Preparation of hydrochar bio-based catalyst for fenton process in dye-containing wastewater treatment.

The use of synthetic dyes in the textile industry pollutes a huge amount of water. Thus, wastewater discharged from many textile companies to the receiving environment without being treated causes serious environmental and human health problems. The development of new techniques has become imperative. In this study, it was aimed to remove anionic dye (RR180) and cationic dye (BR18) by Fenton-like and adsorption process with hydrochars obtained from laurel leaves and watermelon peels. In the comparison of the adsorption and Fenton-like processes used in the dye removal of the produced bio-based materials, the Fenton-like process was selected in order to enhance the highest removal efficiency. The effects of various operating factors such as solution pH, amount of catalysts, hydrogen peroxide (H2O2) concentration, and initial dye concentration were evaluated on both dyes removal. The experimental results demonstrated that 99.8% RR180 dye and 98.8% BR18 dye removal efficiency were observed for an initial dye concentration of 100 mg/L with an adsorbent concentration of 1 g/L, H2O2 concentration of 15 µL/L, and optimum pH at the end of 60 min of reaction time. It was observed that an increase in initial dye concentration caused to decrease the dye removal efficiency. The optimum pH for the highest RR180 and BR18 dye removal was 4 and 6, respectively. It was observed that the increase in H2O2 concentration in the solution also decreased the dye removal efficiency. It turned out that catalysts obtained from hydrochars are an effective process for the high removal performance of cationic and anionic dyes.

Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures.

Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques.

The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models-Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)-for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment.

Improving energy consumption prediction for residential buildings using Modified Wild Horse Optimization with Deep Learning model.

The consumption of a significant quantity of energy in buildings has been linked to the emergence of environmental problems that can have unfavourable effects on people. The prediction of energy consumption is widely regarded as an effective method for the conservation of energy and the improvement of decision-making processes for the purpose of lowering energy use. When it comes to the generation of positive results in prediction tasks, the Machine Learning (ML) technique can be considered the most appropriate and applicable strategy. This article presents a Modified Wild Horse Optimization with Deep Learning approach for Energy Consumption Prediction (MWHODL-ECP) model in residential buildings. The MWHODL-ECP method that has been provided places an emphasis on providing an up-to-date and precise forecast of the amount of energy that residential buildings consume. The MWHODL-ECP algorithm goes through several phases of data preprocessing in order to achieve this goal. These steps include merging and cleaning the data, converting and normalising the data, and converting the data. A model known as deep belief network (DBN) is used here for the purpose of predicting energy consumption. In the end, the MWHO algorithm is utilised for the hyperparameter tuning procedure. The results of the experiments demonstrated that the MWHODL-ECP approach is superior to other existing DL models in terms of its performance. The MWHODL-ECP model has improved its performance, with effective prediction results of MSE-1.10, RMSE-1.05, MAE-0.41, R-squared-96.28, and Training time-1.23.

Adsorptive sequestration of noxious uranium (VI) from water resources: A comprehensive review.

Groundwater is usually utilized as a drinking water asset everywhere. Therefore, groundwater defilement by poisonous radioactive metals such as uranium (VI) is a major concern due to the increase in nuclear power plants as well as their by-products which are released into the watercourses. Waste Uranium (VI) can be regarded as a by-product of the enrichment method used to produce atomic energy, and the hazard associated with this is due to the uranium radioactivity causing toxicity. To manage these confronts, there are so many techniques that have been introduced but among those adsorptions is recognized as a straightforward, successful, and monetary innovation, which has gotten major interest nowadays, despite specific drawbacks regarding operational as well as functional applications. This review summarizes the various adsorbents such as Bio-adsorbent/green materials, metal oxide-based adsorbent, polymer based adsorbent, graphene oxide based adsorbent, and magnetic nanomaterials and discuss their synthesis methods. Furthermore, this paper emphasis on adsorption process by various adsorbents or modified forms under different physicochemical conditions. In addition to this adsorption mechanism of uranium (VI) onto different adsorbent is studied in this article. Finally, from the literature reviewed conclusion have been drawn and also proposed few future research suggestions.