Artificial Neural Network (ANN) Based Prediction and Modeling of Fe(II) Adsorption from Contaminated Groundwater Using Deccan Hemp stem-Derived Activated Carbon

Iron is a vital element for humans to survive, yet consuming too much of it can be detrimental to one's health. Iron contamination in groundwater is mainly in the form of ferrous iron Fe(II). Adsorption is highly suggested as a commonly utilized, cost-effective, and convenient method for Fe(II) removal. The study reveals that the activated carbon derived from the stem of Deccan hemp (ACDH) is an effective adsorbent for removing Fe(II) from the groundwater sample. Fourier transform infrared, scanning electron microscopy, energy dispersive X-ray, and Brunauer-Emmett-Teller analysis were used to characterize the adsorbent. The Langmuir, Freundlich, Redlich-Peterson and Temkin isotherm models were used to describe the adsorption process and are best fitted to the Langmuir isotherm, with a maximum adsorption capacity of 10.989 mg/g Fe(II) at a 4.0 g/L ACDH dose. The high Fe(II) adsorption capacity makes ACDH an efficient and economical adsorbent material for removing Fe(II) from groundwater. The adsorbent ACDH exhibits second- order adsorption kinetics for the adsorption of Fe(II) with the R2 value of 0.99 for varied Fe(II) initial concentrations. The thermodynamic study verified that the adsorption process is both endothermic and spontaneous, leading to an increasing degree of adsorption. The adsorption of Fe(II) was simulated using the Artificial Neural Network (ANN) technique and accurately predicted the batch process through the use of the Levenberg-Marquardt algorithm. The Artificial neural network (ANN) model correlation R2 values for training, testing and validation are 0.99342, 0.98416 and 0.99579 respectively indicating a close accord of initial raw data with that of ANN prediction. The high Fe(II) adsorption capacity makes ACDH an efficient and economical adsorbent material for removing Fe(II) from groundwater.