Open AccessA hybrid feedforward neural network model for the cephalosporin C production process
Author(s) -
R.G. Silva,
Antonio José Gonçalves Cruz,
Carlos O. Hokka,
Raquel de Lima Camargo Giordano,
Roberto de Campos Giordano
Publication year - 2000
Publication title -
brazilian journal of chemical engineering/brazilian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.313
H-Index - 52
eISSN - 1678-4383
pISSN - 0104-6632
DOI - 10.1590/s0104-66322000000400023
Subject(s) - artificial neural network , bioprocess , feedforward neural network , computer science , robustness (evolution) , feed forward , process engineering , biochemical engineering , control engineering , artificial intelligence , engineering , chemistry , biochemistry , chemical engineering , gene
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