Jan Studziński

Mathematical modeling and computer aided planning of communal sewage networks

In the paper the basic questions connected with modeling of wastewater networks are presented. Methods of modeling basic sewage parameters and appropriate calculation algorithms are described. The problem concerns the gravitational networks divided by nodes into branches and sectors. The nodes are the points of connection of several network segments or branches or the points of changing network parameters as well as of location of sewage inflows to the network. The presented algorithms for networks hydraulic calculation concern sanitary or combined sewage nets. It is assumed that the segments parameters such as shape, canal dimension, bottom slope or roughness are constant. Because of these assumptions all relations considered concern the steady state conditions for the network. The calculation of flow velocities and the filling heights in the segments of the wastewater net are carried out for the known slopes and diameters of the canals.

Estimation of System Structure and Parameters from Noisy Sampled Impulse Response

Abstract A new method for identification of linear systems from impulse response measurements is proposed. It uses the time series anylysis in the first stage, the linear regression in the second, and the exact analytical formulae to calculate the continuous-time model parameters in the third. The method is compared with other known methods of dealing with this problem. Some numerical experience from a real data application is summerized.

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.

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.

Analysis, Modelling and Planning the Communal Sewarage Systems

Abstract: The hydraulic calculations of sewage networks are done ususally by the use of nomograms being the diagrams that show the relation between the main network parameters like pipe diameters, flow rates, hydraulic slopes and flow velocities. In traditional planning of sewage networks the appropriate hydraulic values are read mechanically from the the nomograms. Another way of calculation is the use of professional programs like the SWMM5 hydraulic model and genetic or heuristic optimization algorithms. In the paper still another way of realizing the hydraulic and planning calculations is presented in which the basic hydraulic rules and formulas describing the sewage networks and their functioning are used. The numerical solutions of nonlinear equations resulted from the formulas and describing the main phenomena of sewage flows are used in the paper to solve the tasks of hydraulic calculation and planing of the networks.

An Approach for Planning and Analysis of the Sewage Sanitary Networks Using Some Calculation Formulas and Computer Simulation

The hydraulic calculations are carried out using nomograms, which are the charts connecting diameters, flow rates, hydraulic slopes and average flow velocities. In traditional planning of sewage networks the appropriate hydraulic values are read from the nomogram chart tables. In the paper another way of executing of hydraulic calculations is presented. The numerical solutions of nonlinear equations describing the phenomena of sewage flows are used. The presented method enables the quick analysis of sewage net parameters and opens the possibility of sewage network computer simulation.

Computer Dynamic Modelling of Communal Sewage Networks

In the paper two algorithms of dynamic modeling of communal sewage networks are presented. It is assumed that the hydraulic parameters of segments, namely canal shape, canal dimension and roughness are constant. The goal of the algorithms is to calculate the main sewage network parameters using only the continuity equation and the Manning formula. In the algorithms, fundamental algebraic formulas describing the filling heights in the canals and the sewage flow velocities are also used. The network model based only on the Manning formula and continuity equation in difference form and not on the liquid equations as it is used commonly by modelling the sewage networks is simpler and faster in calculations. While modeling the networks fixed network structure and slowly changing sewage inflows into the canals are assumed. The forecasted inflow values are stated and the investigation presented concerns the sanitary and mixed gravitational sewage networks.

Failures location within water-supply systems by means of neural networks

In the article the neural networks used for failures location for water supply networks are presented. To do this a hydraulic model of the water net, as well as an appropriate developed monitoring system have to be used. The current applications of monitoring systems installed in the waterworks do not realize their possibilities. The monitoring systems provided as autonomic programs to collect and record the information about flows and pressures of water in source pumping stations, in the pump stations bringing up the water pressure inside the water net and in the pipes of water supply network give a general knowledge about state of its work, but if they would be used as elements of IT systems supporting the water network management, they could help to solve the tasks concerning detection and localization of water leaks. The models of failures location in water nets described in the paper are created by means of neural networks in the form of MLP nets.