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Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO 3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay. [Display omitted] • Aqueous solutions percentage copper (Cu) ions adsorption is predicted. • The feasibility of the artificial intelligence (AI) models is adopted in this study. • Five different modeling scenarios based on the related parameters are investigated. • Uncertainty analysis using bootstrap method is applied. • Results are evidence the potential of the AI models for the Cu prediction. [ABSTRACT FROM AUTHOR]

Μining waste, processing by-products and mine water discharges pose a serious threat to the environment as in many cases they contain high concentrations of toxic substances. However, they may also be valuable resources. The main target of the current review is the comparative study of the occurrence of rare earth elements (REE) in mining waste and mine water discharges produced from the exploitation of coal, bauxite, phosphate rock and other ore deposits. Coal combustion ashes, bauxite residue and phosphogypsum present high percentages of critical REEs (up to 41% of the total REE content) with ΣREY content ranging from 77 to 1957.7 ppm. The total REE concentrations in mine discharges from different coal and ore mining areas around the globe are also characterised by a high range of concentrations from 0.25 to 9.8 ppm and from 1.6 to 24.8 ppm, respectively. Acid mine discharges and their associated natural and treatment precipitates seem to be also promising sources of REE if their extraction is coupled with the simultaneous removal of toxic pollutants. [ABSTRACT FROM AUTHOR]

Planned decommissioning of coal-fired plants in Europe requires innovative technical and economic strategies to support coal regions on their path towards a climate-resilient future. The repurposing of open pit mines into hybrid pumped hydro power storage (HPHS) of excess energy from the electric grid, and renewable sources will contribute to the EU Green Deal, increase the economic value, stabilize the regional job market and contribute to the EU energy supply security. This study aims to present a preliminary phase of a geospatial workflow used to evaluate land suitability by implementing a multi-criteria decision making (MCDM) technique with an advanced geographic information system (GIS) in the context of an interdisciplinary feasibility study on HPHS in the Kardia lignite open pit mine (Western Macedonia, Greece). The introduced geospatial analysis is based on the utilization of the constraints and ranking criteria within the boundaries of the abandoned mine regarding specific topographic and proximity criteria. The applied criteria were selected from the literature, while for their weights, the experts' judgement was introduced by implementing the analytic hierarchy process (AHP), in the framework of the ATLANTIS research program. According to the results, seven regions were recognized as suitable, with a potential energy storage capacity from 1.09 to 5.16 GWh. Particularly, the present study's results reveal that 9.27% (212,884 m2) of the area had a very low suitability, 15.83% (363,599 m2) had a low suitability, 23.99% (550,998 m2) had a moderate suitability, 24.99% (573,813 m2) had a high suitability, and 25.92% (595,125 m2) had a very high suitability for the construction of the upper reservoir. The proposed semi-automatic geospatial workflow introduces an innovative tool that can be applied to open pit mines globally to identify the optimum design for an HPHS system depending on the existing lower reservoir. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) has reached a higher level of emissions in the last decades, and as it is widely known, CO2 is responsible for numerous environmental problems, such as climate change. Thus, there is a great need for the application of CO2 capture and storage, as well as of CO2 utilization technologies (CCUS). This review article focuses on summarizing the current CCUS state-of-the-art methods used in Europe. Special emphasis has been given to mineralization methods/technologies, especially in basalts and sandstones, which are considered to be suitable for CO2 mineralization. Furthermore, a questionnaire survey was also carried out in order to investigate how informed about CO2 issues European citizens are, as well as whether their background is relative to their positive or negative opinion about the establishment of CCUS technologies in their countries. In addition, social acceptance by the community requires contact with citizens and stakeholders, as well as ensuring mutual trust through open communication and the opportunity to participate as early as possible in the development of actions and projects related to CO2 capture and storage, at all appropriate levels of government internationally, as citizens need to understand the benefits from such new technologies, from the local to the international level. [ABSTRACT FROM AUTHOR]

