Abstract
An on-line adaptive control strategy based on DO/pH measurements and artificial neural network pattern recognition (ANNPR) model for fed-batch cultivation processes was proposed. Various changing patterns of pH and dissolved oxygen concentration (DO) under pH-Stat, DO-Stat, and the conditions of substrate in excess were collected and used to train the ANNPR models. Based on the on-line measured pH and DO data, the recognition results on current physiological state was deduced by the ANNPR models, and then the on-line adaptive control of nutrient feeding rate was implemented. Compared with the traditional pH-Stat control, the proposed control strategy increased cell productivity about 150% without byproduct accumulation. The control strategy is potentially useful for high cell density cultivation of recombinant microorganisms to efficiently express value-added foreign proteins or enzyme with the most traditional pH and DO sensors.
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