Adsorptive removal of Ciprofloxacin and Amoxicillin from single and binary aqueous systems using acid-activated carbon from Prosopis juliflora.

Adsorptive removal of emerging contaminants like antibiotics from aqueous systems having one or more antibiotics using acid activated carbon have rarely been studied and reported. Current study deals with the adsorptive removal of individual antibiotic species i.e. Ciprofloxacin (CIP) and Amoxicillin (AMX) from single (CIP and AMX) and binary (CIP + AMX) adsorption systems using acid activated carbon prepared from Prosopis juliflora wood (PPJ). Binary adsorption system involved the synergistic and antagonistic influence of one antibiotic over the adsorption of other antibiotic. Physico-chemical alterations of PPJ surface due to acid activation and after adsorption were characterized for any surface modification. Parameters influencing the efficient adsorption of CIP and AMX viz. Initial pH of antibiotic solution, dosage of PPJ, sorbent-sorbate incubation temperature and initial concentration of antibiotic species were optimized. Sorbate-sorbent interaction studies for single system revealed sorbate's monolayer formation over adsorbent's surface and the involvement of chemisorption as verified by Langmuir isotherm model and pseudo-second order model respectively. For single system, Langmuir maximum adsorption capacity of PPJ was 250 mg/g for CIP and 714.29 mg/g for AMX. Meanwhile, competitive Langmuir model was used to investigate adsorption capacity of individual antibiotics in binary system i.e. 370.37 mg/g for CIP and 482.14 mg/g for AMX thus verifying CIP has antagonistic effect on AMX adsorption and AMX has synergistic effect on CIP adsorption on PPJ surface. Recyclability studies verified the PPJ can be used up to 4 cycles and co-existing cationic and anionic salts had minimal effect on the adsorption of antibiotics over PPJ surface. Conclusively PPJ proved efficient in eliminating emerging contaminants like that of antibiotics and thus it can be exploited for other grades of pollutants.

Modeling, experimental validation and optimization of Prosopis juliflora fuelwood pyrolysis in fixed-bed tubular reactor.

This work studied the optimal conditions for pyrolysis of Prosopis juliflora wood in fixed-bed tubular reactor. The optimal conditions are measured by performing pyrolysis experiment with respect to wood properties such as particle size, moisture and pyrolysis condition such as, temperatures, heating rates. Higher solid yield (36.8%) was recorded for a slower heating rate of larger particle size at lower temperatures. Further, higher liquid yield (38.3%) was observed while maintaining high heating rate and temperature. It is observed that with increase in particle size, the yield of char and gas decreases and bio-oil increases. The literature reported biomass pyrolysis kinetic model is validated for Prosopis juliflora wood. The kinetic models are able to predict the performance of fixed-bed tubular reactor in terms of pyrolysis product properties. The validated kinetic model may be used for the design of commercial fixed bed pyrolysis reactor to process Prosopis juliflora wood.

Determination of kinetic parameters in the pyrolysis operation and thermal behavior of Prosopis juliflora using thermogravimetric analysis.

This paper deals with the pyrolysis of Prosopis juliflora fuelwood using thermogravimetric analysis to determine the kinetic parameters at six different heating rates of 2, 5, 10, 15, 20 and 25°C/min. The activation energy of pyrolysis was calculated using different methods, namely Kissinger, Kissinger-Akahira-Sunose, Ozawa-Flynn-Wall and Friedman model and corresponding calculated activation energy were found to be 164.6, 204, 203.2, and 219.3kJ/mol, respectively for each method. The three-pseudo component model was applied to calculate the following three kinetic parameters: activation energy, pre-exponential factor and order of reaction. The experimental results were validated with model prediction for all the six heating rates. The three-pseudo component model is able to predict experimental results much accurately while considering variable order reaction model (n≠1).