Decision tree for identification and prediction of filamentous bulking at full-scale activated sludge wastewater treatment plant

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

doi:10.1016/j.psep.2019.02.023

Cited by 46 publications

(18 citation statements)

References 36 publications

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“…The hairlike objects of surface and filamentous fungus were more commonly observed in C/N 5.0 conditions than any other ones [75,76]. The growth of the filamentous fungus has been reported to be observed in activated sludge and AGS and the sludge bulking has been pointed as a reason [77][78][79]. These results may be also explained by decrease in MLSS and the relatively small size of fungus in low C/N ratio condition is deemed to be due to decrease in AGS/MLSS ratio.…”

Section: Extracellular Polymeric Substances Analysismentioning

confidence: 99%

Effects of carbon to nitrogen ratio on the performance and stability of aerobic granular sludge

Kim

Ahn

2020

Environmental Engineering Research

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The purpose of this study was to assess the nutrient salts removal efficiency and stability of the aerobic granular sludge (AGS) by change in C/N (carbon to nitrogen) ratio. The laboratory-scale experiments were performed to analyze the removal efficiencies for organic matter and contents of nitrogen, MLSS, sludge volume index, and extracellular polymeric substances (EPS) under C/N ratio conditions of 5.0, 10.0, 15.0, and 20.0. The microorganisms were observed using optical microscope and the microbial communities were analyzed using pyrosequencing. The increase in C/N ratio from 5.0 to 20.0 increased the organic matter and nitrogen removal efficiency to 95.9 and 79.1%, respectively. For the EPS contents, an influencing factor of granule stability, the polysaccharides to protein (PS/PN) ratio increased from 0.55 to 0.79. For the microbial community, the <i>Thauera</i> was the most common genus in ending phase occupying 63.7%. This microorganism is regarded as one contributing to organic matter degradation and improved production of EPS including AGS of microorganism, thus, may be an explanation of the results of this study such as increase in organic material in AGS and improvement of denitrification efficiency and contents of EPS with increase in C/N ratio.

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Section: Extracellular Polymeric Substances Analysismentioning

confidence: 99%

Effects of carbon to nitrogen ratio on the performance and stability of aerobic granular sludge

Kim

Ahn

2020

Environmental Engineering Research

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“…In all sections, we considered 80% and 20% of the total dataset for training and testing, respectively. This model uses maximum likelihood estimation to evaluate the probability of class membership [61]. & Artificial neural network (ANN) is mainly based on the biological and processing nervous system, which includes a multilayer perceptron and a single hidden layer.…”

Section: And Decision Tree (Dt) Classifies Instances or Examples Intomentioning

confidence: 99%

“…After finding the value of the predicted output, it is compared with the target output. The aim is to reach a low error level and high accuracy for the model [61]. & Random forest (RF) is considered an ensemble classifier that contains a multitude of decision trees.…”

Section: And Decision Tree (Dt) Classifies Instances or Examples Intomentioning

confidence: 99%

“…Multiple trees (in our study trees = 10) grows to the largest extent possible, and there is no pruning; then, in order to classify a new object and predict new data according to features, a classification is taken from each tree to vote for that class. As a result, the class with the most votes is selected [61,62]. Voting is the simplest form of combining individual classification algorithms in which different multiple models and rules are selected.…”

Section: And Decision Tree (Dt) Classifies Instances or Examples Intomentioning

confidence: 99%

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The prediction of mortality influential variables in an intensive care unit: a case study

Khajehali

Tarokh

2021

Pers Ubiquit Comput

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The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

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“…Due to the large amount of information provided by QIA, its combined use with chemometric techniques has become increasingly important in organizing and extracting relevant information from such comprehensive datasets. Thus, different multivariate statistical techniques, including cross-correlation (CC), principal component analysis (PCA), decision trees (DT), partial least squares regression (PLS), and discriminant analysis (DA), among others, were already successfully applied for a number of studies encompassing biological WWT systems monitoring (Amaral, 2003;Deepnarain et al, 2019;Kim et al, 2011;Leal et al, 2016;Mesquita et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessment of an aerobic granular sludge system in the presence of pharmaceutically active compounds by quantitative image analysis and chemometric techniques

Leal

Lopes

Río

et al. 2021

Journal of Environmental Management

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In this study, a sequencing batch reactor (SBR) with aerobic granular sludge (AGS) was operated with synthetic wastewater containing environmental relevant concentrations of 17β-estradiol (E2), 17α-ethinylestradiol (EE2) and sulfamethoxazole (SMX). Despite the presence of the studied PhAC, the granular fraction clearly predominated (TSS gran /TSS ranging from 0.82 to 0.98) throughout the monitoring period, presenting aggregates with high organic fraction (VSS/TSS above 0.83) and good settling characteristics (SVI 5 ranging from 15 to 39 mL/ gTSS). A principal component analysis (PCA) with quantitative image analysis (QIA) based data allowed to distinguish the different operational periods, namely with mature granules (CONT), and the E2, EE2, and SMX feeding periods. It further revealed a positive relationship between the biomass density, sludge settling ability, overall and granular biomass contents, granulation properties, granular biomass fraction and large granules fraction and size. Moreover, a discriminant analysis (DA) allowed to successfully discriminate not only the different operational periods, mainly by using the floccular apparent density, granular stratification and contents data, but also the PhAC presence in samples. The filamentous bacteria contents, sludge settling properties, settling properties stability and granular stratification, structure and contents parameters were found to be crucial for that purpose.

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Decision tree for identification and prediction of filamentous bulking at full-scale activated sludge wastewater treatment plant

Cited by 46 publications

References 36 publications

Effects of carbon to nitrogen ratio on the performance and stability of aerobic granular sludge

Effects of carbon to nitrogen ratio on the performance and stability of aerobic granular sludge

The prediction of mortality influential variables in an intensive care unit: a case study

Assessment of an aerobic granular sludge system in the presence of pharmaceutically active compounds by quantitative image analysis and chemometric techniques

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