Bartosz Szeląg

Influence of a season on hourly and daily variations in water demand patterns in a rural water supply line – case study

Abstract This article presents the results of a study on hourly and daily variations in water demand patterns, depending on a season. The study was conducted in the years 2014-2015 on a selected rural water supply line. The analysis was based on values of hourly water demand as measured by a water meter coupled with a recording device. The research showed that both the volume and fluctuations in daily water demand were higher in the spring and the summer, versus the autumn and the winter. This was most probably caused by water consumption for additional purposes, specific for rural areas. Individual water demand was the highest in the summer, and the lowest in the winter. Two peaks for hourly water demand were determined for the analyzed seasons. The morning peak always occurred at 7 a.m. on working days, while on days off work it fell at 9 a.m. or 10 a.m., depending on the season. The evening peak always fell at 8 p.m., regardless of a season or a day of a week. On working days, the evening peak was always higher than the morning one, while on days off work the morning peak was higher than the evening one in the autumn and in the winter, and both peaks were the same in the spring and in the summer.

The methods of evaluating storage volume for single-chamber reservoir in urban catchments

Abstract The article presents a method of designing single-chamber rectangular detention reservoirs based on nomographs connecting the parameters and the shape of the inflow with the reservoir hydrograph (triangular, described by the power function and described by the gamma distribution) as well as the hydraulic characteristics of the accumulation chamber and the orifice. The preparation of nomographs involved using the SWMM (Storm Water Management Model) program with the application of numerical calculations’ results of a differential equation for the stormwater volume balance. The performed analyses confirm a high level of similarity between the results of calculating the reservoir volume obtained by using the above mentioned program and using the developed nomographs. The examples of calculations presented in the paper confirm the application aspects of the discussed method of designing the detention reservoir. Moreover, based on the conducted analyses it was concluded that the inflow hydrograph described by the gamma distribution has the greatest impact on the reservoir’s storage volume, whereas the hydrograph whose shape in the rise and recession phases is described by the power function has the smallest effect.

Probabilistic model for the annual number of storm overflow discharges in a stormwater drainage system

Abstract The paper presents an attempt to develop a probabilistic model for predicting an annual number of storm overflow discharges. Forecasting the occurrence of an overflow discharge event involved the application of the logistic regression, which does not require the development of complex hydrodynamic catchment models. The performed calculations showed that the logistic regression model can be successfully used to evaluate the performance of the emergency overflow weir. The resultant logit model eliminates the necessity to develop hydrodynamic models, to conduct continuous measurements of the flow intensity in the stormwater drainage system and to collect detailed information on the characteristics of the subcatchments within the analyzed catchment. The hydrodynamic model was used to simulate the annual number of discharges. The analysis of the results demonstrated that they are in the range of stochastic values, which indicates an application-related character of the method.

The graphic method of sizing pipe reservoir for short, high-intensity rainfalls

Abstract The graphic method of sizing pipe reservoir for short, high-intensity rainfalls. The sizing of a storage reservoir, in order to reduce maximum water discharges in stormwater drainage systems, is one of the major topics in the civil engineering. In this article a methodology that allows identifying an optimal capacity of a pipe reservoir is proposed. Applying a simplifi ed water fl ow model it was possible to bind various reservoir’s parameters (a reservoir diameter, a diameter of an outflow orifice and an outflow coefficient) with an inflow hydrograph (a peak fl ow, a time of rising, a hydrograph fineness, a maximum discharge and a total volume). On the basis of functional relationships the nomographs were elaborated, allowing determining a desirable size of the reservoir, in a sense of a required peak reduction. The usage of the proposed methodology is presented with a simple example. Streszczenie Grafi czna metoda wymiarowania zbiornika rurowego dla krótkotrwałych, intensywnych opadów. Wymiarowanie zbiornika retencyjnego w celu zredukowania maksymalnych odpływów z kanalizacji deszczowej jest jednym z podstawowych zagadnień w inżynierii wodnej i lądowej. W artykule zaprezentowano metody wyznaczania optymalnej pojemności zbiornika rurowego. Zastosowanie uproszczonego modelu przepływu pozwoliło na powiązanie parametrów zbiornika rurowego (średnica komory akumulacyjnej, średnica otworu spustowego i współczynnik wydatku) i hydrogramu dopływu (czas przyboru, przepływ maksymalny podczas kulminacji, objętość fali, współczynnik asymetrii fali). Na podstawie uzyskanej zależności funkcyjnej sporządzono nomogramy pozwalające określić wymiary zbiornika i zapewnić redukcję przepływu maksymalnego na dopływie. Wykorzystanie zaproponowanej metodologii przedstawiono na prostym przykładzie obliczeniowym.

