Equilibrium Studies for ion of Zinc Onto Gallus Domestic Us Shell Powder

8/3/2019 Equilibrium Studies for ion of Zinc Onto Gallus Domestic Us Shell Powder

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EQUILIBRIUM STUDIES FOR BIOSORPTION OF ZINC ONTO GALLUS

DOMESTICUS SHELL POWDER

Document by:Bharadwaj

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ABSTRACT

Applications of statistical optimization techniques like artificial neural networks were

rarely applied for biosorption systems. Statistically based experimental designs like

response surface methodology (Artificial neural networks) are more efficient, as variables

are tested simultaneously. Moreover, the interactions between different variables can be

estimated. The present study aims to evaluate the efficiency of zinc sorption on gallus

domesticus using Artificial intelligence techniques. Four process parameters (initial

concentrations, pH, Biosorbent dosage and biosorbents particle size) served as inputs to

the neural network models, and percentage biosorption of zinc served as a single outputof each model. Genetic algorithms were used to optimize the input space of the neural

network models to monitor the zinc sorption on Gallus domesticus. About 0.1g ofgallus

domesticus was found to be enough to remove 80.51% of zinc of 20mg/l from 30 ml

aqueous solution in 30 min. RSM (ANN) were carried out to obtain response surface

model describing zinc biosorption at various process conditions: initial concentration (20-

100), pH (2-6), Biosorbent dosage (0.1-0.5) and biosorbent particle size (75-212).

INTRODUCTION

Heavy metals are released into aquatic ecosystems as by-products from various industrial

processes and acid-mine drainage residues. They are highly toxic in ionic form as well as

compound form. They are soluble in water and may be rapidly absorbed by the living

organisms. Zinc is one of the heavy metal enters the environment as the result of mining,
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purifying of zinc, lead, and cadmium ores, steel production, coal burning, and burning of

wastes. Most of the zinc in lakes or rivers settles at the bottom. However, a small amount

may remain either dissolved in water or as fine suspended particles, dissolved zinc in

water may increase as the acidity of water. The high levels of zinc affect human

reproduction or cause birth defect, skin irritation.

A wide range of work had been reviewed for removal of toxic metals, by various methods

such as chemical precipitation, ion exchange, Reverse osmosis, Electro dialysis and

adsorption.Biosorption is potentially an attractive technology for treatment of wastewater

for retaining heavy metals from dilute solutions. Literature shows that the many

biosorbents present in the nature have great capacity for removal of heavy metals.

Biosrptions greatly varies with temperature, pH, adsorbent dosage, temperature, size ofbiosorbent and substrate dosage.

Response surface methodology combines statistical experimental designs and empirical

model building by regression for the purpose of process or product optimization. An

artificial neural network (ANN) is a mathematical representation of the neurological

functioning of a brain. A typical artificial neural network has an input layer, one or more

hidden layer, and an output layer. The neurons in the hidden layer, which are linked to

the neurons in the input and output layers by adjustable weights, enable the network to

compute complex associations between the input and output variables. The inputs of each

neuron in the hidden and output layers are summed and the resulting summation is

processed by an activation function (Nagata et al. 2003). Artificial intelligence techniques

can be effectively integrated to create a powerful tool for process modeling and

optimization. The present study aims to monitor the zinc sorption on gallus domesticus

by generating response surface plots using ANN technique at various pH, initial

concentrations, biosorbent dosage and biosorbent particle size.

MATERIAL AND METHODS

Preparation of biosorbent


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Hen egg shells were collected from MS Ramaiah Engineering hostels, MSRIT,

Bangalore, Karnataka, India. Shells were washed with deionized water several times to

remove dirt particles. The dried egg shells powders of 75-212 m particle size were used

as biosorbent without any pretreatment for zinc adsorption.

Chemical

Analytical grades of ZnSO4 7H2O, HCl and NaOH were purchased from Merck, India.

Zinc ions were prepared by dissolving its corresponding sulphate salt in distilled water.

The pH of solutions was adjusted with 0.1 N HCl and NaOH.

Biosorption experiments

Biosorption experiments were performed in a rotary shaker at 180 rpm using 250 ml

Erlenmeyer flasks containing 30 ml of different zinc concentrations. After one hour of

contact (according to the preliminary sorption dynamics tests), with 0.1 g egg shell

powder biomass, equilibrium was reached and the reaction mixture was centrifuged for 5

min. The metal content in the supernatant was determined using Atomic Absorption

Spectrophotometer (GBC Avanta Ver 1.32, Australia) after filtering the adsorbent with

whatman filter paper. The pH of the solution was adjusted by using 0.1 N HCl and 0.1 N

NaOH. (Kalyani et al. 2009).

