In this paper we present results of research on the transformation of chemical forms of two elements (Cu, Zn) that occurred at the highest concentration in sewage sludge being processed in a composting process. The factor that had impact on the direction of the observed transformation was the amount of straw added to the mix with sewage sludge at the batch preparation stage including elimination of an additional source of organic carbon (straw). The analysis of contents of Cu and Zn chemical forms was performed applying Tessiere’s methodology. It was ascertained that reduction of supplementation has positive impact on the allocation of tested elements in organic (IV) and residual (V) fractions with a simultaneous decrease of heavy metals mobile forms share in bioavailable fractions, mostly ion exchangeable (I) and carbonate (II). Using an artificial neural network (ANN), a tool was developed to classify composts based on Austrian standards taking into account only I ÷ IV fractions treated as a labile, potentially bioavailable, part of heavy metals bound in various chemical forms in compost. The independent variables that were predictors in the ANN model were the composting time, C/N, and total content of the given element (total Cu, Zn). The sensitivity coefficients for three applied predictors varied around 1, which proves their significant impact on the final result. Correctness of the predictions of the generated network featuring an MLP 3-5-3 structure for the test set was 100%.

IntroductionConsidering the stochasticity of the volume and quality of the incoming sewage, the operating parameters of reactors need to be controlled within a certain range so that the performance of the respective units of a sewage treatment plant (STP) can be optimized and the desirable pollution reduction effect achieved [1,2]. This requires prediction of various biochemical processes taking place in the reactor, using physical or statistical method for the purpose. The former have the advantage that the qualitative variability of sewage at the discharge from the reactor and the biological reactor's operating parameters is based on differential equation systems. However, for the calibration of physical models, one needs detailed information about the reaction path in the respective units, which requires continuous high-resolution measurements of a number of qualitative parameters of the sewage at the inlet, discharge and inside the reactor, leading to considerable problems in the experimental phase. Moreover, due to the number of model parameters and strong interactions between them, their calibration may often be difficult, as confirmed in multiple Pol. AbstractThis study attempted to develop statistical regression models for predicting the settleability of activated sludge based on the quality of incoming sewage and on the identified dominant filamentous species. As part of the analyses conducted for the purpose, classification models are presented that enable identification of the respective filamentous microorganisms, based on the working parameters of the bioreactor and the quality of the influent. The study calculations demonstrated that the modeling methods based on artificial neural networks, random forests, and boost trees can be applied for the identification of filamentous microorganisms Microthrix parvicella, Nostocoida sp., and Thiotrix sp. in activated sludge chambers in the STP located in Sitkówka-Nowiny. The best predictive capacity, covering identification of the above-mentioned filamentous bacterial species in activated sludge chambers, was observed for statistical models obtained by the random forest method.

The main objective of presented research work was the assessment of the impact of reduced straw content, as organic carbon source, on the course of sewage sludge composting process. During the research work performed in industrial conditions, the composting process going in periodically overturned windrows differing in proportion of dehydrated sludge, straw and structural material being 4:1:1 and 8:1:2 respectively, was observed. The consequence of increase of sludge concentration with relation to straw was decrease of C:N ratio in the input material from 11.5 to 8.5. The following parameters were analyzed as indicators for the assessment of the composting process: contents of fulvic acids (FA), humic acids (HA), lignin, cellulose and hemicellulose as well as absorbance in UV/VIS (λ=280, 465 and 665 nm) range. The results obtained have indicated that the increase of sludge content extends the elevated temperature (T>50°C) period from 42 days to approximately 65 days. Our tests did not confirm that limitation of straw content added to sewage sludge had any adverse effect on the course of composting. PI index (HA/FA), which qualifies the compost as mature in the first case -No 1, exceeds limit value of 3.6 on the 83 rd day whereas, in the second case No 2, on the 48 th day.