Earticle

초록

영어

A recent adaptation of genetic algorithm, NSGA-II-JG, was applied systematically to each key to be completed for developing an optimal batch chromatographic process for 1,3-propanediol purification. The genetic algorithm was used first in the task of determining intrinsic parameters such as adsorption isotherms and mass-transfer coefficient. At this study, the genetic algorithm
plays a part in searching the values of intrinsic parameters that lead to the minimum difference between the experimental and the model predicted elution profiles. The genetic algorithm was applied to optimize a series of operating parameters, which include flow rate, feed loading volume, starting and ending times of product collection, and the time of gap between adjacent two feed injections. Such optimization, which aims for the highest productivity under the
constraints of product purity and pressure drop, was repeated by varying adsorbent particle size. The optimization results confirmed that the productivity for small particles was limited by the pressure drop whereas the productivity for large particles was limited by the mass-transfer efficiency. In consequence, the optimal particle size for the maximum productivity falls on the boundary between the two limiting regions. The results of this study indicate that the application of genetic algorithm is effective in each of a batch chromatographic process development, which covers from parameters estimation to process optimization.

키워드

저자

• Sung-Hee Kang [ Department of Chemical Engineering, Kongju National University ]
• Sun Im Jeon [ Department of Chemical Engineering, Kongju National University ]
• Kang-Hee Lee [ Department of Chemical Engineering, Kongju National University ]
• Sungyong Mun [ Department of Chemical Engineering, Hanyang University ]
• Jin-Hyun Kim [ Department of Chemical Engineering, Kongju National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국생물공학회 학술대회

• 간기

  반년간

• 수록기간

  1985~2013

• 십진분류

  KDC 476 DDC 576