Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

Magnesite Ore Washing Facilities’ Wastewater Treatment and Recovered Water Reuse

Grecian Magnesite S.A., located in Gerakini, Chalkidiki, N. Greece, is a magnesite mining company, which produces and commercializes several Mg-based products. For production purposes, water is applied in large quantities for several uses. As a result, 5 × 106–7 × 106 m3 of wastewater, consisting mainly of muddy water, is produced from the magnesite ore washing facilities each year. In this study, the environmental impact of mining and industrial activities is examined, and the water management issues are addressed through its recovery. Water recovery reaches up to 96% (v/v), whereas the remaining sludge waste is safely deposited in tailings ponds.

High Potential for Anaerobic Microbial Sulfur Oxidation in Oil Sands Tailings Ponds

The biogenic production of toxic H2S gas in sulfate-rich oil sands tailings ponds is associated with strong environmental concerns. Beside precipitation into sulfide minerals and chemical re-oxidation, microbial sulfur oxidation may catalyze sulfide re-cycling but potentially contributes to acid rock drainage (ARD) generation. To evaluate the microbial potential for sulfur oxidation, we conducted a microcosm-based pilot study with tailings of an active pond. Incubations were performed under oxic and anoxic conditions, with and without KNO3 as an electron acceptor and thiosulfate as a common substrate for microbial sulfur oxidation. The highest potentials of sulfur oxidation occurred in oxic assays (1.21 mmol L−1 day−1). Under anoxic conditions, rates were significantly lower and dominated by chemical transformation (0.09 mmol L−1 day−1; p < 0.0001). The addition of KNO3 to anoxic incubations increased microbial thiosulfate oxidation 2.5-fold (0.23 mmol L−1 day−1; p = 0.0474), with complete transformation to SO42− coupled to NO3− consumption, pointing to the activity of sulfur-oxidizing bacteria (SOB) under nitrate-reducing conditions. Importantly, in the presence of KNO3, a decrease in sedimentary sulfides was associated with an increase in S0, which indicates the potential for microbially mediated oxidation of sulfide minerals and ARD generation. Furthermore, the comparative analysis of sediments from other anthropogenic aquatic habitats demonstrated high similarities with respect to viable SOB counts and corresponding activity rates.

Application Analysis of Radial Basis Function Neural Network Algorithm of Genetic Algorithm for Environmental Restoration and Treatment Effect Evaluation of Decommissioned Uranium Tailings Ponds

A new analysis method for the environmental stability of uranium tailing ponds is established in this paper, and the stability intervals and environmental stability rates of indicators are defined in precise mathematical language and analyzed with examples. The results show that the overall environmental stability of this uranium tailings pond is still in a poor state after the first phase of decommissioning treatment, and special decommissioning treatment should be carried out for factors such as pH and radionuclides Po and Pb. Using the powerful nonlinear mapping function of the artificial neural network, a radial basis function neural network algorithm was constructed to predict the environmental stability of the uranium tailing pond. It provides a new feasible method for the comprehensive evaluation technology of uranium tailings ponds. Accuracy in DOA Estimation. The research work in this paper mainly analyzed the environmental stabilization process and stability of decommissioned uranium tailings ponds, proposed a new concept of environmental stability with ecological and environmental protection concepts and gave it a new connotation, established an environmental stability evaluation index system for decommissioned uranium tailings ponds through index screening by using rough set theory, comprehensively considered the influence of environmental factors such as external wastewater and exhaust gas, and realized the multifactor. The system of evaluation indexes for the stability of decommissioned uranium tailings ponds was established by combining multiple factors, and the long-term monitoring and modeling of the environmental stabilization process of decommissioned uranium tailings ponds was carried out by using mathematical methods. The results show that the RBFNN-GA algorithm can reduce the training error of the random radial basis function neural network, improve the generalization ability of the network, and make it capable of handling large data sets.

Assessing the possibility of using dry concentration for oxidized pegmatite and granites at the Kedrovoe feldspar deposit

Introduction. Feldspar raw material is a natural source of silica, alumina, and alkali metal oxides. Each type of feldspar is distinguished by its applications and concentration methods. The main effective method of feldspar raw material concentration is flotation in the course of which the majority of harmful impurities are separated. However, this method is rather costly due to flotation reagents purchase, tailings ponds organization and maintenance, etc. Research relevance. Feldspar dry concertation technologies are promising in terms of resource-saving but uncommon. So, the development of a dry concertation technology for feldspar raw materials seems highly relevant. Research objective is to study the possibility of employing dry concentration technology to process feldspar raw materials at the new Kedrovoe deposit Methods of research. Laboratory equipment of JSC Uralmekhanobr was used for the research, namely centrifugal crusher DC-0.5; centrifugal deflection mill; laboratory-scale cascade classifier; dry electromagnetic separator SMS-20M ITOMAK, and tribo electrostatic separator. Results. The trials confirmed that it is possible to process Kedrovoe feldspar raw materials by dry methods and effectively use centrifugal crushing and grinding as preparation of raw materials for concentration. Feldspar concentrate with a mass fraction of Fe2O3 – 0.30%; SiO2 – 69.42%; Al2O3 – 17.36%; K2O + Na2O – 11.84% has been obtained, which meets the specifications. Conclusions. The technological studies confirmed the possibility in principle of using feldspar raw material dry concentration for oxidized pegmatite and granites of the Kedrovoe deposit at the processing plant of Malyshevskoe Ore Management JSC. Saleable feldspar concentrate has been obtained.