Statistical models to predict discharge overflow

In the paper, a comparison of prediction results concerning the annual number of discharges of stormwater from the drainage system due to stormwater overflows is depicted. The prediction has been computed by means of storm water management model (SWMM) and probabilistic models. Regarding the probabilistic modelling some simple statistical models such as logit, probit, Gompertz and linear discriminant analysis model have been applied, and as for the hydrodynamic modelling a generator of synthetic rainfall based on the Monte Carlo method has been used. The analyses conducted has shown that logit, probit and Gompertz models give outputs that are comparable with the results of hydrodynamic modelling and are concordant with observations. Whereas the annual number of stormwater discharge predicted by the linear discriminant analysis model is significantly lower than the number obtained by hydrodynamic modelling. The calculations made have confirmed the possibility of using statistical models as an alternative for developing labour-consuming and complex hydrodynamic models. The statistical models can be used successfully to predict the stormwater overflows operation provided that the measurements of rainfall in the catchment and of filling the overflow are available.

Modelling and Forecasting the Sludge Bulking in Biological Reactors of Wastewater Treatment Plants by Means of Data Mining Methods

The bulking of active sludge in treatment plant bioreactors occurs very often in communal wastewater works what leads to worsening the abilities of sludge sedimentation and the efficiency of works operation. Because of that there is useful and suitable to model and predict the sludge bulking events in order to take some counteractions. In the paper the data mining methods of Support Vector Machines (SVM), Boosted Trees, Random Forests and Multivariate Adaptive Regression Splines (MARS) have been used for modelling and forecasting the sludge bulking events. By the calculation the measurement data series from 4 years concerning the physical and chemical parameters of wastewater flowing into the treatment plant investigated and the technological parameters of the plant bioreactor were used. The calculation results show that the best sludge bulking model containing the best prediction ability has been received by the MARS method and on another side the worst models have been generated by the Random Forests method.

Sludge Volume Index (SVI) Modelling: Data Mining Approach

In this paper, statistical models to forecast based on the sludge volume index (SVI) with the continuous measurements carried out in the period from 2013 to 2016 for waste water treatment Sitkowka-Nowiny was developed at the same, for two variants of analyses. In the first one, a model of SVI predicting based on the quality indicators of wastewater flowing into the treatment plant, i.e. Biochemical (BOD) and chemical oxygen demand (COD), the content of total nitrogen (TN) and ammonia nitrogen (NH4), total suspended solids, total phosphorus (TP) and the operating parameters of the bioreactor (pH, temperature, oxygen concentration in the nitrification chamber). In the second case, the possibility of replacing individual measurements of the quality of wastewater values calculated on the basis of daily sewage flows to the treatment plant was examined. The above mentioned models statistical analysis was performed using the method of k-nearest neighbor (k-NN), cascading neural network (CNN) and boosted tree (BT). To evaluate the predictive ability of these models the average relative error (MAE) and absolute error (MAPE) were used. The conducted analysis showed that based on the above mentioned indicators of effluent quality and technological parameters of the biological reactor it is possible to modeling of sediment volume index with satisfactory accuracy. In the case under consideration methods of lower values of the prediction error of SVI obtained using a cascade neural networks (MAE = 17.49 ml/g and MAPE = 9.80%) than for the method k-nearest neighbor (MAE = 27.85 ml/g and MAPE = 14.50%). Furthermore, based on the performed simulation, it was found that it is possible to model the analyzed work of the quality of waste water on the basis of the daily flow with reasonable accuracy, it is confirmed by the calculated value of the average and absolute and relative error, and the better ability predictive characterized by the models obtained on the basis CNN than k-NN. In examined cases, the MAP in a set of validation did not exceed 10.13%. The simulation results of quality indicators obtained by CNN were substituted in place of the explanatory variables of sludge volume index in the model for prediction index of sediment and conducted simulations SVI, set out the error MAE = 25.15 ml/g and MAPE = 15.26%. On this basis, it is possible to replace the measured values of the quality of the results of their simulation, thereby reducing the cost of testing, but also gives you continuous control of SVI and adjustments discussed in this work of technological parameters of the biological reactor.

Application of selected methods of black box for modelling the settleability process in wastewater treatment plant

Abstract The paper described how the results of measurements of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plant (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods, namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF + SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

Abstract The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

Preliminary Assessment of Silting and The Quality of Bottom Sediments in A Small Water Reservoir

Abstract The aim of this study was to assess the degree of silting and pollution of bottom sediments in a small water reservoir Lubianka situated in Starachowice, Świętokrzyskie Province, with selected heavy metals (Pb, Cr, Cd, Cu, Ni, Zn, Fe, Mn, Hg). Catchment basin of the reservoir is forested in 92%. Other parts are covered by estates of detached houses, barren lands and green areas. Bathymetric measurements and analyses of trace elements in bottom sediments were made in 2012. After 28 years of exploitation, reservoirs basin accumulated 43 thousand cubic metres of sediments i.e. 4.7% of its initial volume. Mean annual silting rate was 0.17%. Due to the content of copper and chromium, bottom sediments were classified to the II category (sediments of average pollution) according to geochemical standards. Concentrations of Pb, Cd and Hg in all analysed samples were below geochemical background. In a sample collected at the inlet to the reservoir, the TEL index for chromium was exceeded by 25.6%. In other samples the threshold values of the TEL and PEL indices were not exceeded.