The current investigation deals with how chemically activated carbon derived from industrial paper sludge (ACPS) performs on sorptive removal of enrofloxacin (ENF), an antibacterial drug from its water solution. Thermogravimetric (TGA) and proximate analysis of raw paper sludge (RPS) were conducted. ACPS was characterized with proximate analysis, XRD, FT-IR, SEM and BET. The influence of five operational parameters viz. adsorbate concentration (initial), dose of adsorbent, pH, temperature, and contact time on the adsorption of ENF onto ACPS has been conducted using batch experiments. The process of adsorption was optimized through ANN (artificial neural network) in addition to RSM (response surface methodology). The maximum percentage removal (95.85%) was achieved at initial ENF concentration 12 mg/g, adsorbent dose 1.2 g/L, contact duration of 18 h and temperature 20 °C. Kinetic data were best fitted into pseudo-second order kinetic model and adsorption equilibrium study indicates that the adsorption process follows Langmuir isotherm model. Adsorption capacity was noted to have a highest value of 44.44 mg/g. A study on thermodynamics of the adsorption process suggests that it exhibits spontaneity, being essentially exothermic. Cost analysis and reusability study confirm that adsorbent produced from industrial paper sludge is cost-effective and reusable. Therefore, ACPS as adsorbent has potency for removing ENF from aqueous solution.
Keywords: Adsorption; Artificial neural network; Cost estimation; Enrofloxacin; Paper sludge carbon; Response surface methodology.
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