Data-Driven Optimization of Coagulant Dosing and Cost Control in a Full-Scale Drinking Water Treatment Plant: A Case Study in Xiangtan, China

Water treatment plants are essential urban infrastructure with direct implications for public health and everyday life. Data-driven management has received growing attention in drinking water treatment, particularly for optimizing chemical dosing to improve operational efficiency, reduce costs, and ease operator workload. AI-based prediction of coagulant dosage has therefore become an active research topic. Existing studies, however, have focused mainly on model architecture, with less attention to data validity and cost control. In practice, many plants face data-quality problems, including inconsistent dosing records under similar water-quality conditions. Conventional data cleaning may also remove large portions of the dataset, which can weaken model reliability. This study proposes an artificial intelligence (AI) modeling framework for coagulation dosing that handles anomalous data, emphasizes data quality assurance, and combines cost-oriented feedforward prediction with feedback control. A genetic algorithm-optimized backpropagation (GA-BP) neural network was first evaluated on controlled laboratory data and full-scale plant data using the same core model architecture, allowing the effects of model configuration to be separated from those of data quality. Historical plant records were subsequently cleaned through expert-guided validation, approximate time-delay alignment, and turbidity-based classification of operating conditions. Settled-water turbidity was then used as a feedback signal to dynamically adjust subsequent coagulant dosage and assess the resulting chemical savings. Changes in the input structure produced only modest improvements in full-scale prediction performance (R2 = 0.53–0.72). In contrast, data cleaning and process-based data organization markedly improved predictive performance, with R2 values increasing to 0.927–0.969. Standalone AI models achieved only moderate dosage reductions, while their integration with real-time turbidity feedback provided the best cost-control performance. The model-based control strategy reduced average coagulant consumption by 10.37%, with a maximum reduction of 21.33% at a settled-water turbidity target of 1.9 nephelometric turbidity units (NTU). Across the evaluated feedback-control scenarios, manual dosing was up to 32.83% higher than the corresponding feedback-controlled dosage. Overall, AI models can fit coagulation-dosing data and predict coagulant dosage with sufficient accuracy, but data quality assurance remains the main factor determining model performance. Effective cost control also requires real-time turbidity-based feedback regulation rather than model outputs alone.