Application of genetic algorithm to determination of intrinsic parameters and optimization of a batch chromatographic process for 1,3-propanediol purification

Abstract

A recent adaptation of genetic algorithm, NSGA-II-JG, was applied systematically to each key task 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. For such parameters determination, we adopted an inverse method, which is to acquire intrinsic parameters by a least square fitting of the proposed column model to the experimentally measured elution profile. At this stage, 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. In the next stage, 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 (or column 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 stage of a batch chromatographic process development, which covers from parameter estimation to process optimization.