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Received February 21, 2008
Accepted April 14, 2008
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모노에틸렌 글리콜 생산공정의 정상상태 모사 및 에너지 절약 최적화 연구
Steady-state Simulation and Energy-saving Optimization of Monoethylene Glycol Production Process
인하대학교 화학공학과, 402-751 인천시 남구 용현동 253 1재능대학 화장품과, 401-714 인천시 동구 송림동 122
Department of Chemical Engineering, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402-751, Korea 1Department of Cosmetics, Jai-Neung College, 122 Songlim-dong, Dong-gu, Incheon 401-714, Korea
stchung@inha.ac.kr
Korean Chemical Engineering Research, October 2008, 46(5), 903-914(12), NONE Epub 10 November 2008
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Abstract
본 연구는 ethylene oxide로부터 monoethylene glycol을 주제품으로 생산하는 상용화된 실제 공정의 생산 능력 증가시에 필요한 공정 모사와 에너지 절감을 위한 최적화 연구로서, 공정에 관여하는 다성분계의 기/액 상평형 거동을 NRTL-RK식으로 나타내고, 필요한 총 91개의 2성분계쌍의 상호작용 파라미터 값들로는 8개의 2성분계쌍에 대해서는 Aspen PlusTM 상용 모사기(Ver. 2006)에 내장된 값, 28개의 쌍에 대해서는 상평형 데이터를 문헌에서 조사하여 회귀분석하고 나머지 2성분계에 대해서는 모사기 내의 추산 기능을 이용하여 구한 값을 사용하였으며, 공정 모사 결과와 실제 공정 데이터와의 비교를 통해 상평형 계산의 정확성을 확인한 후, 모사기에 내장된 민감도 분석 기능을 사용하여 전체 에너지 소모량에 대한 각 장치의 민감도를 조사하여 적절한 조절변수를 선정하고 모사기 내에 내장되어 있는 순차적 2차 계획법에 의한 최적화 기능을 이용하여 공정 전체의 에너지 절약을 위한 최적화 작업을 수행하였다.
This study was undertaken for the production capacity expansion and energy saving through entire process simulation and optimization for the commercial process of manufacturing monoethylene glycol as a staple from ethylene oxide. Aspen PlusTM(ver. 2006) was employed in the simulation and optimization work. The multicomponent vaporliquid equilibria involved in the process were calculated using the NRTL-RK equation. As for the binary interaction parameters required for a total of 91 binary systems, those for 8 systems were self-supplied by the simulator, those for 28 systems were estimated through regression of the VLE data in the literature, and the remainder were estimated with the estimation system built in the simulator. Subsequent to ascertaining the accuracy of the generated parameters through comparison between actual and simulated process data, sensitive variables highly affecting the process were searched and selected using sensitivity analysis tool in the simulator. The optimum operating conditions minimizing the total heat duty of the process were investigated using the optimization tool based on the successive quadratic programming in the simulator.
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Fredenslund A, Sather GA, J. Chem. Eng. Data, 17, 440 (1972)
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Bae HK, Nagahama K, Hirata M, J. Chem. Eng. Data, 27, 25 (1982)
Davalos J, Anderson WR, Phelps RE, Kidnay AJ, J. Chem. Eng. Data, 21, 81 (1976)
Spano JO, Heck CK, Barrick PL, J. Chem. Eng.Data, 13, 168 (1968)
Somait FA, Kidnay AJ, J. Chem. Eng. Data, 23, 301 (1978)
Stryjek R, Chappelear PS, Kobayashi R, J.Chem. Eng. Data, 19, 334 (1974)
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Anthony RG, McKetta JJ, J. Chem. Eng. Data, 12, 21 (1967)
Helntzt A, Streett WB, J. Chem. Eng. Data, 27, 465 (1982)
Tsierkezos NG, Molinou IE, J. Chem. Eng. Data, 43(6), 989 (1998)
Wei MS, Brown TS, Kidnay AJ, Sloan ED, J. Chem. Eng. Data, 40(4), 726 (1995)
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Bezanehtak K, Combes GB, Dehghani F, Foster NR, J. Chem. Eng. Data, 47, 161 (2002)
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Keshtkar A, Jalali F, Moshfeghian M, Fluid Phase Equilib., 145(2), 225 (1998)
Twu CH, Tassone V, Sim WD, Watanasiri S, Fluid Phase Equilib., 228, 213 (2005)
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Folas GK, Berg OJ, Solbraa E, Fredheim AO, Kontogeorgis GM, Michelsen ML, Stenby EH, Fluid Phase Equilib., 251(1), 52 (2007)
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http://www.cheric.org/research/kdb/hcvle/hcvle.php
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Bandyopadhyay S, Chem. Eng. J., 88(1-3), 175 (2002)
Monroy-Loperena R, Perez-Cisneros E, Alvarez-Ramirez J, Chem. Eng. Sci., 55(21), 4925 (2000)
Soave G, Feliu JA, Applied Thermal Engineering, 22, 889 (2002)
Hou WF, Su HY, Hu YY, Chu J, Chin. J. Chem. Eng., 14(5), 584 (2006)
Langston P, Hilal N, Shingfield S, Webb S, Chem. Eng. Process., 44(3), 345 (2005)
Munoz R, Monton JB, Burguet MC, de la Torre J, Sep. Purif. Technol., 50(2), 175 (2006)
Lee JC, Yeo YK, Song KH, Kim IW, Korean J. Chem. Eng., 18(4), 428 (2001)
Neves FJM, Silva DCM, Oliveira NMC, Comput. Chem. Eng., 29(6), 1457 (2005)
Chang H, Li JW, Chem. Eng. Sci., 60(10), 2771 (2005)
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