کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6466559 1422965 2017 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Symbiosis of denitrification, anammox and anaerobic pathways - An innovative approach for confiscating the major bottlenecks of anammox process
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
Symbiosis of denitrification, anammox and anaerobic pathways - An innovative approach for confiscating the major bottlenecks of anammox process
چکیده انگلیسی


- Proficient symbiosis was established amongst anammox, anaerobes & denitrifiers.
- Optimal seed ratio to facilitate simultaneous removal of OM and nitrogen was 0.6.
- Synergistic association of communities resulted 97.4% N2 & 95.4% OM removal.
- MLR model predicted NRE with least error of precision (0.89 ± 4.45%).
- A new mathematical model predicted N2 and biogas with least error of precision.

Co-occurrence of organic matter (OM) and nitrogen in industrial and domestic wastewaters is a major obstacle in field scale application of anammox process- a novel technology for biological nitrogen removal. This study explored a new symbiosis amongst different consortium i.e., denitrifiers, anammox and anaerobes, which together minimized the inhibition caused due to OM through its concurrent utilization via other metabolic pathways. This symbiosis was achieved by seeding anaerobic granular sludge in existing anammox hybrid reactors at different seed ratios, i.e., 0.2, 0.4, 0.6 and 0.8. COD/TN and seed ratio played a vital role in controlling the overall dynamics of the three different pathways, i.e., denitrification, anammox and anaerobic degradation. The seed ratio of 0.6 was found ideal for synergistic removal of both OM (95.4%) and nitrogen (97.4%). At higher COD/TN ratio, anaerobes were more prevalent and outcompeted anammox bacteria which showed predominance at lower COD/TN ratio. The process performance data were analysed using multi-linear regression (MLR) and non-linear regression models. Biasness of the models were analysed using t-test which dictated MLR model was unbiased and statistically more precise than non-linear regression model. A new mathematical model coceptualised on the mass balance of substrate predicted N2 and biogas with least error of precision.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemical Engineering Journal - Volume 313, 1 April 2017, Pages 355-363
نویسندگان
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