## 3 Results  
Prior to model construction, a comprehensive statistical analysis was conducted on the datasets obtained from both operational wastewater treatment facilities and the laboratory experiment. The results of the statistical evaluation indicated that the numerical features exhibit varying measurement scales, necessitating data scaling during modeling to ensure uniformity. Additionally, no anomalous values were detected within the datasets, confirming their suitability for modeling purposes. Information from both dataframes was examined to facilitate a comparative analysis. It was observed that the nature of the feature values corresponds between datasets, thereby enabling further progress on the project. To test the hypotheses regarding the equality of sample values of feature ratios between the populations of the laboratory experiment and the operational facilities, additional featureswere introduced. These include the ratio of easily oxidizable organic matter (BOD\({}_{5}\)) to ammonium nitrogen (NH\({}_{4}\)) in the incoming wastewater (C/N ratio) and the efficiency of removal of key pollutants. Overall, all numerical features, both input and target variables, exhibited distributions approaching normality, attributed to the controlled conditions of the experiment. Outliers were present for all features, defined as values lying outside the 1.5 interquartile range. The presence of outliers aligns with the inherent variability in wastewater treatment processes. The measured values fell within the analytical equipment's range and corresponded to the technological scheme and operational conditions of the installations. Figure 3 presents the distribution diagrams of ammonium nitrogen and COD values in the incoming wastewater, illustrating the variability and distribution characteristics of these key parameters. To prevent negative impacts on model training quality due to class imbalance, oversampling of the target categorical feature (filamentous bulking occurrence) was performed as a stratification method. Oversampling was conducted using the tools available in the imbalanced-learn library, specifically the \"RandomOverSampler\" function, to ensure adequate representation of the minority class during model training. The distribution of class values concerning the presence of filamentous bulking after oversampling is illustrated in Figure 4.  
Figure 3: Distribution of predictor values: (**a**) ammonium (influent); (**b**) COD (influent). According to the Shapiro-Wilk statistical test, the distributions of indicator variables--primarily the efficiency of wastewater treatment--are generally not normal (\(p<0.05\)). A visual comparison of the samples was conducted using histograms with a highlighted density. The comparative histograms are presented in Figure 5. Despite deviations from normality, the histograms indicate that many distributions approach normality, and the variances of the samples are nearly equal. Applying Student's \(t\)-test to non-normalized samples yields results with significant assumptions; therefore, a bootstrap analysis with 1000 resamples was conducted. The results supported all null hypotheses regarding the convergence of sample means for all indicator variables. According to the statistical tests and bootstrap analysis, the sample means of the  
Figure 4: Distribution of the target feature of the presence of filamentous bulking in the bioreactor. Figure 5: Comparative histograms of the bioreactors (Lab-scale and Full-scale) operation: (**a**) \(\mathrm{C}/\mathrm{N}\) ratio; (**b**) orthophosphates reduction efficiency [%]; (**c**) COD reduction efficiency [%]; (**d**) ammonium reduction efficiency [%]. populations for the majority of indicator variables are equal (\(p>0.05\)). Consequently, we assumed that the results of laboratory modeling can be applied to the operation of existing wastewater treatment facilities.