Nucci Edson R, Silva Rosineide G, Souza Vanessa R, Giordano Raquel L C, Giordano Roberto C, Cruz Antonio J G
Departamento de Engenharia Química, Universidade Federal de São Carlos, PO Box 676, São Carlos, São Paulo 13565-905, Brazil.
Bioprocess Biosyst Eng. 2007 Nov;30(6):429-38. doi: 10.1007/s00449-007-0138-8. Epub 2007 Jul 3.
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.
青霉素G酰化酶（PGA）是制药工业中最重要的酶之一。巨大芽孢杆菌具有产生胞外PGA的优势。这项工作比较了两种神经网络（NNs）架构，用于在PGA生产过程中对充气搅拌罐生物反应器中的巨大芽孢杆菌细胞量进行在线推断。如今，诸如人工神经网络和模糊逻辑等智能计算工具通常应用于生物反应器的状态推断和建模。在混合神经模糊系统中将这两种方法结合起来可能会有优势。我们的结果表明，与多层感知器网络相比，神经模糊推理系统在推断细胞浓度方面表现出更好的性能。
