Drinking water treatment aims to eliminate physical, chemical, and biological impurities to mitigate health risks, ensure adequate water quality, and promote sustainability. However, the treatment process often requires significant energy, chemicals, and technological inputs, which can lead to...

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations.

The production of municipal solid waste (MSW) has led to an unprecedented level of environmental pollution, worsening the global challenges posed by climate change. Researchers and policymakers have recently made significant strides in the field of sustainable and renewable energy sources, which are viable from technological, environmental, and economic perspectives. Consequently, the waste-to-energy programs enhance nations’ socioeconomic status while positively impacting the environment. To predict the higher heating value (HHV) of MSW fuel based on carbon, hydrogen, oxygen, nitrogen, and sulfur content, the current study introduces a Gradient Boosting Regression Tree (GBRT) model optimized with the Slime Mold Algorithm (SMA). This model was evaluated using an additional 50 data points after being trained with 202 MSW biomass data points. The performance of the model was assessed using three metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The results indicated that our model outperformed previously developed models in terms of accuracy and reliability. Additionally, a graphical user interface (GUI) was developed to facilitate the practical application of the model, allowing users to easily input data and receive predictions on the enthalpy of the combustion of MSW fuel.

Anaerobic digestion (AD) of feedstocks yields biogas, a potentially useful new energy source. This study looked into the anaerobic co-digestion of cow dung and organic garbage to produce biogas. An anaerobic biodigester, with a volume of 20 L, was used to digest organic waste (OW) and to trace the changes that occur during the AD process. It was equipped with tools that ensure complete control of the conditions affecting anaerobic biological reactions such as temperature, pH function, and mixing speed. Therefore, an anaerobic biodigester was designed to contain such biological transformations and to improve the biogas production process from OW. Based on the present investigation, the AD of OW was improved by integrating the substrate with sewage sludge or cow manure (CM) during the digestion process to provide the basic microorganisms to complete the digestion process. Feeding into the digester was a blend of 100 kg of cow dung (CM) and OW per day, diluted 1:1 with water. A gasbag was used to capture the methane that resulted. Biogas production began on the seventh day after the substrate was fed into the digester. A performance test was carried out on the produced biogas to determine its composition. For OW–CM, the generated biogas’s methane (CH₄) concentration was determined to be 60%, but the rates of decline for TS and VS were 57 and 50.6%, respectively. Anaerobic biodegradation of OW–CM experiments was observed at 37°C, a mesophilic temperature. For OW–CM, the pH value was 6.7. After being adjusted to standard circumstances, the cumulative volume of methane produced which had been recorded as 4,914 mL became 3964.5 mL.

Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms—Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were trained using an 80/20 train–test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R² values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions.