Dynamic Modeling of Streptomyces hygroscopicus Fermentation Broth Microfiltration by Artificial Neural Networks

Abstract

Artificial neural networks (ANNs) have been used to dynamically model cross-flow microfiltration of Streptomyces hygroscopicus fermentation broths. The aim is to predict permeate flux as a function of temperature, feed flow, transmembrane pressure and processing time. Dynamic modeling of microfiltration performance of complex systems (such as broths) is very important for design of new processes and better understanding of the present. The results of ANN model analysis suggest that the coefficients of the determination have high values. The application of the Bayesian regularization gave better results to the performance of the neural network compared to the Levenberg-Marquet algorithm. The optimal number of neurons in the hidden layer is eight. Analysis of the absolute relative error showed excellent permeate flux estimates for 100 % of the data points, with an error less than 5 % for the data obtained during microfiltration in the presence of a turbulence promoter. Whilst in the case of microfiltration without turbulence promoter 90 % of predictions have an error less than 10 %. The results of applying the concept of neural networks in the dynamic modeling of microfiltration of Streptomyces hygroscopicus fermentative broths with and without a turbulence promoter clearly show the validity of proposed method for simulation and prediction of microfiltration experimental results.

Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter

“…It could be concluded that filtration time has the most significant effect in determination of the permeate flux decline (50.30%). These results are in agreement with findings reported for dynamic modeling of Streptomyces hygroscopicus fermentation broth microfiltration [15], microfiltration of starch wastewater [31] and cross-flow microfiltration of a mixture that contains phosphate and fly ash [38].…”

“…The presence of the Kenics turbulence promoter (SM mode) resulted in a significant increase of flux value (around 300%) for selected experimental conditions depicted in Figure 3. The characteristic shape of the mixer that intensifies radial mixing in the membrane channel and reduces the cake thickness is a probable reason for this [13][14][15]. Combination of the static mixer and two-phase flow (AS + SM mode) resulted in the highest flux values, although the contribution of air-sparging is less compared to the static mixer.…”

“…The hydraulic resistance associated with the cake build-up is the main factor influencing the microfiltration of cultivation broths [8,11,12]. The reduction of cake layer, and consequently permeate flux improvement, can be achieved by applying flow alternations in a membrane channel [13][14][15][16][17].…”

“…In contrast to the previous studies [15,31], the novelty of the approach adopted in this work is training of the single neural network that could be used as simulation tool for the different modes of microfiltration operation. The modes of the operation include the use of hydrodynamic methods (air-sparging and static mixer) for flux decline lessening.…”

“…The modes of the operation include the use of hydrodynamic methods (air-sparging and static mixer) for flux decline lessening. Considering the previous research in the field of ANN application for dynamic modeling of microfiltration [15,31], the aim of this study was to investigate the performance of non-recurrent feed-forward network with one hidden layer in prediction of permeate flux value during microfiltration of Bacillus velezensis cultivation broth. The main goal was determination of the optimal neural network architecture for dynamic permeate flux prediction under various conditions.…”