Evaluation of physicochemical parameters influencing bulking episodes in a municipal wastewater treatment plant

Bayo, Javier; Angosto, José M.; Serrano-Aniorte, J.; Pujalte, J. A. Cascales; Fernández-López, Carmen; López-Castellanos, Joaquín

doi:10.2495/wp060521

Cited by 9 publications

(19 citation statements)

References 20 publications

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“…Additionally, Table 2 indicates high and medium correlation between sludge volume index and activated sludge temperature (r =-0.46) and MLSS (r=-0.61). The results obtained (Table 1) are confirmed by investigations conducted by Lou and Zhao [4], Bayo et al [7]. Due to the fact that the value of the sludge volume index alone does not give direct information on the aeration tank loading, in further analyses pollutant loads were introduced (L BOD5 ).…”

Section: Resultssupporting

confidence: 70%

“…To identify the dependence between the variables of concern and activated sludge index, a correlation matrix was constructed based on the measurements of wastewater quantity and quality and the bioreactor parameters. In the analyses, the criterion for the assessment of the impact of the bioreactor operational parameters and influent wastewater quality was the value of the sludge volume index (SVI), equal to SVI lim = 150 cm 3 /g [7]. When the measured value of SVI was greater than SVI lim , it was assigned the value of 1, otherwise it was given the value of 0.…”

Section: Methodsmentioning

confidence: 99%

“…In the latter case, the range of variation of the measurement results provides a basis for a division into classes. The ranges of variation of the parameter under analysis, set by the researcher, correspond to those classes [7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The literature review shows that the logistic regression model was used to analyse the settling capacity. However, the correlation produced by [7] and also [8] did not allow the possibility of correcting the bioreactor technological parameters in order to improve the settling capacity of the activated sludge. In economic, and also medical and social sciences, to analyse binary data the so-called linear discriminant model is used.…”

Section: Introductionmentioning

confidence: 99%

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Application of the selected classification models to the analysis of the settling capacity of the activated sludge – case study

Szeląg

Siwicki

2017

E3S Web Conf.

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Abstract. The study presents the development of classification models for sedimentation of activated sludge using the artificial neural networks (ANN), logistic-regression (RL), and linear discrimination model (LDM). The input consisted of indicators of wastewater quantity and quality (biochemical oxygen demand, chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to the wastewater treatment plant) and operational characteristic of the bioreactor (pH, temperature of activated sludge, mixed liquor suspended solids, concentration of oxygen in the nitrification chamber, amount of PIX dosing).The prediction quality of the developed models was measured with: sensitivity, specificity, and computed errors. The calculations of the sedimentation were performed for sludge volume index (SVI). The results indicate that successful predictions were obtained using ANN, RL and LDM methods, which is supported by the fit of computations to measurement results. The study shows that for the wastewater treatment plant of concern, sedimentation properties can be obtained using only the loads of organic compounds, mixed liquor suspended solids, temperature, pH of activated sludge, concentration of oxygen in the nitrification chamber and amount of PIX dozing. Other analysed variables appear to be statistically insignificant for the sludge volume index.

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Section: Resultssupporting

confidence: 70%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of the selected classification models to the analysis of the settling capacity of the activated sludge – case study

Szeląg

Siwicki

2017

E3S Web Conf.

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“…The distribution of rainfall data may be based on local conditions determining the advection of air masses, which plays a key role in the dynamics of rainfall events. The time range of particular rainfall groups can then be determined based on meteorological, synoptic, and statistical analysis in the periods of high precipitation totals or precipitation intensity in a given area (Llasat, 2001;Rigo and Llasat, 2004;Millán et al, 2005;Langer and Reimer, 2007;Federico et al, 2008;Lazri et al, 2012;Berg and Haerter, 2013). The classification of precipitation proposed by Sumner (1988) for Western Europe can also be used in such analyses.…”

Section: Methodsmentioning

confidence: 99%

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

Szeląg

Suligowski

Studziński

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. One of the key parameters constituting the basis for the operational assessment of stormwater systems is the annual number of storm overflows. Since uncontrolled overflows are a source of pollution washed away from the surface of the catchment area, which leads to imbalanced receiving waters, there is a need for their prognosis and potential reduction. The paper presents a probabilistic model for simulating the annual number of storm overflows. In this model, an innovative solution is to use the logistic regression method to analyze the impact of rainfall genesis on the functioning of a storm overflow (OV) in the example of a catchment located in the city of Kielce (central Poland). The developed model consists of two independent elements. The first element of the model is a synthetic precipitation generator, in which the simulation of rainfall takes into account its genesis resulting from various processes and phenomena occurring in the troposphere. This approach makes it possible to account for the stochastic nature of rainfall in relation to the annual number of events. The second element is the model of logistic regression, which can be used to model the storm overflow resulting from the occurrence of a single rainfall event. The paper confirmed that storm overflow can be modeled based on data on the total rainfall and its duration. An alternative approach was also proposed, providing the possibility of predicting storm overflow only based on the average rainfall intensity. Substantial simplification in the simulation of the phenomenon under study was achieved compared with the works published in this area to date. It is worth noting that the coefficients determined in the logit models have a physical interpretation, and the universal character of these models facilitates their easy adaptation to other examined catchment areas. The calculations made in the paper using the example of the examined catchment allowed for an assessment of the influence of rainfall characteristics (depth, intensity, and duration) of different genesis on the probability of storm overflow. Based on the obtained results, the range of the variability of the average rainfall intensity, which determines the storm overflow, and the annual number of overflows resulting from the occurrence of rain of different genesis were defined. The results are suited for the implementation in the assessment of storm overflows only based on the genetic type of rainfall. The results may be used to develop warning systems in which information about the predicted rainfall genesis is an element of the assessment of the rainwater system and its facilities. This approach is an original solution that has not yet been considered by other researchers. On the other hand, it represents an important simplification and an opportunity to reduce the amount of data to be measured.

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Comparison of random forest and multiple linear regression to model the mass balance of biosolids from a complex biosolids management area

Pluth

Brose

2021

Water Environment Research

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The use of biosolids as a soil amendment provides an important alternative to disposal and can improve soil health; however, distribution for water resource recovery facilities (WRRFs) in the United States can be challenging due to decreasing cropland, increased precipitation, variable plant operations, and financial constraints. Although statistical modeling is commonly used in the water sector, machine learning is still an emerging tool and can provide insights to optimize operations. Random forest (RF), a machine learning model, and multiple linear regression (MLR) were used in this study to model the mass balance of biosolids from a complex biosolids management area. The RF model outperformed (R 2 = 0.89) the MLR model (R 2 = 0.49) and showed that rainfall was a major factor impacting distribution. Storage for dried biosolids would help decouple drying operations from wet weather and increase distribution. This study demonstrated how machine learning can assist in decision-making processes for long-term planning at WRRFs. Practitioner Points• Random forest predicted the 7-day average mass balance of biosolids from a complex biosolids management area.• Decoupling biosolids drying operations from wet weather was identified as the highest operational priority.• Machine learning outperformed multiple linear regression and can be an important tool for the water sector.

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Evaluation of physicochemical parameters influencing bulking episodes in a municipal wastewater treatment plant

Cited by 9 publications

References 20 publications

Application of the selected classification models to the analysis of the settling capacity of the activated sludge – case study

Application of the selected classification models to the analysis of the settling capacity of the activated sludge – case study

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

Comparison of random forest and multiple linear regression to model the mass balance of biosolids from a complex biosolids management area

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