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Received May 1, 2008
Accepted May 7, 2008
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석유화학공장 규모 최적화를 위한 변수 선정

Selecting Decision Variable for a Plant-wide Optimization

서울대학교 화학생물공학부, 151-742 서울시 관악구 신림9동 산 56-1
School of Chemical and Biological Engineering, Seoul National Universty, San 56-1, Shinlim-dong, Gwanak-gu, Seoul 151-742, Korea
chhan@snu.ac.kr
Korean Chemical Engineering Research, August 2008, 46(4), 714-721(8), NONE Epub 10 September 2008
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Abstract

에너지의 소비가 큰 화학공장은 공정 자체가 가지는 비선형성, 불안정성등과 여러 가지 외란으로 인한 최적의 상태로 운전되고 있지 못하다. 이를 개선하기 위해 공장 전체 최적화를 수행하게 된다. 공장 전체를 대상으로 하는 최적화에는 경제적으로 영향을 주는 조절 변수가 많기 때문에 조절 변수의 개수를 최적으로 선정하는 문제는 중요하다. 경제적으로 영향을 주는 조절 변수를 모두 사용하여 최적화를 할 경우 최적화하여 나온 결과를 운전 조건에 반영할 때 많은 운전조건이 바뀌게 되므로 운전 조건의 변화에 따른 비용이 증가하게 된다.본 연구에서는 TPA(Terephthalic Acid)공정을 대상으로 공장 규모 최적화를 하기 위하여 운전 비용에 영향을 주는 최적화 조절 변수를 최적으로 선정하기 위한 방법을 제시하였다. 즉, 모델을 만든 후 운전비용에 영향을 주는 조절 변수의 정도를 민감도 분석을 통해 알아 봄 으로써 최적화할 때 운전 비용에 영향이 큰 변수들만 사용하는 것이다. TPA공정에서는 본 연구에서 제시한 방법에 의해 3개의 조절 변수를 선정하였고 선정된 변수로 최적화 한 결과 추가적인 설비 투자나 물리적인 개조 등이 없이 연간 약 3억 5천 만원의 에너지 비용 절감이 기대 된다
Chemical plants which consume lots of energy are not operating in the best conditions due to their own peculiar nonlinearity, instability, and diverse disturbances. In order to improve this, the plant wide optimization was performed. It is important to select the most appropriate number of decision variables which strongly affect the operating cost because there are too many decision variables which economically have an effect on plant wide. For instance, if all decision variables which can economically affect are applied in optimization and then the result of the optimization is applied to operation, a lot of operating conditions should be going to be changed. As a result of changing a plenty of operating conditions, the cost of the change will absolutely increase. Thus, in this study, the method of selecting the most appropriate decision variables which can influence on saving operation costs was presented in order to optimize plant wide. TPA (Terephthalic-acid) plant is considered as a case study. In other word, after modeling, the most proper decision variables was selected by examining the degree which decision variables influence on operating costs through sensitivity analysis. In TPA process, the three decision variables were selected by the presented method in this study. Then the plant was optimized by selected the decision variables. Consequently, it was seen that the plant are expected to save the 350 million won of energy annually without additional investment for facilities or remodeling of the plant.

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