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Prediction and Optimization of Desalination System Performance using Artificial Neural Network Approach

Vaziri, Amir Ardalan | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57872 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Mousavi, Ali; Nouri Borujerdi, Ali
  7. Abstract:
  8. Due to the global crisis of water and energy scarcity, the design and optimization of water treatment systems with the aim of reducing energy consumption and increasing efficiency have gained significant importance. An electrodialysis system was simulated and validated using advanced lumped model, considering all critical effects in the process, such as Donnan resistance, boundary layer resistance, and osmotic and electroosmotic flows, across salinity ranges from 0.35 to 200 parts per thousand. Additionally, a reverse osmosis desalination system was simulated and validated using the solution-diffusion model, with the concentration polarization coefficient calculated via the thin-film theory. The impact of all key parameters in the electrodialysis process was analyzed, demonstrating that lower potential differences and more stacks can significantly reduce the system’s energy consumption. to achieve optimal energy usage, two electrodialysis systems were simulated for brine recycling using linear and recirculating flow designs. Results indicated that the recirculating flow system, in very high salinities and regions where reverse osmosis systems are impractical, not only consumes less membrane surface but also saves up to 10% specific energy compared to the linear flow system. The energy consumption of reverse osmosis and electrodialysis systems was compared across different salinity ranges, and the strengths and weaknesses of each system were outlined. Finally, using simulation data from the electrodialysis system, an artificial neural network was trained, demonstrating excellent performance in predicting the functional characteristics of the electrodialysis system
  9. Keywords:
  10. Desalination ; Electrodialysis System ; Reverse Osmosis ; Current Density ; Advanced Lumped Model ; Artificial Neural Network

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