Conventional Mining Research Articles

Transient electromagnetic perspective technology in the ultra-long coal mining face

The ultra-long coal mining face has the advantages of high recovery efficiency and low production cost; however, the widening of the coal face poses the difficulty of detecting the hidden geological bodies inside the coal face, and using conventional geophysical methods gives rise to imprecise detection results. Therefore, this paper proposes a transient electromagnetic perspective technology based on the conventional mining transient electromagnetic method. A long rectangular loop is used as the transmitting source in one coal mining tunnel of the coal mining face, and it is received in the opposite coal mining tunnel. This method is characterized by a long detection distance and high construction efficiency. Firstly, the paper used numerical simulation to investigate the spread of the transient electromagnetic field from the long rectangular loop source in both a uniform full-space model and a full-space laminated model. This showed the transient electromagnetic field propagation law and the effect of the coal seam thickness. Then, we analyzed the transient electromagnetic perspective signal characteristics of typical geological structures and gave the all-time apparent resistivity calculation method. Conclusively, the feasibility of this method is ultimately confirmed by the analysis of the measured data. Also, the research results provide a new technological approach to detecting hidden geological structures in the ultra-long coal mining faces.

Mitigating Coal Spontaneous Combustion Risk within Goaf of Gob-Side Entry Retaining by Roof Cutting: Investigation of Air Leakage Characteristics and Effective Plugging Techniques

Relative to conventional coal pillar retention mining technology (the 121 mining method), gob-side entry retaining by cutting roof (the 110 mining method), a non-pillar mining technique, efficiently addresses issues like poor coal resource recovery and significant rock burst damage. Nonetheless, the open-type goaf created by 110 mining techniques suffers from complex and significant air leaks, increasing the likelihood of coal spontaneous combustion (CSC) within the gob area. To address the CSC problem caused by complex air leakage within the goaf of gob-side entry retaining by roof cutting, this study takes the 17202 working face of Dongrong Second Coal Mine as the object of study. Field tests and simulation calculations are conducted to research the features of air leakage and the distribution of the oxidation zone within the goaf. Subsequently, plugging technology with varying plugging lengths is proposed and implemented. The tests and simulations reveal that the airflow migration within the goaf follows an L-shaped pattern, while air leakage primarily originates from gaps found in the gob-side entry retaining wall. The amount of air leaking into the gob-side entry retaining section is 171.59 m3/min, which represents 7.3% of the overall airflow. The maximum oxidation zone within the goaf ranges from 58.7 m to 151.8 m. After the air leakage is blocked, the airflow migration route within the goaf is transformed into a U-shaped distribution, and the maximum oxidation zone range changes from 42.8 m to 80.7 m. Engineering practice demonstrates that after air leakage plugging, the total air leakage volume within the gob-side entry retaining section significantly reduces to 20.59 m3/min, representing only 0.78% of the total airflow volume. This research provides reference on how to prevent the occurrence of CSC in similar mine goafs.

GPT's idea of stock factors

We amalgamate the capabilities of the GPT-4 computational model with the avant-garde methodology of autonomous factor generation, culminating in the synthesis of high-return factors within the equity investment milieu. Empirical outcomes elucidate that the factors conceptualized by ChatGPT attain a commendable Sharpe ratio peaking at 4.49, accompanied by an annualized return reaching 66.16%. Notably, the superlative excess returns garnered remain unaccounted for by the quintessential five-factor model. Through the implementation of an unembellished model averaging paradigm, the ensemble of 35 factors, conceived by ChatGPT, manifests an apex long-short annualized return of 88% and a Sharpe ratio registering at 2.46. In stark contrast to conventional data mining techniques, the temporal expenditure requisite for GPT's factor generation is minuscule. It relies on knowledge inference without the need for data input, and it can provide a thorough economic explanation for its factors.

Divergent roles of IREG/Ferroportin transporters from the nickel hyperaccumulator Leucocroton havanensis.

