Abstract
This paper addresses the problem of dynamic optimization of ethanol production. This process is described by a nonlinear model. A Model Predictive Control (MPC) has been implemented in order to optimize the bioprocess dynamically. Two algorithms were used together with a MPC: the Pattern Search (PS) and the Iterative Ant Colony Algorithm (IACA). They were compared with an open-loop control experimentally implemented. The MPC with the PS algorithm showed a better performance than the MPC with IACA and than the open-loop control.
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