Dynamic neural-network-based model-predictive control of an industrial baker's yeast drying process

YÜZGEÇ U., BECERİKLİ Y., Turker M.

IEEE Transactions on Neural Networks, vol.19, no.7, pp.1231-1242, 2008 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 7
  • Publication Date: 2008
  • Doi Number: 10.1109/tnn.2008.2000205
  • Journal Name: IEEE Transactions on Neural Networks
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1231-1242
  • Keywords: Drying process, Dynamic neural networks (NNs), Genetic algorithm (GA), Model-predictive control (MPC)
  • Bilecik Şeyh Edebali University Affiliated: No

Abstract

This paper presents dynamic neural-network-based model-predictive control (MPC) structure for a baker's yeast drying process. Mathematical model consists of two partial nonlinear differential equations that are obtained from heat and mass balances inside dried granules. The drying curves that are obtained from granule-based model were used as training data for neural network (NN) models. The target is to predict the moisture content and product activity, which are very important parameters in drying process, for different horizon values. Genetic-based search algorithm determines the optimal drying profile by solving optimization problem in MPC. As a result of the performance evaluation of the proposed control structure, which is compared with the model based on nonlinear partial differential equation (PDE) and with feedforward neural network (FFN) models, it is particularly satisfactory for the drying process of a baker's yeast. © 2008 IEEE.