Propagation Laws of Reclamation Risk in Tailings Ponds Using Complex Network Theory

Accidents have occurred periodically in the tailings ponds where mine solid waste is stored in recent years, and thus their safety has become one of the constraints restricting the sustainable development of the mining industry. Reclamation is an important way to treat tailings ponds, but improper reclamation methods and measures not only cannot reduce the accident risk of tailings ponds, but will further increase the pollution to the surrounding environment. The influencing factors of reclamation accidents in tailings ponds are complex, and the existing models cannot characterize them. In order to study the propagation process of tailings pond reclamation risk, this paper proposes a three-dimensional identification framework for accident hazards based on evidence (TDIFAHE) to identify all potential hazards that may occur during the reclamation stage, and obtain a list of hazards. Based on the complex network theory, this paper uses identified hazards as network nodes and the correlation between hazards as the edges of the network. Based on the identified hazard data, the evolution network of reclamation risk in tailings ponds (ENRRTP) is constructed. By analyzing the statistical characteristics of ENRRTP, it can be found that ENRRTP has small world and scale-free characteristics. The above characteristics show that the reclamation risk of tailings ponds is coupled with multiple factors and the disaster path is short. Giving priority to those hub hazards that have a dominant impact on the reclamation risk can significantly reduce the reclamation risk of the tailings pond.

Ruminant-Waste Protein Hydrolysates and Their Derivatives as a Bio-Flocculant for Oil Sands Tailing Management

Reclamation of tailings ponds is a critical issue for the oil industry. After years of consolidation, the slurry in tailings ponds, also known as fluid fine tailings, is mainly comprised of residual bitumen, water, and fine clay particles. To reclaim the lands that these ponds occupy, separation of the solid particles from the liquid phase is necessary to facilitate water removal and recycling. Traditionally, synthetic polymers have been used as flocculants to facilitate this process, but they can have negative environmental consequences. The use of biological polymers may provide a more environmentally friendly approach to flocculation, and eventual soil remediation, due to their natural biodegradability. Peptides derived from specified risk materials (SRM), a proteinaceous waste stream derived from the rendering industry, were investigated to assess their viability for this application. While these peptides could achieve >50% settling within 3 h in bench-scale settling tests using kaolinite tailings, crosslinking peptides with glutaraldehyde greatly improved their flocculation performance, leading to a >50% settling in only 10 min. Settling experiments using materials obtained through different reactant ratios during crosslinking identified a local optimum molar reactant ratio of 1:32 (peptide amino groups to glutaraldehyde aldehyde groups), resulting in 81.6% settling after 48 h. Taken together, these data highlight the novelty of crosslinking waste-derived peptides with glutaraldehyde to generate a value-added bioflocculant with potential for tailings ponds consolidation.

Estimating Oil Sands Emissions using Horizontal Path-Integrated Column Measurements

Improved technologies and approaches to reliably measure and quantify fugitive greenhouse gas emissions from oil sands operations are needed to accurately assess emissions and develop mitigation strategies that minimize the cost-impact of future production. While several methods have been explored, the spatial and temporal heterogeneity of emissions from oil sand mines and tailings ponds suggests an ideal approach would continuously sample an area of interest with spatial and temporal resolution high enough to identify and apportion emissions to specific areas/locations within the measurement footprint. In this work we demonstrate a novel approach to estimating greenhouse gas emissions from oil sands tailings ponds and open-pit mines. The approach utilizes the GreenLITE™ gas concentration measurement system, which employs a laser absorption spectroscopy-based, open-path, integrated column measurement in conjunction with an inverse dispersion model to estimate methane (CH4) emission rates from an oil sands facility located in the Athabasca region of Alberta, Canada. The system was deployed for extended periods of time in the summer of 2019 and spring of 2020. CH4 emissions from a tailings pond were estimated to be 7.2 t/day for Jul–Oct 2019, and 5.1 t/day for Mar–Jul 2020. CH4 emissions from an open-pit mine were estimated to be 24.6 t/day for Sep–Oct 2019. Descriptions of the measurement system, measurement campaigns, emission retrieval scheme, and emission results are provided.

Using carbon-14 and carbon-13 measurements for source attribution of atmospheric methane in the Athabasca Oil Sands Region

The rapidly expanding and energy intensive production from the Canadian oil sands, one of the largest oil reserves globally, accounts for almost 12 % of Canada’s greenhouse gas emissions according to inventories. Developing approaches for evaluating reported methane (CH4) emission is crucial for developing effective mitigation policies, but only one study has characterized CH4 sources in the Athabasca Oil Sands Region (AOSR). We tested the use of 14C and 13C carbon isotope measurements in ambient CH4 from the AOSR to estimate source contributions from key regional CH4 sources: (1) tailings ponds, (2) surface mines and processing facilities, and (3) wetlands. The isotopic signatures of ambient CH4 indicate that the CH4 enrichments measured at the site were mainly influenced by fossil CH4 emissions from surface mining and processing facilities (53 ± 18 %), followed by fossil CH4 emissions from tailings ponds (36 ± 18 %), and to a lesser extent by modern CH4 emissions from wetlands (10 < 1 %). Our results confirm the importance of tailings ponds in regional CH4 emissions and show that this method can successfully separate wetland CH4 emissions. In the future, the isotopic characterization of CH4 sources, and measurements from different seasons and wind directions are needed to provide a better source attribution in the AOSR.