AI in Water Treatment: AI is Now Being Used to Clean Water

Introduction: Water scarcity is a pressing global challenge affecting millions worldwide. With increasing populations and climate change exacerbating water shortages, the need for efficient and innovative water treatment solutions has never been greater. Traditional methods are struggling to meet demand, prompting a shift towards advanced technologies. One such breakthrough is the application of AI in water treatment, particularly in predicting ion concentrations during desalination processes. This advancement promises to transform water resource management by enhancing efficiency, precision, and scalability.

Understanding CDI and BDI Technologies

To appreciate the impact of AI in water treatment, it’s essential to understand the underlying technologies it enhances: Capacitive Deionization (CDI) and Battery Electrode Deionization (BDI).

• Capacitive Deionization (CDI): CDI is an electrochemical process that removes ions from water using porous carbon electrodes. When an electric potential is applied, ions are attracted to and held on the electrode surfaces, effectively purifying the water. CDI is energy-efficient and ideal for treating brackish water, offering flexibility over traditional methods.
• Battery Electrode Deionization (BDI): BDI operates similarly to CDI but uses battery-like electrodes capable of undergoing Faradaic reactions. This allows for the selective removal and storage of specific ions, such as sodium (Na⁺), by intercalating them into the electrode material. BDI typically demonstrates higher ion adsorption capacities and energy efficiencies compared to CDI.

Both CDI and BDI are gaining traction as decentralized water treatment solutions due to their adaptability and lower operational costs.

The Game-Changing AI Model

Researchers from the Korea Institute of Science and Technology (KIST) and Yeungnam University have developed an AI-driven model based on a random forest algorithm to enhance the performance of CDI and BDI systems.

Why Random Forest?

The random forest model is an ensemble learning method that constructs multiple decision trees and merges their outcomes to improve prediction accuracy. It was chosen over more complex AI models for several reasons:

• Accuracy: It reduces overfitting and variance, leading to more reliable predictions of ion concentrations.
• Efficiency: Requires significantly fewer computational resources—over 100 times less—than deep learning models, making it suitable for real-time applications.
• Interpretability: Offers easier interpretation of results, aiding in understanding the factors influencing ion concentration predictions.

Importance of Predicting Individual Ion Concentrations

Traditional water quality sensors rely on conductivity measurements, which provide only a rough estimate of water quality by indicating the total ionic content. However, they fail to distinguish between different ions, which is crucial because:

• Specific Ion Impact: Different ions affect water quality and treatment processes differently. For example, calcium ions (Ca²⁺) contribute to water hardness, leading to scaling in pipes.
• Optimized Treatment: Knowing the exact concentrations of specific ions allows for better control and optimization of CDI and BDI systems, enhancing efficiency.
• Regulatory Compliance: Accurate ion monitoring ensures adherence to water quality standards, safeguarding public health.

By directly predicting individual ion concentrations, the AI model provides a more precise assessment of water quality than traditional methods.

Desalination Metrics and Definitions

The AI model’s effectiveness was demonstrated through key desalination metrics:

• Sodium Adsorption Capacity (SAC): Measures the amount of sodium chloride (NaCl) adsorbed per gram of electrode material (mg-NaCl g⁻¹). Higher SAC values indicate more effective ion removal.
  • CDI SAC: 18 mg-NaCl g⁻¹
  • BDI SAC: 31 mg-NaCl g⁻¹
• Thermodynamic Energy Efficiency (TEE): Represents the efficiency of energy usage in the desalination process, expressed as a percentage. Higher TEE values denote greater energy efficiency.
  • CDI TEE: 2.79%
  • BDI TEE: 8.00%
• R² Values: The coefficient of determination (R²) indicates how well the predicted ion concentrations match actual measurements. Values closer to 1 signify higher prediction accuracy.
  • CDI R²: 0.86
  • BDI R²: 0.95

These metrics highlight the superior performance of the AI-enhanced BDI system over traditional CDI, particularly in ion adsorption capacity and energy efficiency.

