Influence of temperature and COD loading on biological nitrification–denitrification process using a trickling filter: an empirical modeling approach

Kanda, Ryo; Kishimoto, Naoyuki; Hinobayashi, Joji; Hashimoto, Tsutomu; Tanaka, Satoru; Murakami, Yoshitaka

doi:10.1007/s41742-017-0008-4

Cited by 13 publications

(2 citation statements)

References 6 publications

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“…Stormwater biofilters can be defined as vegetated vertical infiltration systems that can achieve runoff volumes and contaminant load reductions for urban environments [1]. They have shown promising yet variable removal of fecal microorganisms [2][3][4]. Fecal coliform bacteria are a group of bacteria that are passed through the fecal excrement of humans, livestock, and wildlife and they are the indicator bacteria.…”

Section: Introductionmentioning

confidence: 99%

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

Hin

Chin

Goh

et al. 2022

IIUMEJ

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Fecal coliform removal using stormwater biofilters is an important aspect of stormwater management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R2-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work. ABSTRAK: Penyingkiran kolifom tinja menggunakan turas biologi (bioturas) air hujan merupakan aspek penting dalam pengurusan air hujan. Model yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting. Oleh itu, model rangkaian suapan neural perambatan belakang (FBNN) dan sistem adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri input, iaitu jenis rumput, ketebalan bioturas dan kepekatan awal E. coli, manakala anggaran kepekatan akhir bagi E. coli merupakan hasil pembolehubah. Model ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding model FBNN, kerana ia mempunyai nilai R2 yang lebih tinggi iaitu 0.9874, nilai MAE dan RMSE yang lebih rendah iaitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih kecil sebanyak 14.2%. Oleh itu, model ANFIS yang dicadangkan boleh dijadikan alternatif awal bagi menggantikan keperluan kerja makmal.

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Section: Introductionmentioning

confidence: 99%

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

Hin

Chin

Goh

et al. 2022

IIUMEJ

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“…Nitrogen loading significantly influences NH 4 + and TN removal 14 , which determines wastewater processing capacity. To prevent low nitrogen removal efficiency, it is necessary to set the low nitrogen loading at temperatures lower than 15 °C 15 . Under low nitrogen loading conditions (<15 mg TN/L), functional genes (e.g., amoA, nirS, nxrA, napA, and narG) involved in nitrogen metabolism measured by the quantitative polymerase chain reaction (qPCR) were used to calculate and predict NH 4 + , NO 2 − , and NO 3 − transformation rates ( p < 0.05) at 4 °C 16 .…”

Section: Introductionmentioning

confidence: 99%

Nitrogen loading effects on nitrification and denitrification with functional gene quantity/transcription analysis in biochar packed reactors at 5 °C

Ding

Pan

et al. 2018

Sci Rep

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This study investigated the nitrogen transformation rates of different nitrogen-loading (20, 30, and 50 mg TN/L) biochar packed reactors (C:N:P = 100:5:1) within 125 days at 5 °C. The results showed that high nitrogen loading resulted in an NH4+ (TN) removal efficiency decline from 98% (57%) to 83% (29%), with biochar yielding a higher NH4+, TN and DON removal rate than conventional activated sludge. Moreover, all biochar packed reactors realized a quick start-up by dropping in temperature stage by stage, and the effluent dissolved organic nitrogen (DON) concentrations of R20, R30, and R50 were 0.44 ± 0.18, 0.85 ± 0.35, and 0.66 ± 0.26 mg/L, respectively. The nirS/amoA, nxrA/amoA, and amoA/(narG + napA) were deemed to be the markers of ammonium oxidation rate (SAOR), specific nitrite oxidation rate (SNOR), and specific nitrate reduction rate (SNRR), respectively. Compared with functional gene quantity data, transcription data (mRNA) introduced into stepwise regression analyses agreed well with nitrogen transformation rates. High nitrogen loading also resulted in the cell viability decreased in R50. Nitrogen loadings and operation time both led to a significant variation in cell membrane composition, and unsaturated fatty acids (UFAs) significantly increased in R30 (46.49%) and R50 (36.34%). High-throughput sequencing revealed that nitrogen loadings increased the abundance of nitrifying bacteria (e.g., Nitrospira) and reduced the abundance of denitrifying bacteria (e.g., Nakamurella, Thermomonas, and Zoogloea) through linear discriminant analysis (LDA).

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Phosphorus delays the onset of nitrification during aerobic digestion of aquaculture/aquaponic solid waste

Khiari

Kaluthota

Savidov

2020

Biochemical Engineering Journal

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Influence of temperature and COD loading on biological nitrification–denitrification process using a trickling filter: an empirical modeling approach

Cited by 13 publications

References 6 publications

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

Nitrogen loading effects on nitrification and denitrification with functional gene quantity/transcription analysis in biochar packed reactors at 5 °C

Phosphorus delays the onset of nitrification during aerobic digestion of aquaculture/aquaponic solid waste

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