Comparison of Service Characteristics of Two Town’s WWTP

Jurík, Ľuboš; Kaletová, Tatiana; Sedmakova, Miroslava; Balážová, Petra; Cervenanska, Andrea

doi:10.12911/22998993/69365

Cited by 8 publications

(6 citation statements)

References 8 publications

(6 reference statements)

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“…Within the city, there are food industry plants, the operation of which is seasonal; moreover, in the autumn and spring period, the amount of wastewater generated with their share is greater than in winter. Lower values of wastewater quality indicators recorded in autumn and spring may be due to dilution of wastewater flowing into the treatment plant during intense rainfall events, which is also confirmed by the works of other authors [18,32].…”

Section: Variability Of Wastewater Quantity and Quality Inflow To The...supporting

confidence: 81%

“…The literature review [18,32,36] indicates the lack of analyses related to the study of the impact of selected groups of independent variables (wastewater quality indicators, flows, weather conditions, etc.) on the variability of wastewater quality at the inlet to municipal wastewater treatment plants divided into classes (typical values, minimum, maximum).…”

Section: Selection Of Independent Variables For Classification Modelsmentioning

confidence: 99%

“…The statistical models for forecasting the quality of wastewater at the inflow to a wastewater treatment plant are created on the basis of collected measurement data [24][25][26]. On the basis of the values measured in the last measurements taken, the quality of wastewater is modeled in time t [27][28][29][30] or in the case of autoregressive models-in advance [31][32][33][34][35]. For small wastewater treatment plants, it has been shown that the values of selected wastewater quality indicators, i.e., BOD and COD, can be modeled on the basis of the amount of wastewater flowing into the treatment plant [36].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

et al. 2022

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One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.

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Section: Variability Of Wastewater Quantity and Quality Inflow To The...supporting

confidence: 81%

Section: Selection Of Independent Variables For Classification Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

et al. 2022

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“…It takes the values f(t,z i ) = 1 for the time of determination longer than 24 h, otherwise, it is 0. In the model of interest, it was assumed that the values of the wastewater quality indicators in Equations ( 10), ( 11) may be modeled using statistical models [69,[73][74][75]-for these cases, f(t) = 0.…”

Section: Determining the Values Of Weight Factors And Matrices Of A Methods Selection For Identification Of Sludge Bulkingmentioning

confidence: 99%

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

Szeląg

Drewnowski

Łagód

et al. 2020

Sensors

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The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model—quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.

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“…The sources of phosphorus in wastewater are different. Most phosphorus comes from detergents and human excrements, phosphorus in food and other organic substances [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis and balance of phosphorous removal from wastewater at urban wastewater treatment plant

Jurík

2020

Pollack Periodica

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Abstract:Phosphorus is a major substance that is needed especially for agricultural production or for the industry. At the same time it is an important component of wastewater. At present, the waste management priority is recycling and this requirement is also transferred to wastewater treatment plants. Substances in wastewater can be recovered and utilized. In Europe (in Germany and Austria already legally binding), access to phosphorus-containing sewage treatment is changing. This paper dealt with the issue of phosphorus on the sewage treatment plant in Nitra. There are several industrial areas in Nitra where record major producers in phosphorus production in sewage. The new wastewater treatment plant is built as a mechanicalbiological wastewater treatment plant with simultaneous nitrification and denitrification, sludge regeneration, an anaerobic zone for biological phosphorus removal at the beginning of the process and chemical phosphorus precipitation. The sludge management is anaerobic sludge stabilization with heating and mechanical dewatering of stabilized sludge and gas management. The aim of the work was to document the phosphorus balance in all parts of the wastewater treatment plant - from the inflow of raw water to the outflow of purified water and the production of excess sludge. Balancing quantities in the wastewater treatment plant treatment processes provide information where efficient phosphorus recovery could be possible. The mean daily value of P tot is approximately 122.3 kg/day of these two sources. The mean daily value of P tot is approximately 122.3 kg/day of these two sources. There are also two outflows - drainage of cleaned water to the recipient - the river Nitra - 9.9 kg Ptot/day and Ptot content in sewage sludge - about 120.3 kg Ptot/day - total 130.2 kg Ptot/day.

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Comparison of Service Characteristics of Two Town’s WWTP

Cited by 8 publications

References 8 publications

Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

Analysis and balance of phosphorous removal from wastewater at urban wastewater treatment plant

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