Optimizing ranitidine hydrochloride uptake of Parthenium hysterophorus derived N-biochar through response surface methodology and artificial neural network

The present study investigated the feasibility of utilizing Parthenium hysterophorus derived activated N-biochar (PH-ANB) as a potential low cost adsorbent for the effective removal of micro-pollutant and water-soluble cationic pharmaceutical ranitidine hydrochloride (RH) from simulated aqueous system. The structural characteristic features of PH-ANB were analysed using FTIR, SEM, BET and point of zero charge (pHPZC). The process of RH removal was conducted under the influence of varying parameters viz. adsorbent dose (0.01 g-0.1 g), contact time (5 min-180 min), pH (2-10), speed of agitation (40-240 rpm), temperature (293-313 K) and initial RH concentration (25-200 mg L−1) by performing a sequence of single parametric batch sorption experiments. The parametric conditions at which more than 99% removal of RH achieved were: adsorbent dose 0.05 g L−1, agitation speed 120 rpm, pH 2, equilibrium time 90 min and temperature 20 °C. The isothermal Langmuir model was well fitted with the equilibrium adsorption data while kinetic data suggested pseudo second order kinetics. The effects of process parameters on the removal efficiency of RH was optimized following the experimental matrix developed through a 23 full factorial central composite design (CCD) method of response surface methodology (RSM). The ideinvestigational data was then used to train artificial neural network (ANN). Results showed that ANN has superior predictability than RSM in optimization of RH removal. The study suggested that PH-ANB could be a reasonable and promising adsorbent for the elimination of RH from aqueous solution.