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.

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg–Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.

Households sector in Serbia presents a great chance for energy savings and introduction of RES in the future. The public policies in Serbia are currently limited, but this kind of study can influence public measures that would undeniably generate long-term social and economic benefits to the country. The aim of the present work is to assess economic feasibility of closed loop heat pump systems for heating and cooling purposes in Serbia's residential sector. The heat pump system was compared to the most commonly used heating fuels in households. Results indicate that the implementation of ground closed loop heat pump systems for heating and cooling purposes in Serbia's residential sector as a substitute for electric heating is economically feasible. Inadequate prices of natural gas and electricity in public supply are the main problems associated with the project's financial benefits. The best results were obtained in the scenario with combined debt ratio (40%) and grants (~30%) for the project realization, for which equity pay-back period is approximately three years, while benefit to cost ratio is 2.52. Investigated financial metrics (equity pay-back, internal rate of return assets and net present value) indicate the same positive results considering financial viability of the project.