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April 10, 2026Chemical Engineering & Technology1 citations

Optimization of MVR System for High‐Salinity Wastewater Treatment Using Genetic Algorithm

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GYGuorong YueJWJing Wang

Key Points

  • The aim is to optimize a mechanical vapor recompression system for treating high-salinity wastewater efficiently.
  • Developed a multi-objective optimization framework integrating a steady-state process model and SPEA2 algorithm.
  • Conducted exergy evaluation and sensitivity analysis to assess system performance.
  • Determined optimal operational conditions such as evaporation temperature and compressor discharge pressure.
  • Achieved an 8.22% reduction in total heat transfer area.
  • Reduced energy consumption by 8.61% under optimized conditions.
  • Economic analysis revealed a 6.36% cost reduction.
  • Improved thermal efficiency by 5.74% and decreased energy loss by 10.35%.

Abstract

ABSTRACT To tackle high energy consumption for high‐salinity wastewater, this study proposes an optimized multi‐effect mechanical vapor recompression (MVR) system for calcium chloride treatment. A hybrid multi‐objective optimization framework was established by integrating a rigorous steady‐state process model with strength Pareto evolutionary algorithm 2 (SPEA2). Exergy evaluation and sensitivity analysis showed that system performance improved with a first‐effect evaporation temperature of 103.64°C and a compressor discharge pressure of 1.9 bar. Under these conditions, the total heat transfer area decreased by 8.22%, energy consumption reduced by 8.61%, and crucially, economic analysis showed a 6.36% reduction. The system also demonstrated a 5.74% improvement in thermal efficiency and a 10.35% decrease in energy loss. These results confirm the proposed framework's effectiveness in balancing thermodynamic efficiency and economic viability, offering a scalable and robust design for industrial retrofitting of high‐salinity wastewater evaporation.

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Cite This Study

Yue et al. (2026) studied this question.

synapsesocial.com/papers/69d8948f6c1944d70ce058bahttps://doi.org/10.1002/ceat.70210
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