Various types of plutonic and volcanic rocks and their alteration products from Greece (serpentinite, magnesite and andesite), have been used for sustainable removal of Uranium (U) from the acidic drainage of Kirki mine, as well as for the pH increase of the polluted solutions. In this light, this study aims at the further understanding and improvement of the ecofriendly reuse of sterile, natural raw materials (including those remaining through industrial processing and engineering testing of aggregate rocks), for remediation of acid mine drainage. The selected rocks constitute such residues of sterile materials were used as filters in experimental continuous flow devices in the form of batch-type columns, in order to investigate acidic remediation properties with special focus on U removal. The initial pH of the wastewater was 2.90 and increased after seven (7) days of experimental application and more specifically from the fourth day onwards. Uranium removal became quantitatively significant once pH reached the value of 5.09. The volcanic rocks appeared to be more effective for U removal than the plutonic ones because of microtextural differences. However, optimum U removal was mainly achieved by serpentinite: while the raw materials rich in Mg strongly reacted and remediated the pH of the drainage water waste. Furthermore, the increase of pH values due to the presence of mineral raw materials, provided increased oxidation potential which deactivated the toxic load of metals, particularly U. Consequently, batch-type serpentinite reaction with the tailing fluid caused a drop in U concentration from an initial value of 254 ppb to the one of 8 ppb, which corresponds to 97% of removal. Andesite presented the second best reactant for experimental remediation, especially when it was mixed with magnetically separated mineral fractions. Despite the fact that the proposed methodology is currently at a relatively low Technology Readiness Level (TRL), it carries the potential to become an extremely effective and low-cost alternative to conventional environmental restoration technologies. • Filters of mineral raw materials to remove uranium from acidic drainage. • Serpentinite opaque minerals are proved to be the most effective for U removal. • The proposed methodology is extremely effective and a low-cost one. • Key role for the eco-friendly U removals is mineral raw material's microstructure. [ABSTRACT FROM AUTHOR]

A wide range of dyes are being disposed in water bodies from several industrial runoff and the quantity is rapidly increasing over the years. From an environmental safety point of view, it is urgent to improve the removal process of dyes. It is important to understand the stochastic and highly redundant behavior of the process of dye removal (D R) in wastewater treatment. This leads to better utilization of Machine Learning (ML) models for both optimization as well as prediction of the D R process efficiency. In this review, 200 papers (Years, 2006–2021) have been systematically reviewed from the Web of Science and Scopus-indexed journals, covering a total of 84 journals. All applied ML models have been thoroughly studied in the review and analyzed in terms of their architecture setup, hyper-parameters selection, performance, advantages, and disadvantages. A wide range of optimization methods for hyper-parameters tuning were analyzed and discussed scientifically. Explicit information about the data sizes, splitting structure for training-validation-testing along with input and output selection approaches have been logically reviewed and discussed. Data availability, transparency, and reusability have been reported adequately. Various software for data-driven modeling have been discussed with their pros and cons. Trends in statistical evaluators (among about 60 types) have been discussed with their pros and cons including their sensitivity with the data fluctuations. Moreover, the most popular performance metrics have reported. In addition, the D R mechanism has reviewed and discussed inclusively. Extensive media used for the decolorization were discussed thoroughly, including their physical and chemical characteristics, along with feasibility and equilibrium data based on Langmuir model. The cost of the applied media in the decolorization process reported adequately. Finally, the research gap and future road map of the next 5 years, which bridge the gap of the domain are scientifically drafted along with the limitations. This critical review not only provides the appraisal of growth of D R research integrated with ML in the last couple of decades but also scouts the potential studies where all experimental, chemical and modeling processes should be taken under consideration. [Display omitted] • An extensive review on dye removal modeling established over 2 decades. • All machine learning predictive models and optimizers are exhibited and assessed. • Data availability, application of hyper-parameter optimization are analyzed. • Removal media, dye color, and economical global assessment are appraised. • 5 years of future prospective road map are suggested to be explored. [ABSTRACT FROM AUTHOR]