Publication: Adsorption of acetaminophen and Chloramphenicol by corn cob based Activated carbon: experimental and Modelling analysis
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Date
2024-09-01
Authors
Mohamad Razif, Mohd Ramli
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The wastewater containing pharmaceutical compounds has threatened human
health and aquatic life. Previous studies have explored various types of activated
carbon derived from agricultural waste for the adsorption of pharmaceutical
compounds. They often face limitations such as low adsorption capacity, non-
optimized preparation conditions, and the use of non-renewable precursors. So far,
there has been limited research focusing on optimizing the preparation and application
of corn cob based activated carbon specifically for the adsorption of acetaminophen
and chloramphenicol with different molecular structure and characteristics. This study
aims to produce corn cob based activated carbon (CCAC) for the adsorption of
pharmaceutical compounds, namely acetaminophen (ACP) and chloramphenicol (CP).
The CCAC was prepared via a physicochemical activation method and optimized
using response surface methodology. From the analysis of variance (ANOVA), all
developed models have shown significance, with p-values less than 0.05. The
optimum preparation conditions were found to be 3.86 min for activation time, 616
watt of radiation power, and 2.5 g/g for impregnation ratio (IR), which resulted in
16.6% of CCAC’s yield, with adsorption capacities of 22.3 mg/g for ACP and 20.2
mg/g for CP. The CCAC exhibited favourable characteristics in terms of BET surface
area, mesopore surface area, pore volume, and pore diameter, which were 976.29 m
2
/g,
2
631.48 m
/g, 0.3933 cm
3
/g, and 2.38 nm, respectively. CCAC showed adsorption
capacities of 22.43 and 20.68 mg/g, respectively for ACP and CP adsorption at 30 °C. The adsorption of ACP and CP onto CCAC followed Langmuir and Freundlich
isotherms, respectively. For ACP-CCAC and CP-CCAC adsorption system, the kinetic
of adsorption followed a pseudo-second order and pseudo-first order kinetic models,
respectively. Thermodynamic studies confirmed that the ACP-CCAC and CP-CCAC
exhibit endothermic nature. The mass transfer (MT) model indicated the calculated
adsorption capacity of 21.14 mg/g and 21.48 mg/g for adsorption of ACP and CP,
respectively. The artificial neural network (ANN) is applied in this study because of
its ability to accurately model complex, non-linear relationships in the adsorption
process, optimize process parameters, and provide reliable predictions. This
application contributes to a deeper understanding and enhancement of the adsorption
capacity of CCAC for pharmaceutical compounds. The Levenberg-Marquardt (LM)
algorithm was used to train 164 experimental data points input (contact time, initial
concentration, temperature, and pH) into the ANN model to predict adsorption
capacity. The predicted and actual values of the desired output variables achieved an
2
R
above 0.90 for training, validation, and testing. Improvements in mean square error
and test error resulted in the optimal number of neurons in the hidden layers (NHL)
decreasing from 12 to 5 for CP adsorption. However, the optimal NHL remained at 10
neurons for ACP adsorption. The developed framework can predict the adsorption
capacity of adsorbents for adsorbates.