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This study addresses the operational optimization problem (OOP) in large-scale seawater reverse osmosis (RO) desalination plants. A novel two-stage differential evolution algorithm effectively minimizes running costs when operating conditions change.

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Area of Science:

  • Engineering
  • Environmental Science
  • Optimization

Background:

  • Large-scale seawater reverse osmosis (RO) desalination plants utilize multiple RO units.
  • Changes in operating conditions can lead to suboptimal performance of individual RO units.
  • Ensuring efficient plant operation under varying conditions is crucial for cost-effectiveness.

Purpose of the Study:

  • To model the operational optimization problem (OOP) of large-scale parallel-unit RO plants.
  • To develop an effective algorithm for minimizing total running costs during operational changes.
  • To evaluate the performance of the proposed optimization method.

Main Methods:

  • The OOP was formulated as a mixed-integer nonlinear programming problem.
  • A two-stage differential evolution algorithm was designed and implemented.
  • The algorithm was tested using experimental data to assess solution quality.

Main Results:

  • The proposed two-stage differential evolution algorithm successfully addressed the OOP.
  • The method demonstrated satisfactory performance in finding optimal operational schedules.
  • The approach effectively minimizes total running costs for RO desalination plants.

Conclusions:

  • The developed algorithm provides an effective solution for optimizing RO plant operations.
  • This research contributes to more efficient and cost-effective desalination processes.
  • The findings support the practical application of advanced algorithms in industrial water treatment.