In response to our ever-increasing demand for metals, phytotechnologies are being developed to limit the environmental impact of conventional metal mining. However, the development of these technologies, which rely on plant species able to tolerate and accumulate metals, is partly limited by our lack of knowledge of the underlying molecular mechanisms. In this work, we aimed to better understand the role of metal transporters of the IRON REGULATED 1/FERROPORTIN (IREG/FPN) family from the nickel hyperaccumulator Leucocroton havanensis from the Euphorbiaceae family. Using transcriptomic data, we identified two homologous genes, LhavIREG1 and LhavIREG2, encoding divalent metal transporters of the IREG/FPN family. Both genes are expressed at similar levels in shoots, but LhavIREG1 shows higher expression in roots. The heterologous expression of these transporters in A. thaliana revealed that LhavIREG1 is localized to the plasma membrane, whereas LhavIREG2 is located on the vacuole. In addition, the expression of each gene induced a significant increase in nickel tolerance. Taken together, our data suggest that LhavIREG2 is involved in nickel sequestration in vacuoles of leaf cells, whereas LhavIREG1 is mainly involved in nickel translocation from roots to shoots, but could also be involved in metal sequestration in cell walls. Our results suggest that paralogous IREG/FPN transporters may play complementary roles in nickel hyperaccumulation in plants.

Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectories

Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people’s specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, ‘geographically interesting’ patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo-SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people’s specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people’s travel preference beyond travel cost is concerned.

Harnessing Hyper-Accumulator Plants for an Innovative Green Technology to Recover Nickle from Serpentine Soils in Sri Lanka

Phyto-mining is an environmentally sound green technique that has been employed for metal recovery from low-grade sources using hyperaccumulator plants. Due to its economic viability and wide social acceptance, this technology has been favoured over many conventional mining applications. Nickel (Ni) phyto-mining was introduced to the world with the identification of many Ni hyperaccumulator species grown in Ni-enriched metalliferous soils like serpentine, where they accumulate more than 1,000 mg/kg of Ni. Along the boundary of Highland and Vijayan Complexes in Sri Lanka, Ginigalpelessa, Indikolapelessa, and Ussangoda serpentinite deposits are closely located by showing similar geographical features. According to previous studies, these deposits contain a high concentration of Ni in the soil causing the evolution of serpentinite flora unique to these areas. This study aimed to identify the diversity and the presence of hyperaccumulator species that can be employed in Ni phyto-mining. More importantly, the study focused on identifying annual or biannual crop species with short lifespans that can be used in phyto-mining. The plant diversity and abundance analysis were carried out using a grid sampling method (3×3 m), and the highly abundant species were harvested and subjected to aqua regia digestion to determine hyperaccumulator levels using Inductively Coupled Plasma Mass Spectrometer (ICP-MS). Ginigalpelessa and Indikolapelessa showed similar diversity, abundant with grass and small herb species, such as Apluda mutica, Imperata cylindrica, Eupatorium odoratum, and Tephrosia villosa, where the A. mutica was dominant. In addition, plants like Morinda tinctoria, Vitex pinnata, and Azadirachta indica were dispersed throughout the deposit. Ussangoda deposit exhibited few herb species, including Hybanthus enneaspermus, Evolvulus alsinoides, and Desmodium triflorum, and localized thick vegetation patches. Diversity analysis of all the deposits showed very low plant diversity, ranging from 0.1 to 1.3. Moreover, the areas containing high Ni concentrations (6,000- 10,000 mg/kg) were abundant with stunted trees and grass species. The present study identified the Apluda grass species as a potential Ni hyperaccumulator for phytomining since it accumulates a high concentration of Ni (900-2,800 mg/kg) and can be grown biannually. Even though the grass species was dominant in both Ginigalpelessa and Indikolapelessa deposits, it was not observed in Ussangoda deposit, probably due to the arid climate and the deposition of salt spray from the coast. The findings of this study would be promising to establish phytomining in serpentine soils to support the increasing demand for Ni in the global market, especially for clean energy transition.
 
 Keywords: Low-grade soils, Clean energy transition, Ni phyto-mining, Serpentinite deposits
 
 Financial assistance from SR/LT/2021/15 grant from University of Moratuwa is acknowledged

Toward a Comprehensive Evaluation on the Online Methods for Monitoring Transformer Turn-to-Turn Faults

