The primary objective of the present study was to determine the seasonal dynamics of ciliates in activated sludge. Studies were carried out in order to verify the hypothesis that fertility of a habitat may significantly influence the seasonal dynamics of the abundance of ciliates, as well as the number and intensity of correlations between physic-chemical parameters and ciliates. It seems that the values of numbers of ciliates were seasonally changeable. The highest numbers of ciliates were found in spring and summer, however the lowest numbers of ciliate communities were noted in winter. The studies showed that protozoa community is determined by ammonia mainly in summer. In spring and winter additional factors may be important. Probably suspended solid, total organic carbon and concentration of appropriate food (bacteria and flagellates) are the major regulator of abundance of ciliates.
The aim of the research was the evaluation of wastewater management in terms of stability and efficiency of wastewater treatment, using statistical quality control. For this purpose, the analysis of the operation and operation of the “Kujawy” Sewage Treatment Plant was made, which is one of the most important and largest sewage management facilities in the city of Cracow. This assessment was done using control charts x for 59 observations. The analysed research period covered the multi-year from 2012 to 2016. Five key pollutant indicators were used to evaluate the work of the tested object: BOD5, CODCr, total suspension, total nitrogen and total phosphorus. In the case of the majority of them, based on the analysis of control charts, full stability of their removal was found in the tested sewage management facility. The exception was total nitrogen, for which periods of disturbed stability of its disposal processes were noted. Analysis of the effectiveness of wastewater treatment showed each time that the required efficiency of reduction of the analysed pollution indicators in the “Kujawy” Sewage Treatment Plant was achieved.
The paper presents preliminary results of investigations on a relationship between turbidity and other quality parameters in the SBR plant effluent. The laboratory tests demonstrated a high correlation between an effluent turbidity and a total suspended solids (TSS) concentration as well as between TSS and COD. Such a relationship would help to continuously monitor and control quality of a wastewater discharge using turbidity measurement.
The aim of this study was to determine the impact of the temperature of wastewater in a biological reactor with activated sludge and the BOD5/N-NH4 ratio in the inﬂuent to the treatment plant on nitriﬁcation efﬁciency and the concentration of ammonium nitrogen in treated wastewater. Tests were carried out in a household wastewater treatment plant which collects and treats sewage from a school building and a teacher’s house. During the 3-year study, large ﬂuctuations in the sewage temperature in bioreactor were noted which was closely related to the ambient temperature. There were also large ﬂuctuations in the concentration of organic matter and the concentration of ammonium nitrogen in inﬂowing sewage. The inﬂuence of wastewater temperature in the bioreactor and the BOD5/N-NH4 ratio on the concentration of ammonium nitrogen in treated wastewater was determined using Pearson’s linear correlation. A statistical analysis showed that a 1°C decrease in the temperature of wastewater in the bioreactor increased the concentration of ammonium nitrogen in treated wastewater by 2.64 mgN-NH4·L-1. Moreover, it was found that nitriﬁcation depended on the ratio of BOD5 to the concentration of ammonium nitrogen in wastewater ﬂowing into the bioreactor. An increase in the BOD5/N-NH4 ratio by 1 value led to a 5.41 mgN-NH4·L-1 decrease in the concentration of ammonium nitrogen.
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.