Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process

Sekulić, Zoran; Antanasijević, Davor; Stevanović, Slavica; Trivunac, Katarina

doi:10.1007/s13762-017-1248-8

Cited by 18 publications

(5 citation statements)

References 33 publications

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“…Prediction of sorption of heavy metals from aqueous solution using ANN has been performed successfully in several studies . There are a number of distinct ANN architectures among which the general regression neural network (GRNN) is especially useful if only a small and sparse dataset is available . The GRNN is used for non‐parametric estimation of the probability density of data, it does not require iterative training and is useful for relatively non‐linear data processing…”

Section: Methodsmentioning

confidence: 99%

“…[30][31][32] There are a number of distinct ANN architectures among which the general regression neural network (GRNN) 33 is especially useful if only a small and sparse dataset is available. 34 The GRNN is used for non-parametric estimation of the probability density of data, it does not require iterative training and is useful for relatively non-linear data processing. 35 wileyonlinelibrary.com/jctb In the current study, GRNNs, feed-forward one-pass learning supervised trained networks, were used to predict removal efficiency of metal cations using seashell waste.…”

Section: Grnn Architecturementioning

confidence: 99%

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Experimental and theoretical consideration of the factors influencing cationic pollutants retention by seashell waste

Egerić

Smičiklas

Mraković

et al. 2018

J of Chemical Tech & Biotech

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BACKGROUND Seashell waste (SW) is rich in biogenic calcium carbonate and potentially can substitute geological sources in various applications, such as the separation of heavy metals and radionuclides from contaminated solutions. This study aims to compare SW sorption efficiency towards different chemical species (Cu2+, Zn2+, Pb2+ and Sr2+) and to evaluate the effects of various factors based on the experimental data and modeling approach. RESULTS The reaction of SW with aqueous metal solutions is a combination of several processes that result in metal retention, Ca2+ release, and changes in pH. SW demonstrates variable selectivity for investigated cations, depending on their concentrations and reaction times. Maximum sorption capacities declined in the order Zn2+ > Pb2+ ≈ Sr2+ > Cu2+. The model based on general regression neural network (GRNN) architecture was developed, which enabled prediction of removal efficiency taking into account the process specific, metal specific parameters and their non‐linear interactions. Initial concentration and covalent radius of a cation exhibit the highest, while the initial pH the lowest significance. CONCLUSION Ecological problems caused by SW accumulation in coastal areas could be mitigated by mastering technologies for their practical utilization. The results obtained facilitate the understanding of cationic pollutants removal by SW in the range of experimental conditions, while the GRNN approach demonstrates advantages in modeling complex sorption processes. © 2017 Society of Chemical Industry

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Section: Methodsmentioning

confidence: 99%

Section: Grnn Architecturementioning

confidence: 99%

Experimental and theoretical consideration of the factors influencing cationic pollutants retention by seashell waste

Egerić

Smičiklas

Mraković

et al. 2018

J of Chemical Tech & Biotech

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“…Modelling and prediction of UF water and surface water presented in [29] provides an improvement in water production. Several improved models of membrane fouling can be found in [27,30,31] which showed modelling of membrane fouling will be useful in the development of improved fouling control techniques. The efficient use of fouling control strategies can helps to reduce the energy demand and other operational costs associated with fouling.…”

Section: Motivation For Modelling Of Membrane Filtration Processesmentioning

confidence: 99%

Soft computing techniques in modelling of membrane filtration system: a review

Yusuf¹,

Wahab²,

Sudin³

2019

Desalination and Water Treatment

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The complexity of the membrane filtration system is due to membrane fouling problem, which reduce the performance of the system. This is the most challenging issue for further membrane control system design and applications. Pursuant to the understanding of different mechanisms of fouling, various modelling approaches have been explored. Modelling of membrane bioreactor filtration system has been extensively employed for the purpose of predicting dynamic behaviour of the process. The application of soft computing in solving real-life problems has become an upward trend nowadays. This paper reviews on the application of soft computing techniques to model, predict and optimize the membrane bioreactor filtration system. A brief review on membrane filtration process for reverse osmosis and other related solid liquid separation processes is also given. Due to the non-linearity of the filtration process and almost impossible to represent it using standard mathematical equation, the application of artificial intelligent based models such as neural network, knowledge-based, fuzzy system and adaptive neuro-fuzzy is investigated and reviewed. For better prediction of models, the application of soft computing tools in the framework of optimization scheme such as genetic algorithm, particle swarm optimization, gravitational search algorithm and hybrid are also reviewed.

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“…Similarly, ANN has emerged as a powerful tool for understanding complex systems due to its accuracy and computational efficiency [14,15]. Its applications in membrane separation processes, including microfiltration and ultrafiltration, have showcased predictive capabilities for flux, rejection, and separation efficiency in various domains [16][17][18][19]. In dairy processing, ANNs have been used as an efficient method for modelling and simulating the ultrafiltration of milk in cross-flow mode [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

John,

Giri,

Subeesh

et al. 2024

Journal of Food Processing and Preservation

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The growing popularity of soy proteins among vegans and vegetarians, owing to their high protein content and widespread availability, has led to scientific studies on its various extraction methods mainly on ultrafiltration. This research employed artificial neural network (ANN) and Box-Behnken design (BBD) methodologies to predict the process parameters of ultrafiltration for the preparation of soy protein. Using BBD, the optimum process parameters of ultrafiltration were identified via the desirability function approach. The optimized permeate flux was 11.13 litres per hour (LPH) and 85.52% protein content in retentate. The identified ideal process parameters for ultrafiltration to achieve maximal protein retention encompassed a 10 kDa membrane module, a transmembrane pressure of 117 kPa (17 PSI), a volume concentration ratio of 3.5, diafiltration set at 1, and a flow rate of 65% of the pump capacity, exhibiting an absolute percent error value of 2.81. Employing these refined process parameters, the predicted value for protein retentate stood at 80.49%. The predictive accuracy of the model achieved an impressive 99.61% for protein retention. The ANN model effectively predicted the optimal ultrafiltration conditions, resulting in maximal protein retention and a protein content accuracy of 96.41% and 99.61%, respectively.

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Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process

Cited by 18 publications

References 33 publications

Experimental and theoretical consideration of the factors influencing cationic pollutants retention by seashell waste

Experimental and theoretical consideration of the factors influencing cationic pollutants retention by seashell waste

Soft computing techniques in modelling of membrane filtration system: a review

Optimization of Process Parameters for the Production of Soy Protein by Ultrafiltration Using ANN

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