## 1 Introduction  
Wastewater treatment is a critical human activity, directly influencing the sustainable development of a comfortable living environment. It is essential to all types of industrial processes and is a key infrastructure component in communities of any size. Despite significant capital investments, consistent improvements in wastewater treatment quality are not always achieved. Recent studies on existing treatment facilities reveal that inadequate performance is often linked to operational errors. Modern wastewater treatment plants are equipped with sufficient automation systems, enabling the control of complex processes under normal operating conditions. However, because wastewater treatment relies primarily on biological treatment facilities, which function as finely tuned bioreactors, operating under quasi-stationary conditions, even minor deviations in one of the many technological parameters can lead to emergencies. The shortage of qualified personnel in the industry often results in delayed responses to emerging technological issues, causing instability in the biological system, a decline in treatment quality, and potential system failure. Such situations pose direct risks of technological, environmental, and sanitary disasters, leading to substantial financial losses for both the state and operating organizations. In recent years, research on the predictive modeling of wastewater treatment plant operations has gained considerable attention worldwide. These studies aim to forecast treated wastewater quality and to predict abnormal and emergency situations at treatment facilities. Various model classes are being explored to achieve these objectives. Regression models focus on implementing \"soft sensors\"--models designed to replace physical sensors at treatment plants to reduce costs. [PERSON] [1] used machine learning models to refine rates of nitrogen and phosphorus removal from wastewater, using activated sludge species composition as input parameters. The XGBoost model demonstrated a determination coefficient (DC) of over 0.8. For the dataset, the authors suggest using 16S rRNA sequencing. This study is notable for its approach to predictor selection, as similar studies have not been conducted previously due to insufficient data. However, a drawback of this approach is its technical complexity, as very few wastewater treatment plants are equipped to perform regular PCR tests for detecting indicator microorganisms. For broader practical relevance, it is recommended to consider more accessible characteristics as predictors. [PERSON] [2] investigated the impact of operational characteristics at the Umea wastewater treatment plant (Sweden) on the efficiency of suspended solids and orthophosphate removal. The dataset included 105,763 entries across 32 predictors. The primary outcome of the study was a Variable Importance Measure (VIM) analysis, which allowed the authors to form several hypotheses regarding technological dependencies at the plant. A limitation of this approach is that the model, based on such assumptions, functions as a \"black box\", presenting certain challenges during the technological development phase. Nevertheless, the authors address the critical issue of time lags associated with treatment processes and their effect on model performance. Approaches to handling wastewater treatment duration at various stages should be adopted in future research. For example, a similar approach is used in the work of [PERSON] [3], which focuses on developing a new Long Short-Term Memory (LSTM) model. In this study, a model was developed to predict wastewater treatment quality using a \"soft sensor\", based on data from inexpensive and limited physical sensors, while accounting for the time lag in wastewater treatment duration. The Mean Absolute Percentage Error (MAPE) for total nitrogen removal in this model was 2.3%, significantly lower than the results from the traditional multivariate models tested on the same data by the authors.