboratory experimental activity aimed at determining the best coagulant type and dosage. Among the different chemicals tested, Poly aluminum chloride (PAC) provided the highest removal at the lowest dosage (90% at 3.5 mg/L PAC, 88% at 18.9 mg/L PACS and 77% at 30 mg/L FeCl 3). The addition of polyelectrolytes did not improve the removal at such a level to justify the consequent increase of the costs. Based on the findings of this phase of the study, a data-driven model was implemented using as input variables the historical data of influent and effluent turbidity and influent flow rate. Combining regression models and statistical analysis, it was possible to build up an algorithm for the case-study plant allowing to select the PAC dosage to apply as a function of the influent turbidity, to ensure the constant compliance with the regulation limit on effluent turbidity.
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