a hybrid feedforward neural network model for the cephalosporin c production process

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ID: 201213
2000
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Abstract
At present, direct on-line measurements of key bioprocess variables as biomass, substrate and product concentrations is a difficult task. Many of the available hardware sensors are either expensive or lack reliability and robustness. To overcome this problem, indirect estimation techniques have been studied during the last decade. Inference algorithms rely either on phenomenological or on empirical models. Recently, hybrid models that combine these two approaches have received great attention. In this work, a hybrid neural network algorithm was applied to a fermentative process. Mass balance equations were coupled to a feedforward neural network (FNN). The FNN was used to estimate cellular growth and product formation rates, which are inserted into the mass balance equations. On-line data of cephalosporin C fed-batch fermentation were used. The measured variables employed by the inference algorithm were the contents of CO2 and O2 in the effluent gas. The fairly good results obtained encourage further studies to use this approach in the development of process control algorithms.
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silva2000braziliana Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;R.G. Silva;A.J.G. Cruz;C.O. Hokka;R.L.C. Giordano;R.C. Giordano
Journal database and network journal
Year 2000
DOI
10.1590/S0104-66322000000400023
URL
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322000000400023
Keywords
Neural network hybrid model

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