A hybrid feedforward neural network model for the cephalosporin C production process

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

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

“…The concept of using more than one neural network was also explored in serial hybrid semi-parametric models (Cao et al, 2004;Gnoth, Jenzsch, Simutis, & Luebbert, 2008;Gupta et al, 1999;Patnaik, 2001Patnaik, , 2003Patnaik, , 2010Piron, Latrille, & Rene, 1997;Preusting et al, 1996;Reuter et al, 1993;Silva, Cruz, Hokka, Giordano, & Giordano, 2000, 2001. Preusting et al (1996) used two ANNs in parallel to model separate phenomena, i.e.…”

“…Gupta et al (1999) applied two parallel ANNs, each of which inferring a variable value, in series with three other parallel ANNs, each of which estimating a quantity that enters as an input to the mechanistic model. In Gnoth et al (2008), Silva et al (2000Silva et al ( , 2001) the prediction of one central kinetic rate (usually the specific biomass growth rate) by a first ANN, was used as an input (beside others) to another ANN, which in turn predicts another rate, e.g. the product formation rate.…”

“…for the modeling of yeast fermentations (Beluhan & Beluhan, 2000;Boareto, De Souza, Valero, & Valdman, 2007;Eslamloueyan & Setoodeh, 2011;Mazutti et al, 2010;Peres et al, 2001;Saraceno et al, 2010;Saxen & Saxen, 1996;Schubert et al, 1994aSchubert et al, , 1994b, for modeling of fungi cultivations (Chen et al, 2000;Ignova et al, 2002;Preusting et al, 1996;Silva et al, 2000Silva et al, , 2001Thibault et al, 2000;van Can et al, 1997Wang, Chen, Liu, & Pan, 2010), for modeling of bacteria cultivations (Costa, Alves, Henriques, Filho, & Lima, 1998;Gnoth et al, 2008;Henneke, Hagedorn, Budman, & Legge, 2005;Henriques et al, 1999;James et al, 2002;Jenzsch, Gnoth, Kleinschmidt, Simutis, & Luebbert, 2007;Laursen et al, 2007;Roubos et al, 2000;Thibault et al, 2000;Tholudur & Ramirez, 1999;Zuo, Cheng, Wu, & Wu, 2006;Zuo & Wu, 2000), for modeling of mammalian cell cultivations (Dors et al, , 1996Teixeira et al, 2005;Vande Wouwer et al, 2004), for modeling of insect cell cultivations (Carinhas et al, 2011), for modeling of hybridoma cell cultivations (Fu & Barford, 1995a, 1995b or for modeling the counter-ion fluxes across an ion-exchange membrane in a membrane-supported biofilm reactor …”

Preliminary Modeling of an Industrial Recombinant Human Erythropoietin Purification Process by Artificial Neural Networks

“…One of the most popular neural network paradigms applied to the modeling of a wide range of nonlinear systems, especially chemical and biological engineering processes, is the feed-forward back propagation neural network (Silva et al, 2000;Khaouane et al, 2013), which has been used in this paper with one hidden layer.…”

Modeling of an industrial process of pleuromutilin fermentation using feed-forward neural networks

“…It consists of a number of interconnected simple processing units called artificial neurons. One of the most popular neural network paradigms applied to the modeling of a wide range of nonlinear systems, especially chemical and biological engineering processes, is the feed-forward back propagation neural network (FFNN) (Lee and Park, 1999;Silva et al, 2000), which has been used throughout this paper with forecasting horizon and supervised learning.…”