Transformer winding turn-to-turn fault is the prominent cause of transformer total failure, so detecting the winding fault in real time to stop the failure development in advance is imperative. However, existing techniques entailing periodic offline inspections fail to continuously monitor transformer winding states while causing extra costs due to the outage during inspections. This has driven researchers to consider effective continuous online monitoring methods from several technical perspectives, including typically port voltage current analysis, online frequency response analysis, and vibration analysis. Since these methods are conventionally evaluated with qualitative comparisons focusing only on feasibility, quantitative assessments indispensable for the targeted improvement of the methods and the most suitable method decision in specific scenarios are still missing. To this end, we conduct a comprehensive evaluation on the three methods by leveraging both experiment and theoretical analysis. Specifically, a customized experiment platform has been designed to support data acquisition under different operating conditions. As conventional feature mining algorithms cannot process the monitoring data produced by different methods in a uniform manner, a feature extraction algorithm leveraging image mining is proposed to extract data features after mapping the test data into a high-dimensional image. This novel algorithm allows us to fully assess several fundamental aspects (i.e., sensitivity, repeatability, and anti-interference capability) of these monitoring methods.

Roof movement and instability fracture characteristics in shallow-buried thin coal seam conventional mining faces

The variation in the width of the mining face significantly affects the stability of the face, leading to potential roof fracturing and collapse. Additionally, strong mining pressure can manifest, severely impeding the safe production of coal mines. This study uses the No. 16705 conventional working face of Jinda Coal Mine as its engineering background to investigate the characteristics of roof strata movement and instability under conditions of variable-width mining in shallow-buried thin coal seams. First, the dynamic load of the roof strata is estimated based on the key strata theory. Next, a mechanical model of the immediate roof strata movement in the working face is established based on the theory of elastic thin plates, which has been used to reveal the impact of different dimensions of the overhanging plate structure and residual overhanging structures in the corner on roof movement and its associated fracture mechanics. The findings indicated that the maximum bending deformation, deformation moment, and bending stress all have an exponential function relationship with the roof width. Similarly, these metrics have an exponential function relationship with the overhanging span of the roof. In addition, these parameters all have a linear functional relationship with the size of the residual overhanging structures in the corner. Finally, the effect of roof instability on overlying pressure is analyzed, and both the initial fracture step length and cyclic movement fracture step length of the roof are estimated. These insights offer valuable scientific guidance and a theoretical foundation for analyzing the adaptability of load-bearing pillars pressure in thin coal seam mining faces, bearing significant relevance to safety production.

Can bitcoin mining empower energy transition and fuel sustainable development goals in the US?

The transition to renewable energy in the US, vital for Sustainable Development Goals (SDGs) 7 and 13, faces economic obstacles, leaving a knowledge gap regarding potential innovative solutions to accelerate this shift. Simultaneously, the energy-intensive nature of blockchain applications like bitcoin mining is a cause for climate concerns. This work addresses the previously unanswered research question of whether bitcoin mining can be strategically used to harness surplus renewable energy from forthcoming clean energy installations, bridging the gap between economic viability and SDGs. Specifically, we conduct a comprehensive analysis, comparing the economic returns of bitcoin mining with three alternative chemical-based energy storage systems, including hydrogen, ammonia, and methanol, all powered by planned renewable sources. Mixed-integer linear programs were formulated to determine the maximum profits that can be obtained based on power utilization from planned renewable installations using varying alternatives. Results suggest that bitcoin mining is profitable in 80 out of 83 examined planned installations, generating a maximum profit of $7.68 million and harnessing 62% of the available renewable energy. Therefore, integrating bitcoin mining with planned renewable installations offers a dual solution of bolstering investments in the renewable energy sector while addressing climate concerns associated with conventional mining operations.

Soil Phytomining: Recent Developments—A Review

Phytomining (PM) is defined as the process of using plants capable of bio-extracting metals from soil in order to explore them economically. This relatively new, innovative method has been gathering significant attention in both the academic and commercial domains. Conventional mining methods are often economically unviable when applied to lean ores, and they can lead to secondary pollution in soil—a situation that applies to all excavated metals. On the other hand, PM is an environmentally friendly and economically viable solution that addresses the growing demands for metal resources, while simultaneously contributing to energy production by harnessing biomass energy. This comprehensive review presents the current PM techniques, challenges, and the hyperaccumulator plant species that may be used for the extraction of the main targeted elements in the process. Typically, the targeted metals are those of economic value, which can later be deposited or sold to various industries. This review also analyzes the factors influencing the economic viability of PM and proposes potential enhancements. Undeniably, PM offers the opportunity for economically sustainable exploration of metal-rich soils, but its full commercial viability remains constrained under current conditions as scientists are actively searching for the identification and utilization of new hyperaccumulator plant species in different locations worldwide, while creating new relationships and business avenues within the mining industry. Overall, this review highlights the current status of PM technology and the plants used, emphasizing the need for further research to enhance its commercial implementation and its potential to assist the mining industry. We conclude that PM, although a relatively new and unexplored concept, may provide economic and environmental benefits to soil end-users and managers who must cultivate on metal-contaminated soils as PM may turn yield shortages (of specific commercial crops) to benefits if high-yield hyperaccumulators are cultivated for industrial valorization of their high metal-content biomass.

