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篇名 芳香烴萃取製程產生廢萃取劑之回收再利用
卷期 35
並列篇名 Recycling of the spent extractant generated from the aromatic extraction process
作者 李依釗林文崇吳叡衡
頁次 001-012
關鍵字 芳香烴廢溶劑回收再利用二乙二醇aromaticspent extractantrecyclediethylene glycol
出刊日期 202212

中文摘要

以環境保護著眼,將碳氫油品中之芳香烴減量是必須的。而碳氫油品中芳香烴的去除或減量,常以溶劑萃取的方法來進行,但萃取完後帶有芳香烴之萃取溶劑必須以另法處理以回收再使用;然而,在整個操作過程中,尤其是在溶劑回收的加熱過程,溶劑常會劣化變黑、變黏稠、甚至於結焦,製造出為數不少的廢溶劑。這不僅會浪費許多的煉製成本,更會衍生出難以處理的廢棄物問題。本研究針對汽油成分範圍油料(五碳烴~十二碳烴)分離出苯、甲苯、二甲苯工場萃取其中芳香烴製程所衍生之溶劑回用及廢溶劑問題,秉持著減廢及資源回收再利用的理念,從製程上做一探討,期能將萃取後富含芳香烴之萃取溶劑運用適當的蒸餾操作條件處理以循環再使用,並盡量降低廢溶劑的產出量;另一方面將取樣分析這些既成廢溶劑之組成和主成分的含量,以探討將其回收再利用之可行性。本研究以實驗及應用電腦模擬的方式進行,完成以芳香烴萃取劑-二乙二醇,建立蒸餾模式,探討其萃取汽油範圍中之芳香烴後蒸餾回收之板數、回流比、進料位置以及壓力等之影響,找出理想的蒸餾操作條件。其次應用氣相層析-質譜儀等儀器分析,鑑定出以萃取劑二乙二醇萃取汽油中芳香烴,經多次蒸餾回用後變成黑色黏稠狀廢溶劑中之詳細組成分。接著對此廢溶劑形成之原因及反應機制做探討。最後比照傳統實場萃取操作的條件,包括溫度、溶劑/油料比等進行廢溶劑中之各種組成分對芳香烴做一系列的萃取性能試驗,判斷出絕大部分的廢溶劑應回收再使用,以達減廢之目標。

英文摘要

In view of environmental protection, it is necessary to reduce the aromatic contents of hydrocarbon products. Solvent extraction is an effective method for removing aromatics from hydrocarbon oils. After extracting, the solvent with aromatics(rich-solvent)should be regenerated and recycled by adequate operation method. But during these procedures, especially the regeneration step, it was found the quality of recovered solvent gradually degraded to deep color and viscous fluid. The deep color solvent was wasted conventionally. The general solvents used in the benzene, toluene, and xylene extraction from gasoline fraction (C5~C12)was diethylene glycol. The derivatives of the spent solvents and their recovery methods were investigated in this study. The method of approach is completely on waste minimization and solvent resource utilization ideas. The main items have been studied are as follows: Find the optimum conditions of distillation to recycle the extractants after extracting aromatics by using a process simulation method. The derivatives of spent solvent have been investigated and identified in detail. The performances of these derivatives for the aromatic extraction were also compared by using scale down experiments.

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