American Journal of Science and Technology 2018; 5(1): 1-13 http://www.aascit.org/journal/ajst ISSN: 2375-3846 +Modelling the Sorption of Zn 2+ Ions onto Luffa cylindrica Oboh Innocent O. 1 , Aluyor Emmanuel O. 2 1 Department of Chemical and Petroleum Engineering, University of Uyo, Uyo, Nigeria 2 Department of Chemical Engineering, University of Benin, Benin City, Nigeria Email address [email protected] (Oboh I. O.) Citation Oboh Innocent O., Aluyor Emmanuel O. +Modelling the Sorption of Zn 2+ Ions onto Luffa cylindrical. American Journal of Science and Technology. Vol. 5, No. 1, 2018, pp. 1-13. Received: April 30, 2017; Accepted: January 8, 2018; Published: February 27, 2018 Abstract: Biosorption experiment for Zn (II) was investigated in this study using the plant material Luffa cylindrica. The applicability of some selected kinetic models was tested. Characterization like the surface area, chemical bonds, bulk density, Pore size distribution, microstructures, composition, morphology and elemental composition were determined. The coefficient of determination (R 2 ) of all the models studied were mostly greater than 0.9. In most cases these coefficients were found to be close to one. This indicates that all the kinetic models adequately describe the experimental data of the biosorption of Zn (II) ions. Kinectic models were developed mathematically, and also, Artificial Neural Network (ANN) was applied to develop a Multiple Input Single Output (MISO) back propagation neural network model which was validated. The RMSE value was found to be 0.5912 and 1.6267 for MISO Zn-1 and MISO Zn-2 respectively. Artificial neural network was able to predict the sorption capacity quite reasonably for the model. Keywords: Artificial Neural Network, Kinectic Model, Luffa cylindrica, Biosorption, Waste Water 1. Introduction Environmental pollution due to the discharge of heavy metals from various industries, including metal plating, mining, painting and agricultural sources such as fertilizers and fungicidal sprays are causing significant concern because of their toxicity and threat to human life, especially when tolerance levels are exceeded [1]. Water contaminated with metal ions can cause several health problems. Heavy metal ions such as cadmium, zinc, nickel, chromium, copper and lead can bio-accumulate to be toxic comounds through the food chain [2]. Zinc is often found in effluents discharged from industries involved in acid mine drainage, galvanizing plants, natural ores and municipal waste water treatment plants and is not biodegradable and travels through the food chain via bioaccumulation. Therefore, there is significant interest regarding zinc removal from waste waters since its toxicity for humans is 100-500 mg/day. World health organization (WHO) recommended the maximum acceptable concentration of zinc in drinking water as 5 mg/L [3]. Activated carbon is the most employed adsorbent for heavy metal removal from aqueous solution and have been well documented in the literature [4-5]. However, the extensive use of activated carbon for metal removal from industrial effluents is expensive [6], limiting its large application for wastewater treatment. Therefore, there is a growing interest in finding new alternative low-cost adsorbents for metal removal from aqueous solution, such as: the residuals of agricultural products [7-8]. Biosorption is an attractive technology which involves sorption of dissolved substances by a biomaterial [9]. Very low cost and environmentally friendly plant materials are now used as biosorbents for the removal of divalent cations from aqueous solutions as the cellulose, hemicelluloses, pectin and lignin present in the cell wall are the most important sorption sites [10]. The structure of Luffa cylindrica for example, is cellulose based [11-12], and the surface of cellulose in contact with water is negatively charged. Metal compounds used in this study will dissolve to give the cationic metal and this will undergo attraction on approaching the anionic Luffa cylindrica structure [13]. However, few researchers have addressed the mathematical modelling of the sorption of metal ions onto
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American Journal of Science and Technology
2018; 5(1): 1-13
http://www.aascit.org/journal/ajst
ISSN: 2375-3846
+Modelling the Sorption of Zn2+ Ions onto Luffa cylindrica
Oboh Innocent O.1, Aluyor Emmanuel O.
2
1Department of Chemical and Petroleum Engineering, University of Uyo, Uyo, Nigeria 2Department of Chemical Engineering, University of Benin, Benin City, Nigeria
Transfer function: Fermi (Min=0.00, Max=1.00, Thld=0.00, Inc=1.00)
Learning rate 0.500.
Figure 11b. Accuracy of prediction of Sorption capacity (mg/g) of Zn (II)
ions onto L. cylindrica for MISO Zn-2 neural network model.
Figure 11c. Scatter plot of the measured sorption capacity and of the
predicted sorption capacity derived from a 12-neuron MISO Zn-2 neural
network model.
A Feed forward back propagation Artificial Neural
Network (ANN) was trained using the training data from the
experimental results. Figure 11c shows the experimental and
ANN predicted Sorption capacity for MISO Zn-2 model
developed for the effect of Luffa cylindrica dose on the
biosorption of Zn(II) ions. The accuracy of the prediction of
the trained ANN was then compared with the actual
measured values. It was observed that ANN gave near
12 Oboh Innocent O. and Aluyor Emmanuel O.: +Modelling the Sorption of Zn2+ Ions onto Luffa cylindrical
accurate prediction for Sorption capacity values.
To have a more precise investigation into the various
models, a regression analysis of outputs and desired targets
was performed as shown in Figure 11c. There was a high
correlation between the predicted values by the ANN model
and the measured values resulted from experimental data.
The correlation coefficient was 0.950 for MISO Zn-2, which
implies that the models succeeded in prediction of the
sorption capacity.
Table 7. The RMSE values for MISO neural network models.
Neural Network model RMSE
MISO Zn-1 0.5912
MISO Zn-2 1.6267
The root mean square error (RMSE) is chosen as indicator
of performance of the networks. The Root Mean Squared
error (RMSE) is calculated using the following formula [38-
39]:
(15)
Generally, the artificial neural network offers the
advantage of being fast, accurate and reliable in the
prediction or approximation affairs, especially when
numerical and mathematical methods fail. There is also a
significant simplicity in using ANN due to its power to deal
with multivariate and complicated problems [40].
The root mean square error (RMSE) of the performance of
ANN MISO model on the experimental data for predicting
Sorption capacity is 0.5912 and 1.6267 for MISO Zn-1 and
MISO Zn-2 models respectively as can be seen in Table 7.
ANN was able to predict the sorption capacity quite
reasonably for all models.
4. Conclusion
A kinetic study was carried out and the experimental data
fitted into Pseudo-first order, Pseudo-second order, Intra-
particle diffusion and Avrami models. This was done using
the nonlinear regression method to obtain the kinetic
parameters.
Kinetic models have been developed and fitted for the
sorption of the divalent metal ions onto L. cylindrica on the
effect of Luffa cylindrica dose and the initial ion
concentration. The results showed sorption for Zn (II) ions
onto L. cylindrica during agitation by suspended shaking; the
process can be described by all kinetic models but pseudo-
second order model based on the assumption that the rate
limiting step may be chemical sorption involving ion
exchange between sorbent and sorbate. The parameter which
has the influence on the kinetics of the sorption reaction was
the sorption equilibrium capacity, qe, a function of initial
metal ion concentration, Luffa cylindrica dose and the nature
of solute ion.
In this study two ANN models were developed, which
were all MISO networks. BPNN models were able to predict
the sorption capacity quite reasonably for the effect of initial
ion concentration and Luffa cylindrica dose for the
biosorption process.
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