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Structural-model-based genome mining can efficiently discover novel non-canonical terpene synthases hidden in genomes of diverse species.

Non-canonical terpene synthases (TPSs) with primary sequences that are unrecognizable as canonical TPSs have evaded detection by conventional genome mining. This study aimed to prove that novel non-canonical TPSs can be efficiently discovered from proteins, hidden in genome databases, predicted to have 3D structures similar to those of class I TPSs. Six types of non-canonical TPS candidates were detected using this search strategy from 268 genome sequences from actinomycetes. Functional analyses of these candidates revealed that at least three types were novel non-canonical TPSs. We propose classifying the non-canonical TPSs as classes ID, IE, and IF. A hypothetical protein MBB6373681 from Pseudonocardia eucalypti (PeuTPS) was selected as a representative example of class ID TPSs and characterized. PeuTPS was identified as a diterpene synthase that forms a 6/6/6-fused tricyclic gersemiane skeleton. Analyses of PeuTPS variants revealed that amino acid residues within new motifs [D(N/D), ND, and RXXKD] located close to the class I active site in the 3D structure were essential for enzymatic activity. The homologs of non-canonical TPSs found in this study exist in bacteria as well as in fungi, protists, and plants, and the PeuTPS gene is not located near terpene biosynthetic genes in the genome. Therefore, structural-model-based genome mining is an efficient strategy to search for novel non-canonical TPSs that are independent of biological species and biosynthetic gene clusters and will contribute to expanding the structural diversity of terpenoids.

Maximizing Mining Operations: Unlocking the Crucial Role of Intelligent Fleet Management Systems in Surface Mining’s Value Chain

On the one side, the operational expenses of mining enterprises are showing an upward trend; and on the other side, conventional mining fleet management systems (FMSs) are falling short in addressing the high-dimensionality, stochasticity, and autonomy needed in increasingly complex operations. These major drivers for change have convinced researchers to search for alternatives including artificial-intelligence-enabled algorithms recommended by Mining 4.0. The present study endeavors to scrutinize this transition from a business management point of view. In other words, a literature review is carried out to gain insight into the evolutionary trajectory of mining FMSs and the need for intelligent algorithms. Afterward, a holistic supply chain layout and then a detailed value chain diagram are depicted to meticulously inspect the effect of technological advancements on FMSs and subsequently the profit margin. The proposed value-chain diagram is advantageous in explaining the economic justification of such intelligent systems, illustratively, for shareholders in the industry. Moreover, it will show new research directions for mining scholars.

The Mining Industry's Role in Enhanced Weathering and Mineralization for CO2 Removal.

Enhanced weathering and mineralization (EWM) aim to remove carbon dioxide (CO2) from the atmosphere by accelerating the reaction of this greenhouse gas with alkaline minerals. This suite of geochemical negative emissions technologies has the potential to achieve CO2 removal rates of >1 gigatonne per year, yet will require gigatonnes of suitable rock. As a supplier of rock powder, the mining industry will be at the epicenter of the global implementation of EWM. Certain alkaline mine wastes sequester CO2 under conventional mining conditions, which should be quantified across the industry. Furthermore, mines are ideal locations for testing acceleration strategies since tailings impoundments are contained and highly monitored. While some environmentally benign mine wastes may be repurposed for off-site use─reducing costs and risks associated with their storage─numerous new mines will be needed to supply rock powders to reach the gigatonne scale. Large-scale EWM pilots with mining companies are required to progress technology readiness, including carbon verification approaches. With its knowledge of geological formations and ore processing, the mining industry can play an essential role in extracting the most reactive rocks with the greatest CO2 removal capacities, creating supply chains, and participating in life-cycle assessments. The motivations for mining companies to develop EWM include reputational benefits and carbon offsets needed to achieve carbon neutrality.

