Application of Genetic Algorithms for Parameter Estimation in Liquid Chromatography

Application of Genetic Algorithms for Parameter Estimation in Liquid Chromatography

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0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented.Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost.These methods are iterative process to perform a robust search of a Lecithin solution space.

Genetic algorithms are optimization techniques based on the principles of genetics and natural selection.They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems.In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography.