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Coagulation

Coagulation

Colloidal solids are solid particles suspended in solution. They are usually negatively charged, attracting a compact primary layer of positively charged ions, which attract more counterions to form an electrical double layer. Electrostatic repulsion between the charged double layers prevents the particles from colliding, stabilizing the colloids. These solids are often undesirable because they can contain toxins that are difficult to remove. Coagulation is a technique that helps aggregate and...

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Optimizing coagulant dosage using deep learning models with large-scale data.

Jiwoong Kim¹, Chuanbo Hua², Kyoungpil Kim³

¹Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Korea Water Resources Corporation (k-water), 200 Sintanjin-ro, Daedeok-gu, Deajeon, 34350, South Korea.

|December 22, 2023

Summary

This study uses deep learning to optimize coagulant dosage in water treatment, achieving significant cost savings and reducing chemical use. The advanced model enhances efficiency and automation in drinking water processes.

Keywords:

Coagulant dosage Convolutional neural network Deep learning model Gated recurrent unit Optimization

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Area of Science:

Environmental Engineering
Artificial Intelligence in Water Treatment
Process Automation

Background:

Water treatment plants require advanced technologies for automation due to operational challenges.
Optimizing coagulant dosage is crucial for efficient and cost-effective water purification.
Existing methods lack the precision for real-time, data-driven dosage adjustments.

Purpose of the Study:

To develop and validate a deep learning model for optimizing coagulant dosage in drinking water treatment.
To leverage a comprehensive five-year dataset for advanced time-series modeling.
To demonstrate the model's capability in reducing coagulant usage and improving process efficiency.

Main Methods:

Utilized a deep learning model combining one-dimensional convolutional neural network (Conv1D) and gated recurrent unit (GRU).
Trained the model on five years of minute-by-minute real-time water quality data.
Validated predictions against a physicochemical model and applied optimization strategies based on turbidity guidelines.

Main Results:

The deep learning model accurately predicted coagulant dosage and sedimentation basin turbidity.
Optimized coagulant dosage strategies led to significant reductions in chemical usage (approx. 22%).
Achieved substantial cost savings (approx. 21 million KRW/year) while maintaining water quality standards.

Conclusions:

Deep learning models offer a powerful tool for optimizing water treatment processes.
The proposed Conv1D-GRU model enhances efficiency, cost-effectiveness, and facilitates automation in water treatment.
This approach represents a significant advancement in applying AI to drinking water process management.