ARTIFICIAL NEURAL NETWORK

The first step in implementing a neural network modeling approach is to design the

topology of the network. The choice of design parameters for a neural network is thus

often the result of empirical rules combined with trial and error. The configuration of the

neural networks developed in this work (a 4-10-1 structure: four input neurons-ten

neurons in one hidden layer-one output neuron) was determined after brief

experimentation. The data set comprising 19 experimental runs reported was split into

two categories: a training set comprising 15 experimental runs was used to optimize the


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weights of the neural networks and a validation set comprising 3 experimental runs was

used to evaluate their predictive capability. Because empirical models like neural

networks do not extrapolate data well, data for network training should be selected

carefully if the best results are to be achieved. In this study the data selected for network

training covered the lower and upper bounds of the two output neurons (Nagata et al.

2003). MATLAB software (Version 6.5, MathWorks, Inc, USA) used for this study.

Table 1: Experimental data fed to Matlab software for performing Artificial Neural

Networks based on preliminary studies

S. No. Biosorbant

Concentration

(g/100ml)

pH Metal Concentration

(mg/l)

Particle

size (m)

% Biosorption

1 0.1 2 20 145 29.12

2 0.1 3 20 145 52.143 0.1 4 20 145 77.14

4 0.1 5 20 145 81.41

5 0.1 6 20 145 87.31

6 0.1 6 20 75 86.517 0.1 6 20 105 85.54

8 0.1 6 20 150 80.12

9 0.1 6 20 212 77.4510 0.1 6 20 145 86.51

11 0.2 6 20 145 87.56

12 0.3 6 20 145 89.78

13 0.4 6 20 145 92.7414 0.5 6 20 145 95.24

15 0.1 6 20 145 86.3316 0.1 6 40 145 84.45

17 0.1 6 60 145 83.20

18 0.1 6 80 145 82.02

19 0.1 6 100 145 73.41

Response surface plots showing % adsorption at various concentrations, biosorbant

dosage, pH and initial concentration. The contour plots given in Figures 1-6 show the

relative effects of any two variables when the concentration of the third variable ismaintained at a constant level. These constant levels are the central levels of each

variable taken in the respective ranges considered. In all cases, the contours were more or

less spherical and there were no saddle points. An idea of the approximate ranges of the

three factors, which could result in maximum synthesis of pectinase under these

conditions, was obtained from the contour plots. The coordinates of the central point


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within the highest contour level in each of these figures will correspond to the optimum

concentrations of the respective components.

Fig 1a shows the relative effect of biosorbent concentration and zinc concentration on

percentage adsorption of zinc. The contours in this figure are plotted for a constant

adsorbent particle size (145 m) and pH (6). The highest contour level in this figure

corresponds to 80% biosorption. The relative effect of Zinc concentration and adsorbant

particle size is considered at a constant biosorbent concentration (0.1 g) and pH (6) (Fig.

1b). At a constant metal concentration (20 mg/ml) and particle size (145 m), Fig. 1c

describes the effect of pH and Biosorption concentration on percentage Biosorption. Fig

1d shows the effect of particle size and pH on percentage Biosorption of zinc at constant

zinc concentration (20 mg/ml) and biosorbent concentration (0.1 g). Fig 1e and Fig 1f

describes the effect of particle size and biosorbent concentration & biosorbent

concentration and zinc concentration on percentage Biosorption at constant pH (6) and

zinc concentration (20 mg/ml) & particle size (145 m) and pH (6).

(a) (b)


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(c) (d)

(e) (f)

Fig 1: Response surface plots, drawn using trainlm function in Matlab software

At a constant corn concentration of 18.5 kg/m3, Fig. 3 gives the synthesis of pectinase as

a function of ammonium sulphate and glucose levels. In this case, the coordinates of the

central point within the contour level of 1.65 U corresponds to about kg/m3 of

ammonium sulphate and about 31 kg/m3 of glucose.

CONCLUSION

This work found that neural networks provided better fits to experimental data than

Conventional biosorption equilibrium studies (Like Langumuir, Freundlich) The input

space of a neural network model can be optimized using genetic algorithms which do not

require the objective function to be continuous or differentiable. The hybrid neural

network-genetic algorithm approach described in this work serves as a viable alternative

to the standard approach for the modeling of biosorption processes.


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REFERENCES

1. Nagata Yuko, Chu KH Optimization of a fermentation medium using neural

networks and genetic algorithms.Biotechnology Letters25: 18371842(2003).

2. Kalyani1, G. Babu Rao, B. Vijaya Saradhi ,Prasanna Kumar Y Equilibrium and

kinetic studies on biosorption of zinc onto gallus domesticus shell powder, ARPN

Journal of Engineering and Applied Sciences 4(1):39-49(2009)

3. S. Mahesh kumar, G M. Madhu, M. A. Lourdu Antony Raj, Adsorption isotherms

and brekthrouh curves for phenol on granular activated carbon in fixed beds,

Indian Chem. Engg., Section A, Vol., 4 234-239, 2004.

4. S.R. Nair, T. Panda (1997) Statistical optimization of medium components for

improved synthesis of pectinase by Aspergillus niger, Bioprocess Engineering

16:169-173

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Equilibrium Studies for ion of Zinc Onto Gallus Domestic Us Shell Powder