A hybrid neural model (HNM) for the on‐line monitoring of lipase production by Candida rugosa

Abstract

A mechanistic model was proposed by Gordillo for the representation of lipase production by Candida rugosa, with the bioreactor in batch and fed-batch operation. However, the model was not able to represent the lipolytic activity. The objective of the present study is to propose an efficient hybrid neural-phenomenological model (HNM) for this process. The experimental data used corresponded to fed-batch operation with constant substrate feed rate at 2.8 × 10 −7 ; 5.6 × 10 −7 and 9.7 × 10 −7 kg s −1 . Artificial neural networks (ANNs) were trained to represent the aqueous and intracellular lipase activity and were further associated with a reduced version of the mechanistic model of the proposed HNM. When compared to the experimental data, the HNM exhibited higher accuracy. The HNM can be employed in process monitoring using only on-line measurements of CO 2 and substrate feed rate to infer enzyme activities and also substrate and biomass concentrations.

Neural Network Based Modeling and Operational Optimization of Biomass Gasification Processes

“…Presently, works based on hybrid neural-phenomenological [38] are being developed by the group as done before with a biotechnological process [39]. …”

Biomass measurement online: the performance of in situ measurements and software sensors

“…PCA models are also useful, particularly because they provide a simple variable for fault detection and quality control [72]. Neural networks have been used for biomass estimation on their own [2,14,27,30,47] or in combination with other modeling techniques [9].…”

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

“…The application of hybrid semi-parametric models in form of a soft-senor is very attractive for monitoring and both parallel (Lee et al, 2005) and serial (Boareto et al, 2007;Gnoth et al, 2008;Henneke et al, 2005;James et al, 2002;Jenzsch et al, 2007;Psichogios & Ungar, 1992;Schubert et al, 1994a;Silva et al, 2000Silva et al, , 2001von Stosch et al, 2011b) hybrid semi-parametric models find application. It was shown that the performance of a model in which the states and parameters were estimated by Nonlinear Programming (NLP) optimization or Extended Kalman Filter (EKF) approaches was inferior to the performance of a model in which the variable parameters were estimated using neural networks (Psichogios & Ungar, 1992), namely a hybrid semi-parametric model.…”

“…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 …”