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Time Accounting Artificial Neural Networks for Biochemical Process Models

  • Petia GeorgievaEmail author
  • Luis Alberto Paz Suárez
  • Sebastião Feyo de Azevedo
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Abstract

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 exemplified: (i) Recurrent Neural Network (RNN) with global feedback (from the network output to the network input); (ii) time-lagged feedforward neural network (TLFN), and (iii) reservoir computing network (RCN). Crystallization growth rate estimation is the benchmark for testing the methodology. The proposed hybrid (dynamical ANN and analytical submodel) schemes are promising modeling framework when the process is strongly nonlinear and particularly when input--output data is the only information available.

Keywords

Artificial Neural Network Recurrent Neural Network Distillation Column Observation Error Crystal Size Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was financed by the Portuguese Foundation for Science and Technology within the activity of the Research Unit IEETA-Aveiro, which is gratefully acknowledged.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Petia Georgieva
    • 1
    Email author
  • Luis Alberto Paz Suárez
    • 2
  • Sebastião Feyo de Azevedo
    • 2
  1. 1.Signal Processing Lab, IEETA, DETIUniversity of AveiroAveiroPortugal
  2. 2.Faculty of Engineering, Department of Chemical EngineeringUniversity of PortoPortoPortugal

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