摘要
反應蒸餾係指結合蒸餾塔之熱分離技術及流入塔內之進料化學反應以達成連續反應的單元操作，優勢在克服平衡限制、提高轉化率、降低能耗需求及減少設備及操作成本等。然而，蒸餾塔啟動實為化工產業極為複雜的動態程序，過程中耗時且耗能。本研究利用異丙醇反應蒸餾合成乙酸異丙酯作為標的，並使用商業軟體Aspen Plus®及滲透蒸發薄膜設計產品(異丙酯)濃度達99.5 wt%且不純物(水)低於0.1 wt%之規格要求，同時建立一套反應蒸餾─滲透蒸發啟動手法，藉由在臺科大化工系建置反應蒸餾試驗廠來驗證實驗與模擬結果以及呈現系統對擾動排除之表現。
基於相同反應蒸餾啟動手法，本研究探討以三種不同初始填料方式：醋酸/異丙醇進料比、穩態餘料及純醋酸做最佳啟動策略比較，結果顯示，凝液罐內填入醋酸/異丙醇進料比或純醋酸，啟動時間將耗費6小時以上，而以穩態餘料用作填料能於2小時內完成啟動且可大幅減少啟動時間及節省填料。此外，針對側流產品端於343.15 K至383.15 K進料溫度下作薄膜滲透蒸發通量與產品濃度影響之分析，其結果得出進料溫度在363.15 K以上即能將含水率降低至0.1 wt%並得出高純度99.5 wt%乙酸異丙酯產品。
本實驗研究亦使用Matlab/Simulink®建立類神經網路模型(Artificial Neural Network, ANN)來探討智能操作指引與傳統PID控制對製程產能擾動控制之差異，並透過離線式虛擬模型給予試驗廠操作指引來強化實驗操作結果。比較傳統PID控制下於擾動過程中產品濃度將降低至90 wt%，結果顯示使用者經智能操作指引提供之擾動後溫度建議設定值進行每5、15、30分鐘調動，乙酸異丙酯產品濃度控制仍可比傳統PID控制佳，而相比15及30分鐘調動需長時間排除擾動，最後以每5分鐘調動可於2小時內穩定製程並維持產品濃度99 wt%之操作為最佳控制。

Abstract
Reactive distillation (RD) is a process which combined separation and chemical reaction in a one unit to improve the conversion of reaction and energy-saving; however, the startup of distillation column is a time- and energy-consuming process and is one of the most complex operations in the chemical industry. In this study, esterification of isopropanol (IPA) by using RD is the target process to demonstrate the startup operation and also show the performance of disturbance rejection in the normial operating condition. Furthermore, isopropyl acetate (IPAc) is purified to 99.5 wt% and less than 0.1 wt% H2O is required as specifications via reactive distillation−pervaporation hybrid process and startup instruction is established experimentally in the pilot-scale plant.
There are three different initial charge strategies in RD optimal startup procedure: (1) the feed ratio, (2) residue and (3) pure acetic acid. According to the results, the residue in the sump could be minimized the startup period about 2 hrs and the others would be taken over 6 hrs to finish startup operation. Moreover, pervaporation (PV) has been studied to remove impurity (water) below 0.1 wt% and examined under different feed temperature from 343.15 K to 383.15 K. Finally product could be met 99.5 wt% and less than 0.1 wt% H2O as specifications over 363.15 K.
In this work, the artificial neural network (ANN) built by Matlab/Simulink® is taken to compare with conventional PID control performance under the throughput changes. After the disturbances, the offline virtual model could provide the track setpoints of temperature controller (TIC-07) for the users. In the different operating change frequencies(5、15、30 mins), it shows that product would be maintained at the high purity after the disturbances while operators adjusting the setpoints in five-min period.

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【網頁】
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