Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant.

Although the management of sewage sludge is an important and challenging task of wastewater treatment, there is a scarcity of studies on the prediction of waste sludge. To overcome this deficiency, the present work aims to develop an appropriate model providing accurate and fast prediction of sewage sludge. With this aim, different machine learning (ML) algorithms were tested by data obtained from a real advanced biological wastewater treatment plant located in Kocaeli, Turkey. In modelling studies, a data set from January 2022 to December 2022 composed of 208 daily measurements was considered. The flow rate of the plant (Q), polyelectrolyte dosage (PD) and removed amounts of total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), total phosphorous (TP), total nitrogen (TN) were assigned as input parameters to predict sludge production (SP). The precision of the models was evaluated in terms of Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient (R2). Among the various tested models Kernel Ridge Regression provided the best accuracy with R2 value of 0.94 and MAE value of 3.25. Mutual information-based feature selection (MIFS) and correlation-based feature selection (CFS) algorithms were also used in the study in order to enhance the model performance. Thus, higher prediction accuracies were achieved using the selected subset of features. Furthermore, importance contribution of features were calculated and visualized by SHapley Additive exPlanations (SHAP) technique. The overall results of the work indicate the feasibility of ML models for describing the dynamic and complex nature of SP. The process operators may benefit from this modelling approach since it enables accurate and fast estimation of sewage sludge by using fewer and easily measurable parameters.

Comparative assessment of modeling deep learning networks for modeling ground-level ozone concentrations of pandemic lock-down period.

Covid-19 pandemic lock-down has resulted significant differences in air quality levels all over the world. In contrary to decrease seen in primary pollutant species, many of the countries have experienced elevated ground-level ozone levels in this period. Air pollution forecast gains more importance to achieve air quality management and take measures against the risks under such extra-ordinary conditions. Statistical models are indispensable tools for predicting air pollution levels. Considering the complex photochemical reactions involved in tropospheric ozone formation, modeling this pollutant requires efficient non-linear approaches. In this study, deep learning methods were applied to forecast hourly ozone levels during pandemic lock-down for an industrialized region in Turkey. With this aim, different deep learning methods were tested and efficiencies of the models were compared considering the calculated RMSE, MAE, R 2 and loss values.

Application of Taguchi L32 orthogonal array design to optimize copper biosorption by using Spaghnum moss.

In this work, Taguchi L32 experimental design was applied to optimize biosorption of Cu(2+) ions by an easily available biosorbent, Spaghnum moss. With this aim, batch biosorption tests were performed to achieve targeted experimental design with five factors (concentration, pH, biosorbent dosage, temperature and agitation time) at two different levels. Optimal experimental conditions were determined by calculated signal-to-noise ratios. "Higher is better" approach was followed to calculate signal-to-noise ratios as it was aimed to obtain high metal removal efficiencies. The impact ratios of factors were determined by the model. Within the study, Cu(2+) biosorption efficiencies were also predicted by using Taguchi method. Results of the model showed that experimental and predicted values were close to each other demonstrating the success of Taguchi approach. Furthermore, thermodynamic, isotherm and kinetic studies were performed to explain the biosorption mechanism. Calculated thermodynamic parameters were in good accordance with the results of Taguchi model.