Desarrollo de un marco computacional para la optimización mejorada de bioprocesos mediante algoritmos genéticos
DOI:
https://doi.org/10.15517/00je4649Keywords:
Algoritmos genéticos, fermentación de xilosa, optimización de bioprocesos, optimización híbrida, producción de xilitolAbstract
La optimización de bioprocesos frecuentemente implica modelos dinámicos no lineales que desafían los métodos basados en derivadas. En este estudio, se optimizó un modelo cinético para producir xilitol, mediante un esquema jerárquico de algoritmos genéticos (GA). Primero, un GA secundario minimizó una función objetivo económica. Posteriormente, un GA primario ajustó el tamaño de población y la fracción de entrecruzamiento del GA secundario. El GA secundario reveló un “valle de estabilidad” en el intervalo de fracción de entrecruzamiento: 0.60 ≤ cf ≤ 0.90, donde la convergencia fue estable y las desviaciones estándar se mantuvieron bajas. Las pruebas con la función de Ackley confirmaron que la rugosidad de la superficie de solución rige el desempeño del GA. El enfoque jerárquico identificó una configuración óptima: cf = 0.53 y población = 260, fuera del valle, que incrementó la función objetivo en un 3.1 % respecto al mejor valor dentro del valle. No se introdujeron modificaciones al modelo cinético ni al criterio económico, la ganancia se atribuye exclusivamente a la sintonización de los metaparámetros. Sin embargo, el tiempo de cómputo se incrementó, por tanto, sistemas de mayor tamaño podrían requerir estrategias híbridas, como modelos de fidelidad variable o esquemas de mutación adaptativa. Aun así, la mejora demostrada se traduce en beneficios económicos significativos y subraya el valor de la metaoptimización sistemática de los GA para bioprocesos industriales. Los resultados proporcionan un referente reproducible y una base para extender este marco a modelos más complejos, metaheurísticas híbridas y ajustes de parámetros guiados por aprendizaje automático.
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