Fermenter modelling using local linear models

  1. Francisco, M.
  2. Vega, P.
  3. Gil, A. B.
Aktak:
Modelling, Identification, and Control Conference (MIC 2001) : proccedings

ISSN: 1025-8973,  2293-5126

ISBN: 0-88986-316-4

Argitalpen urtea: 2001

Alea: 1

Orrialdeak: 308-313

Biltzarra: IASTED International Conference on Modelling, Identification, and Control (MIC 2001), Innsbruck, Austria, February 19-22, 2001

Mota: Biltzar ekarpena

GOOGLE SCHOLAR

Laburpena

In this work, some advanced methods have been developed to model a fermenter for baker’s yeast production. Firstly, the process and its mathematical model are described, explaining the reasons that make necessary the use of new techniques. Secondly, a state space neural network (SSNN) is used to model the process globally, giving good results for typical validation data. Then, a linear local modelling approach is presented, and two methods are considered to identify this local models. The first one allows to train a linear SSNN for every local model, and the second one allows to linearize the global model obtained with the SSNN around the points where local models are placed. Both methods give good results, but the accuracy of the second is better due to the robustness of the SSNN global model.