Experimental multivariable adaptive optimization of the steady‐state cellular productivity of a continuous baker's yeast culture

Abstract

A multivariable adaptive optimization algorithm that uses transient data to improve the optimization speed was successfully implemented on-line to maximize the steady-state cellular productivity of a continuous culture of baker's yeast. The algorithm was shown to be stable even during periods of oscillatory growth and was able to reoptimize the culture when planned disturbances were introduced. Although adaptive tuning of the forgetting factor improved the performance, further refinements in the adaptive forgetting factor algorithm are necessary for completely satisfactory results.