Definitions:

• SAC (Sodium Adsorption Capacity): A measure of the amount of sodium chloride removed from water per gram of electrode material.
• TEE (Thermodynamic Energy Efficiency): A percentage that indicates how efficiently energy is used during the desalination process.
• R² (Coefficient of Determination): A statistical measure that explains the proportion of variance in the dependent variable predictable from the independent variable(s).

Ion Compositions of Feed Solutions

To test the AI model under various conditions, different salt solutions were prepared, simulating real-world water scenarios.

This table represents the concentrations of sodium chloride (NaCl), potassium chloride (KCl), and calcium chloride (CaCl₂) in the different feed solutions used for Capacitive Deionization (CDI) and Battery Electrode Deionization (BDI) operations. The compositions are as follows:

Concentration	C20	C15	C10	B20	B15	B10
NaCl (mM)	20	15	10	20	15	10
KCl (mM)	0	2.5	5	0	2.5	5
CaCl₂ (mM)	0	2.5	5	0	2.5	5

Explanation of Terms:

• NaCl (mM): Concentration of sodium chloride, providing sodium (Na⁺) and chloride (Cl⁻) ions.
• KCl (mM): Concentration of potassium chloride, supplying potassium (K⁺) and chloride (Cl⁻) ions.
• CaCl₂ (mM): Concentration of calcium chloride, yielding calcium (Ca²⁺) and chloride (Cl⁻) ions.

Solution Code:

• C20: Contains 20 mM NaCl and no other salts, making it a basic sodium solution.
• C15: Includes 15 mM NaCl along with 2.5 mM each of KCl and CaCl₂, introducing both potassium and calcium ions.
• C10: A more complex solution with 10 mM NaCl, 5 mM KCl, and 5 mM CaCl₂, with higher concentrations of potassium and calcium ions.
  
• B20: A BDI (Battery Electrode Deionization) experiment using a feed solution with 20 mM NaCl.
• B15: A BDI experiment using a feed solution with 15 mM NaCl, 2.5 mM KCl, and 2.5 mM CaCl₂.
• B10: A BDI experiment using a feed solution with 10 mM NaCl, 5 mM KCl, and 5 mM CaCl₂.

By varying these concentrations, the researchers assessed the AI model’s ability to predict ion concentrations in diverse conditions.

Optimal Sampling Intervals

The AI model’s accuracy depends on the frequency of data sampling. The researchers found that:

• Optimal Interval: Sampling intervals under 80 seconds maintain high prediction accuracy.
• Impact of Longer Intervals: Intervals beyond 80 seconds result in a decline in accuracy, emphasizing the need for frequent updates.

Frequent sampling ensures the model has sufficient data to accurately predict ion concentrations, which is crucial for effective water treatment operations.

Why Intervals Under 80 Seconds?

• Real-Time Monitoring: Shorter intervals allow for near real-time adjustments in the treatment process.
• Data Sufficiency: More frequent data points improve the model’s predictive capabilities by providing more information.
• Operational Efficiency: Helps in maintaining consistent water quality and prevents potential system inefficiencies.

Why AI in water treatment?

Implementing AI in water treatment offers several significant advantages:

• Enhanced Precision: Directly predicts individual ion concentrations, leading to a more accurate assessment of water purity.
• Improved Efficiency: The random forest model’s computational efficiency allows for widespread use without the need for extensive resources.
• Scalability: Can be integrated into large-scale, decentralized water treatment systems, providing frequent updates and maintaining high prediction accuracy.

By addressing the limitations of traditional conductivity measurements, the AI-driven model ensures better water quality management and resource optimization.

Conclusion

The development of this revolutionary AI model marks a significant milestone in water treatment technology. It not only improves the precision of ion concentration monitoring but also enhances the efficiency and scalability of water management systems. With AI in water treatment, we are one step closer to solving the global water crisis.

Source: https://www.sciencedirect.com/science/article/pii/S0043135424009928