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<p>This study focuses on optimizing the separation and purification processes in diethyl ether (DEE) production to enhance energy efficiency, reduce waste, and improve product quality. Utilizing process simulation with Aspen-Hysys-V14, statistical modeling, and optimization techniques, this study investigates operational parameters across key units, including two drums and two distillation columns. Design of experiment was performed using response surface methodology (RSM) and central composite design (CCD). Key results indicate that under optimized conditions, a DEE purity of 96.43% was achieved with total energy consumption of 2,150,566&#xa0;kJ/h, corresponding to an energy requirement of 1,499,754.87&#xa0;kJ/kmol of DEE. Scenario-based optimization minimized DEE loss in fuel and vent streams while balancing energy demands. Non-linear relationships between parameters, such as temperature and pressure, were modeled with high predictive accuracy. These findings contribute to the development of sustainable and cost-effective DEE production processes and offer transferable insights for similar chemical manufacturing systems.</p>

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Optimization of separation and purification processes in diethyl ether production for improved efficiency and sustainability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月31日

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  • Amin Hedayati Moghaddam,
  • Morteza Esfandyari

摘要

This study focuses on optimizing the separation and purification processes in diethyl ether (DEE) production to enhance energy efficiency, reduce waste, and improve product quality. Utilizing process simulation with Aspen-Hysys-V14, statistical modeling, and optimization techniques, this study investigates operational parameters across key units, including two drums and two distillation columns. Design of experiment was performed using response surface methodology (RSM) and central composite design (CCD). Key results indicate that under optimized conditions, a DEE purity of 96.43% was achieved with total energy consumption of 2,150,566 kJ/h, corresponding to an energy requirement of 1,499,754.87 kJ/kmol of DEE. Scenario-based optimization minimized DEE loss in fuel and vent streams while balancing energy demands. Non-linear relationships between parameters, such as temperature and pressure, were modeled with high predictive accuracy. These findings contribute to the development of sustainable and cost-effective DEE production processes and offer transferable insights for similar chemical manufacturing systems.