An Auto-Adjustable and Time-Consistent Model for Determining Coagulant Dosage Based on Operators' Experience

This article examines how to automate the determination of the coagulant dosage for water treatment plants. Whilst most of the processes for water treatment are automated, determining the coagulant dosage, required for reducing turbidity, depends on well-trained and experienced operators. Based on a time-series data set provided by the Shanghai municipal investment water production company, this article comprehensively surveys existing coagulant prediction methods and utilizes an auto-adjustable and time-consistent model to incorporate the operators' experience. Compared to existing methods, the algorithm introduced in this article produced a better accuracy for predicting the coagulant dosage. Moreover, this article demonstrates that taking seasonal effects into account can approximate operator behavior more accurately. To examine the robustness of the identified models, this article examines the model performance based on water drawn from different locations/sources.

Automated summary

This study examines how to automate the determination of coagulant dosage for water treatment plants. By surveying existing coagulant prediction methods and utilizing an auto-adjustable and time-consistent model, the algorithm introduced in this article shows better accuracy for predicting coagulant dosage. Additionally, taking seasonal effects into account can approximate operator behavior more accurately.