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Prediction of forward osmosis membrane engineering factors using artificial intelligence approach
Sung Ju Im1, Viet Duc Nguyen2, Am Jang2
1Department of Global Smart City, Sungkyunkwan University (SKKU), 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea; Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, United States.
Journal of Environmental Management
|June 24, 2022
Summary
Artificial intelligence (AI) enhances forward osmosis (FO) for wastewater treatment. AI models accurately predict water flux, membrane fouling, and contaminant removal, improving FO system performance and enabling sustainable water reuse.
Area of Science:
- Environmental Engineering
- Membrane Science
- Artificial Intelligence
Background:
- Forward osmosis (FO) is a promising technology for wastewater treatment and reuse.
- Commercial application of FO faces challenges, including system performance and control.
- Artificial intelligence (AI) offers capabilities to address complex nonlinear relationships in FO systems.
Purpose of the Study:
- To develop an AI-based model for early control and decision-making in FO membrane systems.
- To improve the performance and applicability of FO for wastewater treatment.
- To identify key variables influencing FO system performance.
Main Methods:
- Development of an artificial neural networks (ANN) model.
- Utilizing AI to analyze relationships between multiple variables.
- Dataset selection focusing on organic matter, sodium, and calcium ion concentrations.
Main Results:
- ANN models demonstrated high accuracy in predicting water flux (R²=0.92), membrane fouling (R² up to 0.98), and removal efficiencies (R²=0.87).
- Optimal model architecture suggested 2-4 hidden layers with 10-15 neurons.
- Key input variables identified include organic matter, sodium, and calcium ion concentrations.
Conclusions:
- AI, specifically ANNs, is highly suitable for predicting FO performance metrics.
- The developed AI models can significantly enhance control and decision-making in FO wastewater treatment.
- This research provides actionable insights for developing sustainable, future-ready FO systems.
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