Stress distribution and roof subsidence of surrounding strata considering in situ coal conversion and CO2 mineralization backfilling: Photoelastic experiments using 3D-printed models of mining faces and goafs

Coal, a reliable and economical fuel, is expected to remain the primary energy source for power generation for the foreseeable future. However, conventional mining and utilization of coal has caused environmental degradation and infrastructure damage. An in situ coal conversion method has been proposed to mitigate environmental problems and reduce CO2 emissions resulting from coal extraction and utilization. This method involves the in situ conversion and utilization of coal, backfilling of waste rock, and CO2 mineralization to backfill the goaf. In this study, the impact of mining and conversion activities on the surrounding strata was evaluated to ascertain the effectiveness and advantages of the in situ coal conversion method. Transparent stope models were created using three-dimensional printing technology. The stress distribution and deformation characteristics of the surrounding strata were examined using photoelasticity and digital image correlation methods. The results were compared with those obtained using the traditional backfill mining method. The comparison revealed that the disturbance to the surrounding strata was 14.4 times less in the in situ conversion method than in the traditional backfill mining method. Additionally, the disturbance height at the roof and the disturbance depth at the floor were 4.2 and 2.1 times lower, respectively. The roof subsidence in the in situ conversion method was 1.97 times less than that in the traditional backfill mining method. These results confirm the advantages of minimizing the disturbance to surrounding rocks and controlling the subsidence of roof strata.

Data science in drug discovery safety: Challenges and opportunities.

Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, and competitor compound intelligence to understand the potential safety risks associated with modulating a drug target. However, there is a vast amount of information, updated daily that must be considered for each TSA. We have developed a data science-based approach that allows acquisition of relevant evidence for an optimal TSA. This is built on expert-led conventional and artificial intelligence-based mining of literature and other bioinformatics databases. Potential safety risks are identified according to an evidence framework, adjusted to the degree of target novelty. Expert knowledge is necessary to interpret the evidence and to take account of the nuances of drug safety, the modality, and the intended patient population for each TSA within each project. Overall, TSAs take full advantage of the most recent developments in data science and can be used within drug projects to identify and mitigate risks, helping with informed decision-making and resource management. These approaches should be used in the earliest stages of a drug project to guide decisions such as target selection, discovery chemistry options, in vitro assay choice, and end points for investigative in vivo studies.

A novel process for multi-stage continuous selective leaching of lithium from industrial-grade complicated lithium-ion battery waste

Lithium-ion batteries are widely used in fields such as electric vehicles, portable electronic devices, energy storage systems, and medical equipment, and their indispensable and irreplaceable characteristics are highly regarded. However, extensive disposal of lithium-ion batteries occurs due to severe electrochemical property degradation. These waste batteries, as high-grade secondary resources, have become exceedingly valuable, especially given their lithium content far exceeding the mineable grade from conventional mining processes. Recovering this lithium not only contributes to the circular utilization of resources but also yields substantial economic benefits. This paper presents an innovative method that directly and selectively leaches lithium from industrial-grade composite lithium-ion battery waste. Unlike conventional methods, which require the separation of cathode active materials from other components, this method directly acts on complicated mixed powders, achieving a high leaching rate of lithium and exceptional leaching selectivity (Li extraction rate is higher than 96 %, while the extractions of iron, copper, aluminum, nickel, cobalt, and manganese are all lower than 1 %). Furthermore, a multi-stage continuous leaching process has been developed, enabling efficient selective leaching and enrichment of lithium from industrial-grade mixed waste materials. This method is suitable for various compositions of industrial lithium-ion battery waste and has demonstrated excellent industrial prospects in mini-pilot industrial experiments. Additionally, the paper investigates the washing, purification, and precipitation processes, resulting in a high-concentration and high-purity lithium-rich solution after four-stage washing and two-stage purification. And battery-grade lithium carbonate can be obtained after precipitation. Moreover, the economic evaluation calculations confirm that this method is profitable and feasible. This new method offers advantages such as high leaching rates, excellent selectivity, a streamlined process, low costs, wide applicability, environmental friendliness, and feasibility for industrialization. It represents efficient and sustainable technology with promising applications.

