Both anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R²). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research conducted to predict TS and TSS in Langat River. Therefore, this is the first study to evaluate the water quality (TSS, TS, and DS) of Langat River using the aforementioned models (especially SVM and the six regression models).

A significant issue for the ecosystem is the presence of boron in water resources, particularly in produced water. Batch and dynamic experiments were used in this research to extract boron in the form of boric acid from aqueous solutions using boron selective resins, DIAION CRB05. DIAION™ CRB05 is an adsorbent that is effective in extracting boron from aqueous solutions due to its high binding capacity and selectivity for boron ions, and it is also regenerable, making it cost-effective and sustainable. Field Emission Scanning Electron Microscopy (FESEM), X-ray diffraction (XRD), and FTIR analysis for DIAION CRB05 characterization. To increase the adsorption capacity and find the ideal values for predictor variables such as pH, adsorbent dose, time, and boric acid concentration, the Box–Behnken response surface method (RSM) was applied. The dosage was reported to be 2000 mg/L at pH 2 and boron initial concentration of 1115 mg/L with 255 min for the highest removal anticipated from RSM. According to the outcomes of this research, the DIAION CRB05 material enhanced boron removal capability and has superior performance to several currently available adsorbents, which makes it suitable for use as an adsorbent for removing boric acid from aqueous solutions. The outcomes of isotherm and kinetic experiments were fitted using linear methods. The Temkin isotherm and the pseudo-first-order model were found to have good fits after comparison with R² of 0.998, and 0.997, respectively. The results of the study demonstrate the effectiveness of DIAION™ CRB05 in removing boron from aqueous solutions and provide insight into the optimal conditions for the adsorption process. Thus, the DIAION CRB05 resin was chosen as the ideal choice for recovering boron from an aqueous solution because of its higher sorption capacity and percentage of boron absorbed.

The conventional disposal of green straws through burning can be eliminated in a biorefinery that converts them into a range of sustainable commercial products. However, this leads to the generation of green straw biorefinery effluent (GSBE). Green straw biorefineries discharge wastewater into the ecosystem that contains high concentrations of COD and NH₄⁺−N. It is one of the most notable sources of visual pollution and disruption of aquatic life as well as public health that requires treatment prior to discharge. To improve the GSBE quality for environmental sustainability, the attainment of sustainable development goals 6, 9, and 14, “clean water and sanitation”, “inorganic and organic waste utilization for added values from material”, and “life below water” is very important. Therefore, the effectiveness of the continuous mode activated sludge (CMAS) system and the biocomposite-based–continuous mode activated sludge (SB-CMAS) system in the treatment of GSBE was investigated in this study. Response surface methodology (RSM) was used to optimize the process variables. At their optimized conditions, the performances of CMAS and SB-CMAS were analyzed in terms of COD and NH₄⁺−N. Findings showed 81.21% and 95.50% COD and 78.31% and 87.34% NH₄⁺−N reduction in concentration for CMAS and SB-CMAS, respectively. The high COD and NH₄⁺−N removal efficiencies indicate the better performance of CMAS and SB-CMAS. The first- and second-order models and the modified Stover–Kincannon biokinetic models were utilized to analyze substrate removal rates. It was discovered that the modified Stover models were ideal for the measured data with R² values 0.99646 and 0.91236 attained for COD and NH₄⁺−N, respectively, in CMAS. The SB-CMAS had 0.99932 and 0.99533 for COD and NH₄⁺−N, respectively. Maximum contaminant elimination was attained at 60% GSBE and 2-day HRT. Thus, to achieve the UN SDGs for 2030, findings from this study have the potential to answer goals 6, 9, and 14.

Purpose. The purpose of the paper is to provide open-pit mining operations with practical strategies and insights to optimize truck loading and hauling cycles, ultimately leading to enhanced productivity and economic advantages. Methods. The objectives are to minimize loading time, optimize the haul road network, enhance truck performance, and optimize dumping and return time. By diligently implementing these methods and achieving these objectives, open-pit mining operations can significantly reduce the truck cycle times, resulting in increased productivity, lower costs and improved profitability. Findings. In this case, the total loading time of the excavator and shovel is determined to be 3.98 and 2.92 minutes, respectively, while the hauling time for total loading of the open-pit floor depends mainly on the average distance and speed of 239 m and 10.1 km/hour, which results in 1.53 minutes. Originality. As a result, the total cycle time for open-pit mining is 19.765 minutes, resulting from the total loading time, hauling time for total loading, total dumping time, and total return time for empty transport of 4.265, 8.46, 0.86 and 6.18 minutes, respectively. Practical implications. By combining theoretical analysis with practical insights and site-specific considerations, the paper aims to provide a comprehensive and applicable framework for optimizing truck cycle time in open-pit mining, resulting in improved efficiency and profitability of mining operations.

Purpose. This study aims to investigate fluid flow and heat transfer within rocks containing boreholes, focusing on the complex mechanisms within hot reservoirs. Non-commercial finite element (FE) software is used to visualize and present the results. Methods. The study involved the use of FE method with Visual Finite Element Analysis (VisualFEA) software to analyze the coupled phenomena of fluid flow and heat transfer in a rock sample. Special attention was given to incorporating material structure and geotechnical analysis in the software, as well as the treatment of cracked elements. In addition, the validation was done by comparing the current numerical solution using VisualFEA with the numerical solution using ANSYS Software. Findings. The study findings highlight the capabilities of VisualFEA software to accurately represent fluid flow, stress, and heat transfer in borehole-containing rocks. The results include insights into flow direction within the borehole, temperature distribution, and the validation of the software performance against expected system behavior. The study demonstrates the effectiveness of VisualFEA in handling complex loading and its ability to visualize multiple flow directions within a 2D model. The results are presented in the form of contours and curves. Originality. This study contributes to the field demonstrating the application of VisualFEA software in analyzing fluid flow and heat transfer in rocks with boreholes. The focus on incorporating material structure, geotechnical analysis, and treatment of cracked elements adds originality to the study, providing a comprehensive understanding of the coupled phenomena in hot reservoirs. Practical implications. The practical significance of this study is in the validation and benchmarking of VisualFEA software for studying fluid flow and heat transfer in geotechnical application. The findings can be utilized by geotechnical engineers and researchers to better understand the behavior of borehole-containing rocks under specific pressure and thermal loading conditions. The insights gained from this study can be used in decision-making processes related to resource mining, reservoir engineering, and geothermal energy use.

Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R², MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R², MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively.