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The precise coagulation add-in in the wastewater process treatment is key for efficient contamination removal. However, the complexity of the coagulant chemical theory and affected by many factors (turbidity, pH, conductivity, flow rate, etc.) that it is difficult to determine the optimal dosage. The traditional method in the production process, such as PID controller had a bad adaptability on the complex systems and high performance required systems due to its inefficient parameter coordination, and it has a large time delay, difficult to achieve precise control. Excessive dosage will lead to waste and cost-waste, insufficient dosage could not guarantee the quality of effluent water. In this research study, we proposed an intelligent precisely dosing prediction algorithm based on LightGBM, using the characteristics of the influent water quality parameters PH, turbidity, electrical conductivity and flow rate to predict the dosage of coagulant. Perform experiments based on the actual data collected from the sewage treatment plant. Compared to experimental results with the optimal dosage solution, it demonstrated that the proposed approach could predict the dosage more accurate, resulting in intelligent and precise dosing add-in in water treatment process.

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Proper chemical demand prediction is important for water management and the environment. The study aimed to select and apply proper data-driven models based on real-world big data for dosage prediction...