Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of Bacillus megaterium cellular concentrations

Abstract

Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.

Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study

“…The production medium was centrifuged at 14,0009g and 4°C, during 20 min. The enzyme activity was determined by colorimetric PDAB method [23] and the cell concentration by optical density [24]. More details can be found in [9,15,24].…”

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

“…5 . In batch mode training, the cluster centers vector V={v i } and the membership degree matrix U={u i k } are calculated with respect to the optimal number of cluster centers.…”

“…The combination of ANNs and FL techniques makes neuro-fuzzy (NF) systems a very flexible option for optimization applications [5]. The essential behavior of biological processes can be characterized by using these systems based on gathered information from experiments.…”