Artificial intelligence and multivariate statistics for comprehensive assessment of filamentous bacteria in wastewater treatment plants experiencing sludge bulking

Deepnarain, Nashia; Nasr, Mahmoud; Kumari, Sheena; Stenström, Thor Axel; Reddy, Poovendhree; Pillay, Kriveshin; Bux, Faizal

doi:10.1016/j.eti.2020.100853

Cited by 29 publications

(13 citation statements)

References 43 publications

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“…Compared with the prediction performance of SVI using ANN by Deepnarain et al (2020), the model established in this paper performed better with more appropriate inputs. And most of the reported research used the wastewater treatment processes of A 2 /O and oxidation ditch rather than SBR.…”

Section: Resultsmentioning

confidence: 67%

Prediction of sludge settleability through artificial neural networks with optimized input variables

Zheng

Peng

Xia

et al. 2022

Water & Environment J

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Sludge bulking is a major problem in activated sludge processes. It is of great practically useful to predict the sludge settleability through water quality (influent and effluent) and the operation patterns. In this study, the artificial neural network (ANN) to predict the variation of sludge settleability was established with MLSS, organic loading rate and NH4+‐N loading rate as basic input variables. Additional input variables were optimized through comparing the performance of multilayer perceptron artificial neural network (MLPANN) with different combinations. The results showed that excellent nitrification will improve sludge settleability, and the famine phase would promote the growth of filamentous bacteria. Furthermore, the model performance of MLPANN and long short‐term memory networks (LSTM) were compared by using optimized input variables. The results indicated the MLPANN performed better than LSTM with optimized inputs. This study provided a reference of optimizing variables to predict the variation of sludge settleability in activated sludge process. This study will provide a reference for selecting appropriate variables to predict sludge settleability in activated sludge processes.

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Section: Resultsmentioning

confidence: 67%

Prediction of sludge settleability through artificial neural networks with optimized input variables

Zheng

Peng

Xia

et al. 2022

Water & Environment J

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“…Studies of the last few years are dedicated to exploring more techniques (Martienssen & Adonadaga, 2015;Sheik et al, 2015;Nguyen et al, 2016;Deepnarain et al, 2020;Jiang et al, 2021) confirming that if the in-situ identification and growth rate of filamentous bacteria in the activated sludge can be determined researches can more accurately assess the effect of operating conditions on filament growth and improve the mathematical modeling of bulking. For this purpose, quantification methods of the in-situ growth rate of Sphaerotilus natans (as a model of filamentous bacteria) in activated sludge using the specific 16S rRNA/ rDNA ratio of the species are being explored.…”

Section: Discussionmentioning

confidence: 99%

Untitled

2022

LSJ

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P a g e | 18including Microthrix parvicella, Bogiotae , and Sphaerotilus natans. The effluent in the biological unit was characterized by BOD5/COD ratio of 0.17 and COD removal efficiency of 53.32%. Those results demonstrated that the sewage treatment plant is frequently affected by bulking problems. This study had confirmed the importance of continuous monitoring of different process parameters like Wastewater Biodegradability Index, Dissolved Oxygen, filamentous flora, and Dilute Sludge Volume Index (DSVI) to avoid irreversible damage at the level of a wastewater treatment plant.

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“…Various factors can contribute to the occurrence of sludge bulking, but the primary factor that induced sludge bulking is the excessive proliferation of filamentous microorganisms (Deepnarain et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Sludge bulking remains one of the key challenges in the operation of the wastewater treatment process. Various factors can contribute to the occurrence of sludge bulking, but the primary factor that induced sludge bulking is the excessive proliferation of filamentous microorganisms (Deepnarain et al., 2020). Sludge bulking occurs when the biomass present in the activated sludge process fails to separate from the effluent by gravity settling (Hvala & Kocijan, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improvement of biomass aggregation in sludge bulking by magnetic field application

Zaidi

Zhan

Bahrodin

et al. 2022

Environmental Quality Mgmt

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High biomass aggregation is commonly associated with good solid–liquid separation that can inhibit the occurrence of sludge bulking. The application of magnetic fields in the removal of pollutants has been studied previously but the relation between a magnetic field with biomass aggregation and its effect on pollutant removal efficiency receives very little attention. This present study was conducted to analyze the effect of the magnetic field toward biomass aggregation that can eventually contribute to the good removal performance of the pollutants. Two continuous laboratory‐scale sequencing batch reactors known as Reactor A and Reactor B were set up. Reactor A was equipped with an 88 mT magnetic field while Reactor B acted as the control. Reactor A recorded higher biomass aggregation with the range of 75.0–95.2% compared to Reactor B which possessed mostly medium aggregation with an average of 65.5%. As a result, lower sludge volume index (SVI) was recorded in Reactor A (25.8 ml/g) compared to Reactor B (73.1 ml/g). Likewise, Reactor A showed significantly higher chemical oxygen demand (COD) and ammonia‐nitrogen removal compared to Reactor B. These findings imply that the magnetic field successfully improves biomass aggregation, therefore improving the removal performance under the sludge bulking occurrence.

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Artificial intelligence and multivariate statistics for comprehensive assessment of filamentous bacteria in wastewater treatment plants experiencing sludge bulking

Cited by 29 publications

References 43 publications

Prediction of sludge settleability through artificial neural networks with optimized input variables

Prediction of sludge settleability through artificial neural networks with optimized input variables

Untitled

Improvement of biomass aggregation in sludge bulking by magnetic field application

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