Application of neural networks in membrane separation

Abstract

<jats:title>Abstract</jats:title><jats:p>Artificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.</jats:p>

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

“…Membrane technology and its applications are a significant aspect of chemical and biochemical engineering. Mathematical models for membrane separation processes are crucial in membrane science and technology as they play an important role in planning of economical and effective membrane separation processes [8]. In view of the complex nature of fermentation broth as well as complicated relationship between filtration performance and operation conditions, it is reasonably difficult to describe the non-linear behaviors using a mathematical model [7].…”

“…In view of the complex nature of fermentation broth as well as complicated relationship between filtration performance and operation conditions, it is reasonably difficult to describe the non-linear behaviors using a mathematical model [7]. Conventional mathematical models involve rather complex mathematical equations in predicting permeate flux decline with time during membrane filtration processes and these models have certain limitations [8].…”

“…Artificial neural networks are used as an alternative approach in modeling. In the recent years, ANN has been widely used to predict the membrane performance in various filtration processes [8]. Compared to theoretical or mathematical models ANN modeling of CFMF flux decline is much simpler.…”

“…ANNs have been employed in a wide range of membrane separation processes such as reverse osmosis nanofiltration, ultrafiltration, microfiltration, gas separation, membrane bioreactors, and fuel cells [8]. In literature, numerous applications of ANN in CFMF processes can be found, and some of them are focused on the prediction of permeate flux and membrane fouling during CFMF of bentonite [9,10], protein [11,12] and colloid [13] suspensions, removal of phosphate from aqueous solution with fly ash [14], suspensions of baker's yeast [15], etc.…”

“…Accurate flux decline modeling is essential for optimization, simulation, and scale-up [8]. So, in this study, an ANN model, using feed flow, TMP, temperature and time as input variables, was established to predict the flux decline during turbulence promoter assisted CFMF of Streptomyces hygroscopicus fermentation broth.…”