Prediction of methyl orange removal by iron decorated activated carbon using an artificial neural network

Abstract

Date Stones were used as a bio-degradable waste source for preparing iron impregnated activated carbon. The prepared activated carbon-containing oxides of iron were characterized using SEM, XRD, FTIR, and BET. The specific surface area of the iron decorated activated carbon was 738.65 m 2 /g. The XRD confirmed the presence of magnetite and hematite while the SEM images assured the presence of pores. The prepared activated carbon was used to remove methyl orange from wastewater. Genetic Algorithm was used to develop a model which could predict the removal efficiency of the dye. The ANN model was validated and the effect of different parameters like adsorbent dosage (0.1-1g/L), initial dye concentration (2-20 mg/L), pH (2-11), time (10-55 minutes) and temperature (30-75°C) was estimated both experimentally and predicted using the model. The adsorption process follows the Freundlich isotherm and pseudosecond-order kinetic model. The values of 1/n and KF obtained from the Freundlich isotherm designate good adsorption capacity. Both experimental and model-predicted data agrees with the kinetic model. The adsorption rate is proportionate to the square of the number of vacant adsorption sites. From the thermodynamic study, the positive worth of ΔH° indicates the energyabsorbing nature of the surface assimilation method and the process is endothermic in nature.The low values of each ΔG° (-200 to 0 kJ/mol) and ΔH° correspond to physical surface assimilation. A positive worth of ΔS° reflects the inflated randomness at the solid-aqueous interface with some structural changes in adsorbate and adsorbent.

Development of Predictive Models for Activated Carbon Synthesis from Different Biomass for CO2 Adsorption Using Artificial Neural Networks

“…18,29 Chemical activation can be done by acidic (ZnCl 2 , FeCl 3 , H 2 SO 4 ) or basic (KOH and NaOH) activators. 18,30 Therefore, the adsorption capacity of CO 2 is determined by factors such as carbon surface acidity−basicity, AC hydrophobicity−hydrophilicity, high BET, porosity distribution and order, isosteric heat of adsorption value (Q st ), structural elements such as nitrogen, and the presence of micropores with a size of less than 1 nm. 2,3, 21,22,31 Since laboratory experiments are timeconsuming and difficult, a mathematical prediction model is recommended instead.…”

“…2929 Mitra et al generated an ANN that could estimate the dye removal performance using a variety of parameters. 30 Li et al presented a neural network model that predicts the CO 2 capture rate using variables such as inlet flue gas flow rate and CO 2 concentration in the inlet flue gas. 38 A summary of the research on different ANN models using the MLP structure for different adsorbents and absorbents has been illustrated in Figure 1.…”

“…As a result, various methods must be used to reduce greenhouse effects, acid rain formation, and climate change by capturing or separating this anthropogenic polar gas from other nonpolar gases. − The separation and capture of post-combustion CO 2 from flue gas is accomplished primarily by the use of amine solutions, solid adsorbents, and membranes. Solid sorbents are very competitive for CO 2 capture because of the advantages they have, such as high adsorption capability, low cost, ease of operation, excellent selectivity, and low environmental effects. , Up to now, a variety of solid physical adsorbents, such as mesoporous silicates, metal–organic frameworks (MOFs), carbon nanotubes (CNTs), porous polymers, metal oxyhydroxide-biochar composites, graphene, and others have been developed to substitute traditional alkanolamines in order to mitigate the drawbacks of the latter, such as corrosion, propensity for amine losses, expensive regeneration, and high energy intensity. , Activated carbon (AC) has piqued interest as a promising CO 2 adsorbent for large-scale use due to its low disposal cost, easy accessibility, thermal stability and conductivity, high specific surface area (BET), well-developed micropores and mesopores, tailored porous structure, high adsorption, and rapid regeneration due to weak interactions. − ACs are traditionally made by the pyrolysis of artificial material like titanium carbide, sodium alginate, or natural material (biomass, and cheap agricultural and forestry wastes) such as algae, celtuce leaves, and olive stones using various synthesis methods. , The choice of an appropriate precursor for pyrolysis of porous carbon is important for the final product’s porous texture, which is determined by a variety of factors, including scalability, cost, availability, non-hazardous nature, a high percentage of carbon in the precursor with low ash, and the presence of enough volatiles to produce porosity. ,,, Chemical and physical activation are the two most popular traditional approaches for activating/pyrolyzing a variety of carbonaceous biomass. , The pyrolysis of a natural precursor produces coal, which is then activated by physical (air, N 2 , O 2 , NH 3 , CO 2 , or steam) or chemical agents to generate porosity and increase the textural features of small pores (meso- and micropores). , Chemical activation can be done by acidic (ZnCl 2 , FeCl 3 , H 2 SO 4 ) or basic (KOH and NaOH) activators. , Therefore, the adsorption capacity of CO 2 is determined by factors such as carbon surface acidity–basicity, AC hydrophobicity–hydrophilicity, high BET, porosity distribution and order, isosteric heat of adsorpt...…”

“…Souza et al developed an ANN to model and investigate the adsorption of industrial AC in aqueous solutions for the removal of Ni 2+ Mitra et al generated an ANN that could estimate the dye removal performance using a variety of parameters . Li et al presented a neural network model that predicts the CO 2 capture rate using variables such as inlet flue gas flow rate and CO 2 concentration in the inlet flue gas .…”

“…Summary of different ANN models’ research analysis using the MLP structure for AC as the adsorbent. ,,,− …”