A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants.

Area of Science:

• Environmental Engineering
• Chemical Engineering
• Artificial Intelligence

Background:

• Growing populations and dwindling water resources increase stress on water supply systems.
• Desalination offers a solution by generating freshwater from saline sources.
• Reverse Osmosis (RO) is a leading desalination technology, but its complex systems require computationally intensive optimization.

Purpose of the Study:

• To develop a computationally efficient modeling and optimization strategy for industrial-scale RO plants.
• To accurately capture membrane behavior using artificial neural networks (ANNs).
• To minimize energy consumption while maximizing water utilization in RO systems.

Main Methods:

• Utilized a feed-forward artificial neural network (ANN) with rectified linear units for surrogate modeling.
• Trained and evaluated multiple ANN configurations using data from the H2Oaks RO plant.
• Transformed the ANN model into a mixed-integer linear programming (MILP) formulation.

Main Results:

• Developed an accurate ANN surrogate model for RO membrane behavior.
• Successfully formulated an optimization strategy to balance energy consumption and water recovery.
• Visualized trade-offs between objectives using a Pareto front, revealing potential cost savings.

Conclusions:

• The proposed ANN-based optimization strategy effectively addresses the operational challenges of industrial RO plants.
• This approach enables efficient decision-making by visualizing energy-water recovery trade-offs.
• The methodology provides a pathway for optimizing desalination operations to conserve resources and reduce costs.