Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Today, using low cost materials such as agricultural wastes as an adsorbent for heavy metals removal has gained attention in water and waste water treatment. This research aims to produce biochar (a porous material with high carbon content and low density) from three different types of oil pal...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68179/1/fk%202015%20192%20ir.pdf http://psasir.upm.edu.my/id/eprint/68179/ |
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| Summary: | Today, using low cost materials such as agricultural wastes as an adsorbent for heavy
metals removal has gained attention in water and waste water treatment. This
research aims to produce biochar (a porous material with high carbon content and
low density) from three different types of oil palm wastes via pyrolysis process in a
lab scale fixed bed reactor. The raw feed stocks for the pyrolysis experiment include
oil palm frond (OPF), oil palm empty fruit bunches (OPEFB), and oil palm Mesocarp
fiber (OPMF). The synthesized biochars were then characterized for their
physiochemical properties using CHNS elemental analysis, proximate analysis,
scanning electron microscopy (SEM), BET surface area, and Fourier transform
infrared spectroscopy (FTIR).
The adsorption capacity of produced biochars for removing zinc from aqueous
solution was investigated by performing batch adsorption experiments. The result of
batch adsorption experiments showed that oil palm empty fruit bunches biochar
(OPEFBB) had the best efficiency for zinc removal and therefore was chosen for
further optimization study.
The estimation and modeling capacities of two statistical tools; response surface
methodology (RSM) and artificial neural networks (ANNs) in determining and
optimizing the effect of pyrolysis conditions on percentage of yield and adsorption
capacity of OPEFBB toward zinc removal were evaluated. The effect of three
independent variables namely: highest treatment temperature (HTT), heating rate
(HR) and residence time (RT) on OPEFBB percentage of yield and adsorption
capacity were determined. A central composite design was utilized to determine the effect of these factors as well as the interaction of them on responses. Based on
central composite design, two second order regression models were developed for
OPEFBB adsorption capacity and percentage of yield. The optimum actual values for
percentage of yield and adsorption capacity were 25.49% and 15.18mg/g,
respectively, under the predicted conditions of 615°C for HTT, 8°C/min for HR, and
128 minute for RT. The input and output of the RSM design was used in artificial
neural networks for training purpose. The incremental back propagation algorithm
demonstrated the best results and which has been used as learning algorithm for
ANN in combination with Genetic Algorithm in the optimization. The estimated
production conditions to reach the optimum actual values of yield at 25.38% and
adsorption capacity of 15.29mg/g were HTT of 625°C, HR of 9 ̊ C/min and RT of
130 min.
In both RSM and ANN methods, percentage of yield and adsorption capacity of
OPEFBB were mostly influenced by the highest treatment temperature (HTT)
followed by heating rate (HR) and residence time (RT). The performance of RSM
and ANNs were compared in terms of root mean square error (RMSE), coefficient of
determination (R²), and absolute average deviation (AAD). The results demonstrated
that both models fitted the experimental data well; however the predicted values
confirmed that ANN outperformed RSM due to superiority of ANN model in
capturing non linear behavior and better estimating capability rather than RSM.
The batch adsorption experiments for removal of zinc by optimum product were
carried out by determining the impact of solution pH, biochar dosage and heavy
metal concentration on the adsorption process. The results suggest that solution pH is
one of the most important factors influencing the adsorption capacity. At low pHs,
the removal of zinc ions was low due to high concentration of protons in sorption
media and competition of protons with zinc ions for binding sites. By increasing pH,
the removal of zinc showed an upward trend and reached the maximum value at pH6.
After that by rising pH, precipitation and hydroxyl formation occurred which masked
the true adsorption. Biochar dosage and heavy metal concentration also influenced
the removal of zinc and the optimum values were found to be 10 g/l and 80 mg/l
respectively.
Four adsorption isotherms namely: Langmuir, Freundlich, Dubinin–Radushkevich,
and Temkin were applied for modeling the adsorption equilibrium data. Among them
Langmuir isotherm could describe the adsorption data better by coefficient of
determination of 0.9988 and the maximum adsorption capacity was at 19.27 mg/g.
From Dubinin equation, ion exchange mechanism was found to be predominant
mechanism in the adsorption of zinc by OPEFBB. |
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