이 연구는 벤젠과 프로필렌의 기상반응을 통해 쿠멘을 생산하는 쿠멘 생산 공정의 경제성 최적화에 대한 것이다. 최적화의 목적함수는 제품 판매 이득에서 자본비용, 유틸리티 비용, 원료 비용을 뺀 연간 조업이득이고, 설계변수는 6개이다. 설계변수의 변화에 따른 조업이득의 계산을 위해 Unisim Design과 Matlab을 연동하였다. 최적화는 3단계로 수행되었다. 설계변수를 샘플링한 후 조업이득 데이터를 얻고, 이 데이터로부터 설계변수와 조업이득의 관계를 추정 모델로 표현하고, 이 모델을 이용하여 최적화하였다. 추정모델로는 반응표면법에서 사용되는 2차 회귀 다항식과 비선형 모델인 support vector regression을 비교하였다. 설계변수의 샘플링 방법으로는 중심합성계획과 Hammersley 순차 추출법을 비교하였다. 각각 얻어진 모델을 이용한 최적화 결과, 추정방법으로는 SVR이, 샘플링방법은 Hammersley 순차추출법이 더 정확하였다. 최적화된 조업이득은 연간 17.96 MM$로, 기준 조건에서의 연간 16.04 MM$에 비해 12% 증가하였다.

Economic optimization of cumene manufacturing process to produce cumene from benzene and propylene was studied. The chosen objective function was the operational profit per year that subtracted capital cost, utility cost, and reactants cost from product revenue and other benefit. The number of design variables of the optimization are 6. Matlab connected to and controlled Unisim Design to calculate operational profit with the given design variables. As the first step of the optimization, design variable points was sampled and operational profit was calculated by using Unisim Design. By using the sampled data, the estimation model to calculate the operational profit was constructed, and the optimization was performed on the estimation model. This study compared second order polynomial and support vector regression as the estimation method. As the sampling method, central composite design was compared with Hammersley_x000D_ sequence sampling. The optimization results showed that support vector regression and Hammersley sequence sampling were superior than second order polynomial and central composite design, respectively. The optimized operational profit was 17.96 MM$ per year, which was 12% higher than 16.04 MM$ of base case.

Optimization Response Surface Method Central Composite Design Hammersley Sequence Sampling Support Vector Regression

Norouzi HR, Fatemi S, Chem. Eng. Commun., 199(11), 1375 (2012)
Turton R, Bailie RC, Whiting WB, Shaeiwitz JA, Analysis, Synthesis and Design of Chemical Processes, 3rd Ed., Prentice Hall, Upper Saddle River, NJ (2009)
Choi SH, Manousiouthakis V, Korean J. Chem. Eng., 19(2), 227 (2002)
Park SH, HWAHAK KONGHAK, 18(6), 503 (1980)
Luyben WL, Ind. Eng. Chem. Res., 49(2), 719 (2010)
Gera V, Kaistha N, Panahi M, Skogestad S, “Plantwide Control of a Cumene Manufacture Process,” 21st European Symposium on Computer Aided Process Engineering, May, Greece (2011)
Box GEP, Wilson KB, J. Royal Stat. Soc., Series B., 13(1), 1 (1951)
Oh KK, Kim SW, Jeong YS, Hong SI, HWAHAK KONGHAK, 34(4), 418 (1996)
Yoon CH, Bok HS, Choi DK, Row KH, Korean Chem. Eng. Res., 50(3), 545 (2012)
Sim CH, Korean Chem. Eng. Res., 51(6), 685 (2013)
Cho SK, Kim DH, Yun YM, Jung KW, Shin HS, Oh SE, Korean J. Chem. Eng., 30(7), 1493 (2013)
Simpson TW, Lin DKJ, Chen W, Int. J. Reliab. Appl., 2, 209 (2001)
Vining G, Kowalski SM, Statistical Methods for Engineers, 3rd Ed., Cengage Learning, Boston, MA (2011)
Lee CJ, Ko JW, Lee G, Korean Chem. Eng. Res., 48(6), 717 (2010)
Vapnik VN, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY (1995)
http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
Diwekar UM, Kalagnanam JR, AIChE J., 43(2), 440 (1997)
Luyben WL, Distillation Design and Control Using Aspen Simulation, Jonh Wiley & Sons, Hoboken, NJ (2006)