Prediction of Cadmium Removal Using an Artificial Neural Network and a Neuro-Fuzzy Technique

Singh, T. N.; Singh, Vishal; Sinha, Shivam

doi:10.1007/s10230-006-0135-1

Cited by 24 publications

(15 citation statements)

References 5 publications

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“…Application of artificial neural network (ANN)-based model simulation has emerged as an effective tool for the same because of its well adaptability in linear/nonlinear simple/complex systems. Earlier published works corroborate the mileages of the ANN-based simulations for the optimization of adsorption of the heavy metal pollutants like Cr, Pb, Cu, and As [36][37][38][39]. Prakash et al [28] have applied five-layer ANN neural network for the prediction of biosorption efficiency of the sawdust for the removal of copper(II) from the wastewater samples.…”

Section: Introductionmentioning

confidence: 55%

“…The batch experiments were conducted at 28 and 38°C for the different doses of CSO-INPs (0.1, 0.3 0.5, and 1 mg/ml) and different pH (2,3,5,8,10) conditions for different time interval (10,20,30,40,50, 60 min). ANN simulation has been carried out to obtain the Cr removal efficiency for different temperatures conditions (20,25,28,30,35,38,40, 45°C) at varying inputs pH (2,3,4,5,6,7,8,9,10), CSO-INPs doses (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 mg/ml), initial pollutant concentrations (10, 15, 20, 25, 30, 35 ppm). Figure 9 indicates that an increase in temperature induces higher Cr removal efficiency.…”

Section: Effect Of Effective Temperature On Percentage Adsorption Effmentioning

confidence: 99%

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An artificial neural network (ANN)-based framework for the Cr removal from the spiked water samples by chitosan oligosaccharide-coated iron oxide nanoparticles

Shukla

Kumar

Prakash

et al. 2017

Nanotechnol. Environ. Eng.

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Chromium contamination in water has become a major concern worldwide due to its adverse health effects. Chitosan oligosaccharide-coated iron oxide nanoparticles (CSO-INPs) were used in the present study for the magnetic separation of chromium from a chromium spiked water samples. Transmission electron microscope-energydispersive X-ray spectroscopy elemental mapping and scanning electron microscope-energy-dispersive X-ray spectroscopy elemental analysis were carried out to confirm the successful removal of the contaminant (total Cr) from the spiked water samples. A feedforward artificial neural network (ANN) model has been developed to predict the optimum efficiency of chromium ions removal from aqueous solution by CSO-INPs. Both the batch experiments and the ANN have been applied to assess the impact of various factors such as pH, nanoparticles dose, temperature, and time influencing the Cr removal efficiency of CSO-INPs. Removal efficiency has been found to be higher at low pH, with higher dose of nanoparticles at higher temperature. The ANN simulation further gives us a set of desired conditions (pH 3, CSO-INPs dose 0.7 mg/ml, temperature 28-30°C, time 60 min) to achieve an optimum Cr removal efficiency of CSO-INPs. Graphical AbstractKeywords Chromium Á Chitosan oligosaccharide Á Iron nanoparticles Á Artificial neural network (ANN)

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Section: Introductionmentioning

confidence: 55%

Section: Effect Of Effective Temperature On Percentage Adsorption Effmentioning

confidence: 99%

An artificial neural network (ANN)-based framework for the Cr removal from the spiked water samples by chitosan oligosaccharide-coated iron oxide nanoparticles

Shukla

Kumar

Prakash

et al. 2017

Nanotechnol. Environ. Eng.

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“…The algorithm has been applied on adsorption of Cadmiu m used by T Singh, V Singh and S Sinha. [22]. The result has been furnished in table 3.…”

Section: Discussionmentioning

confidence: 95%

Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique

Mandal¹,

Sinha²,

Chatterjee³

et al. 2012

IJISA

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Abstract-Clustering is partitioning of data s et into subsets (clusters), so that the data in each subset share some co mmon t rait. In this paper, an algorith m has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmiu m by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimu m and maximu m value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorith m, mod ified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorith m has been tested in prediction of adsorption of cad miu m by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.

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“…In addition to this common approach, the resilient backpropagation neural network can successfully be employed for the prediction of parameters with confidence [13]. On the other hand, as proven in [14], an adaptive neurofuzzy inference system can be more efficient than single layered feed forward artificial neural networks in some specific cases. The following set of equations describes the operation of the BP algorithm [15][16][17]:…”

Section: Artificial Neural Network-based Estimation Of Groundwater Qumentioning

confidence: 99%

ANN-Based Estimation of Groundwater Quality Using a Wireless Water Quality Network

Kılıçaslan

Tuna

Gezer

et al. 2014

International Journal of Distributed Sensor Networks

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Water is essential for life. Considering its importance for humans, it must be periodically analyzed to ensure its quality. In this study, a wireless water quality network is deployed to collect water quality parameters periodically and an artificial neural network-based estimation method is proposed to estimate groundwater quality. Estimating groundwater quality enables the authorities to take immediate actions for ensuring water quality. Compared to traditional water quality analysis methods, the proposed method has the advantage of letting the authorities know the quality of their water resources beforehand. A set of simulation studies given in this paper proves the efficiency and accuracy of the proposed method.

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Prediction of Cadmium Removal Using an Artificial Neural Network and a Neuro-Fuzzy Technique

Cited by 24 publications

References 5 publications

An artificial neural network (ANN)-based framework for the Cr removal from the spiked water samples by chitosan oligosaccharide-coated iron oxide nanoparticles

An artificial neural network (ANN)-based framework for the Cr removal from the spiked water samples by chitosan oligosaccharide-coated iron oxide nanoparticles

Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique

ANN-Based Estimation of Groundwater Quality Using a Wireless Water Quality Network

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