Moghaddam Amin Hedayati, Esfandyari Morteza
Department of Chemical Engineering, CT.C., Islamic Azad University, Tehran, Iran.
Department of Chemical Engineering, University of Bojnord, Bojnord, Iran.
Sci Rep. 2025 Jul 31;15(1):27911. doi: 10.1038/s41598-025-10516-x.
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.
本研究聚焦于优化乙醚(DEE)生产中的分离和纯化工艺,以提高能源效率、减少废弃物并提升产品质量。利用Aspen-Hysys-V14进行过程模拟、统计建模和优化技术,本研究考察了包括两个鼓和两个精馏塔在内的关键单元的操作参数。采用响应面方法(RSM)和中心复合设计(CCD)进行实验设计。关键结果表明,在优化条件下,DEE纯度达到96.43%,总能耗为2,150,566 kJ/h,相当于每千摩尔DEE的能量需求为1,499,754.87 kJ。基于情景的优化在平衡能源需求的同时,将DEE在燃料和排放流中的损失降至最低。对温度和压力等参数之间的非线性关系进行了具有高预测精度的建模。这些发现有助于开发可持续且具有成本效益的DEE生产工艺,并为类似的化学制造系统提供可借鉴的见解。