Luis A. Paz Suarez

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The present work reports our study on the benefits of integrating the Artificial Neural Network (ANN) technique as a time series predictor, with the concept of Model-based Predictive Control (MPC) in order to build an efficient process control. The combination of ANN and MPC usually leads to computationally very demanding procedure, that finally makes this(More)
The purpose of this paper is twofold. On one hand, we propose a modification of the general Model Predictive Control (MPC) approach where a prespecified tracking error is tolerated. The introduction of error tolerance (ET) in the MPC optimization algorithm reduces considerably the average duration of each optimization step and makes the MPC computationally(More)
This paper is focused on developing more efficient computational schemes for modeling in biochemical processes. A theoretical framework for estimation of process kinetic rates based on different temporal (time accounting) Artificial Neural Network (ANN) architectures is introduced. Three ANNs that explicitly consider temporal aspects of modeling are(More)
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