Genetic Algorithm Service Vulnerability Mining Technology of Android System

The inland ships energy efficiency is significantly influenced by navigational environment, including speed and direction of wind and water depth. In order to solve the problem of low efficiency of conventional fuzzy test mining, a research method of Android system Service Vulnerability mining technology based on Genetic Algorithm (GA) is proposed. An efficient genetic selection operator model based on probability ranking and combination is also presented to improve the sample coverage and fuzzy test efficiency. Through the framework testing on different systems of mobile phones, multiple system service vulnerabilities are excavated. The execution results guide the generation of test cases, which reduces the proportion of invalid parameters in the test process to improve the efficiency of fuzzy testing. It is observe that the fuzzy test based on GA is much better than the conventional fuzzy test method in the vulnerability mining of system services, and has certain effectiveness and superiority. In addition, after using the two-point crossover algorithm to recombine the gene strings of two individuals, the phenotype of the newly generated individual gene string may become meaningless. It is observed that the selection algorithm factor has a very low p-value, while the ANOVA test confirms at least two groups that have statistically-significant difference.

Environmental footprint analysis of gold recycling from electronic waste: A comparative life cycle analysis

While e-waste poses significant environmental and health risks, it also harbors valuable metals like gold, ripe for recycling. However, extracting gold from e-waste carries its own ecological footprint. Therefore, employing tools like Life Cycle Assessment (LCA) to evaluate environmental impacts and identify mitigation measures is imperative. This research addresses critical gaps in the literature surrounding gold recovery from e-waste. It covers a range of methods, including hydrometallurgical and pyrometallurgical (well-established) to mechanical and biotechnological (less explored for gold recovery), rectifying the imbalance in methodological attention. This study also prioritizes environmental assessment, elevating LCA to a central investigative focus for understanding the sustainability of gold recovery from e-waste. Notably, there is a paucity of consistent and systematic comparisons between diverse gold recovery methods from e-waste in existing literature. While some publications touch on environmental aspects, comprehensive LCA investigations are lacking. Thus, this research explicitly bridges this gap through a comparative LCA, evaluating mechanical, hydrometallurgical, pyrometallurgical, electrochemical, and biotechnological methods and contrasting them with conventional mining (baseline method). The results highlight significant reductions in damage to human health, with the mechanical and electrochemical methods leading the way with a noteworthy 98% reduction compared to the baseline. The hydrometallurgical and pyrometallurgical methods also demonstrate substantial decreases of 83% and 92%, respectively. Though the biotechnological method exhibits a comparatively lower reduction of 60%, it represents a significant advancement in curbing damage to human health. Regarding ecosystem quality, the mechanical and electrochemical methods excel with exceptional reductions of over 99%, while the biotechnological, hydrometallurgical, and pyrometallurgical methods collectively achieve a decrease of approximately 99% compared to the baseline. Across the board, all evaluated methods manifest substantial reductions in total weighted environmental impacts, amounting to an approximate 99% reduction. The biotechnological method does entail the highest exergy demand, signifying greater energy consumption, while the mechanical method demonstrates the lowest exergy consumption. Considering feasibility and scalability, the electrochemical and mechanical methods emerge as the most favorable options for sustainable gold recovery. These methods demonstrate promising results in reducing environmental impacts and are well-suited for practical implementation.

Preliminary phytotoxicity of Mercury in conventional gold mining wastewater on Typha latifolia and Pistia stratiotes

Small-scale mining, or conventional mining, is quite common in Indonesia. Almost all of these artisanal mining activities disregard environmental safety around the mining areas. The current widely used gold extraction method for gold exploitation is the amalgamation method, which utilizes mercury (Hg). The use of this method can result in environmental pollution from mercury. Based on the impact of mercury that can contaminate the environment, it is necessary to treat gold mining wastewater before disposing of it into the environment. One alternative that can be used to treat gold mining wastewater is to use plants (phytotreatment). The aims of study was to observe the tolerance of Hg to Typha latifolia and Pistia stratiotes plants, The methods was call the Range Finding Test (RFT). The study was conducted using synthetic Hg at concentrations of 0.3, 0.7, 1, 3, and 6 mg/L and a control (0 mg/L) in a batch reactor. The reactors were added with soil up to 25% as a growing medium. The physical observations of plants were carried out for 28 days. Typha latifolia and Pistia stratiotes could both grow and thrive at a Hg content of 6 mg/L, according to physical observations of the plants. In the first seven days, Pistia stratiotes plants exhibited a stress reaction in the form of yellowing and falling leaves. Both plants had vigorous, extended root development in all concentrations. Based on these findings, phytotreatment can be utilized as an alternate therapy to lower the Hg levels in gold mine wastewater.