Green synthesis of graphene-like porous carbon (GPC) for the adsorption of pharmaceutical traces from an aquatic system: predicting the performance by machine learning and response surface methodology

This research work aimed to synthesis graphene-like porous carbon (GPC) from walnut shell powder (WSP) via a green approach. The characterisation results indicated that the adsorbents were mesoporous, with large surface area, pore volume and irregular morphology. BET surface area of green GPC NPs was 45 m2/g, pore volumes was 6.94 cm3/g, and average pore diameters was 4.73 nm. The adsorption experiments were also performed using spiked water sample collected from Dhankot Nahar Gurgaon (DNG). The maximum adsorption of Diclofenac (DF), Tetracycline (TC), and Ciprofloxacin (CF) from DNG were 71.4%, 73.9%, and 83.2%, respectively, under optimal conditions. The Langmuir adsorption isotherm indicated maximum adsorption capacities for TC, CF, and DF, i.e., . 40.57 mg/g, 40.68 mg/g, and 83.53 mg/g, respectively. The pseudo-second-order kinetic analysis indicated that chemisorption is the rate-limiting step. Thermodynamic analysis validated that the process is spontaneous and endothermic in nature. Optimal operational conditions for the removal of TC, DF, and CF were also determined using six machine learning models: Linear Regression (LR), Decision Tree (DT), XG Boosting (XB), Random Forest (RF), Multi-layer Perceptron (MLP), and Artificial Neural Networks (ANN). MLP for DF, CF, and LR for TC outperformed among other models and observed R2 values were 0.98, 0.96, and 1.00, respectively, for both the test and training sets. The current investigation demonstrated the effectiveness of the adsorbents in natural water and their economic suitability in industrial applications.