Treatment of leachate from an exploited since 2004 landfill by using two methods of advanced oxidation processes was performed. Fenton’s reagent with two different doses of hydrogen peroxide and iron and UV/H2O2 process was applied. The removal efficiency of biochemically oxidizable organic compounds (BOD5), chemically oxidizable compounds using potassium dichromate (CODCr) and nutrient (nitrogen and phosphorus) was examined. Studies have shown that the greatest degree of organic compounds removal expressed as a BOD5 index and CODCr index were obtained when Fenton’s reagent with greater dose of hydrogen peroxide was used - efficiency was respectively 72.0% and 69.8%. Moreover, in this case there was observed an increase in the value of ratio of BOD5/CODCr in treated leachate in comparison with raw leachate. Application of Fenton’s reagent for leachate treatment also allowed for more effective removal of nutrients in comparison with the UV/H2O2 process.
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.