Mechanism of purification of low-pollution river water using a modified biological contact oxidation process and artificial neural network modeling
| Autor: | Buxian Yuan, Jibiao Zhang, Jingwei Feng, Aiyong Zhang, Xiangxiang Han, Liu Zhang |
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| Rok vydání: | 2021 |
| Předmět: |
Pollutant
Pollution Denitrification Process Chemistry and Technology media_common.quotation_subject chemistry.chemical_element 02 engineering and technology 010501 environmental sciences 021001 nanoscience & nanotechnology 01 natural sciences Nitrogen Denitrifying bacteria chemistry Anammox Environmental chemistry Chemical Engineering (miscellaneous) Sewage treatment 0210 nano-technology Waste Management and Disposal Effluent 0105 earth and related environmental sciences media_common |
| Zdroj: | Journal of Environmental Chemical Engineering. 9:104832 |
| ISSN: | 2213-3437 |
| DOI: | 10.1016/j.jece.2020.104832 |
| Popis: | A modified biological contact oxidation (MBCO) process was adopted to treat low-pollution river water, mainly contaminated with effluent from a sewage treatment plant, containing nitrate nitrogen as the main form of nitrogen. The effects of varying carbon sources, carbon-to-nitrogen (C/N) ratios and hydraulic retention times on the effectiveness of river water treatment were investigated, as well as the mechanism of pollutant removal. An artificial neural network (ANN) model optimized using a genetic algorithm was established to predict the effluent water quality after MBCO treatment. Results showed that when sodium acetate was applied as the carbon source, the MBCO reactor denitrification effect was optimal, with the denitrification efficiency increasing in accordance with increasing C/N ratio. When sodium acetate was applied as the carbon source at a C/N ratio of 4.5, the relative abundances of Anammox and denitrifying bacteria in the MBCO reactor were highest. These results indicate that these conditions were most favorable for the reproduction of Anammox and denitrifying bacteria, accounting for the improved denitrification effect, achieving a total nitrogen removal efficiency of ≥ 86.8%. A genetic algorithm optimized ANN model was applied, which accurately predicted the effluent pollutant concentration of low-pollution river water treated using the MBCO process. The MBCO process provides an effective alternative for the treatment of low-pollution and low C/N ratio water. |
| Databáze: | OpenAIRE |
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