Application of surface response method (RSM) to optimize ammonia nitrogen removal from fresh leachate using combination of ultrasound and ultraviolet
Author(s) -
Mohammad Hasan Zarghi,
Neamat Jaafarzadeh,
Aliakbar Roudbari,
Amir Zahedi
Publication year - 2020
Publication title -
water science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.406
H-Index - 137
eISSN - 1996-9732
pISSN - 0273-1223
DOI - 10.2166/wst.2020.116
Subject(s) - leachate , response surface methodology , nitrogen , ammonia , central composite design , irradiation , ultraviolet , pulp and paper industry , chemistry , environmental engineering , environmental chemistry , environmental science , materials science , chromatography , biochemistry , physics , optoelectronics , organic chemistry , nuclear physics , engineering
Ammonia nitrogen levels are very high in leachate. This study was conducted to optimize the removal of ammonia nitrogen from fresh landfill leachate using a combination of ultrasound waves and ultraviolet irradiation. A sample of fresh landfill leachate was obtained from a municipal landfill site, located in Shahroud (Semnan, Iran) and its ammonia nitrogen was measured by spectrophotometric method. Ultrasound and ultraviolet irradiation were simultaneously used to remove ammonia nitrogen. Box–Behnken design (BBD) based on response surface method (RSM) was applied to analyze and optimize ammonia nitrogen removal by different variables, including pH, contact time, ultrasound frequency and UV intensity. Based on this method, 29 samples with three replications were tested. The analysis of variance indicated quadratic model was significant for removal of ammonia nitrogen from leachate. According to the model, 99.7% removal efficiency (%) of ammonia nitrogen was obtained in the optimal conditions (pH at 9.7, contact time of 59.1 min, ultrasound frequency of 54 kHz and UV intensity of 40 W). The removal efficiency of ammonia nitrogen was obtained 98.6% from the laboratory experiment in these conditions, which agrees well with the predicted response value.
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