تعتبر شركة Olux شركة رائدة ومتميزة على المستوى الدولي في مجال البحث والتطوير وتصنيع وبيع محطات التكسير والغربلة واسعة النطاق ومعدات الطحن الصناعية ومصانع الاستفادة.
the application of machine learning and data mining in manufact uring has carried on the overview of the system, and introduces the research status both at home and abroad, and specify the ...
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For improving the accuracy and precision of the instrument in the field geology application, Shi et al. [89] studied the ... Mineral analysis applied for mineral resources exploration and mining industry required a well database. ... and then acquires an accurate analysis result. The applications of LIBS combined with machine learning have ...
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Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to efficiently design novel materials with superior performance. Here we reviewed the recent applications of ML-assisted design of high-entropy alloys, titanium alloys, copper alloys, aluminum alloys and magnesium alloys.A representative workflow of ML approaches was …
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Mine planning is essential for a mining company to ensure optimal ore extraction for a mineral asset (Koushavand, et al., 2014). For underground mines systematic extraction of material is required ...
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Various AI and ML algorithms such as Neural Networks, Decision Trees, and Random Forests are being used for mineral exploration. These techniques help in identifying patterns and correlations in...
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Full-face tunnel boring machine (TBM) tunnelling has unparalleled advantages over conventional drill-and-blast (D&B) techniques in terms of higher advance rates and lower risk levels in favourable ground conditions. However, there are only a few successful TBM applications in mines. The aim of this paper is to discuss the technical challenges of using TBMs in mining …
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Figure 1 illustrates that the proportion of publications discussing the application of AI within the total articles on mine ventilation has notably increased over the past four years, surpassing 20% by the end of 2023. Deep learning emerged as one of the frequently cited keywords in a cluster analysis of bibliographic works on mine ventilation spanning the past decade, as reported by …
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Importantly, machine learning has been applied prosperously in recent studies to predict hanging wall failures and mining dilution in underground mining operations (Jang and Topal, 2013; Jang et al., 2016; Chongchong et al., 2018; Mottahedi et al., 2018). Affirmative results reported from these studies suggest that machine learning applications ...
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Jung D and Choi Y Systematic review of machine learning applications in mining: exploration, exploitation, and reclamation Minerals 2021 11 2 148. Crossref. Google Scholar ... et al. Open-pit mining area segmentation of remote sensing images based on DUSegNet J Indian Soc Remote Sens 2021 49 6 1257-1270. Crossref. Google Scholar [155] Xu J ...
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Machine vision is the application of machine learning techniques to images and video, rather than numerical data from plant sensors. Image data presents a significant challenge for any analytical technique due to its high-dimensional nature, as each pixel represents information about the image. ... (Thomas et al., 2018), and data mining and ...
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It is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Machine learning tools represent key enablers for empowering material scientists and …
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Smart mines were introduced and widely applied around the world with advanced technologies based on the applications of AI. This paper aims to provide a comprehensive view of AI applications in mining and geological …
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Introduce the partial applications of different types of machine learning methods in the field of MOFs. ... which belong to micropores according to their pore size. In 1977, Ludi et al. [6] used x-ray single crystal diffraction to ... database proposed by Snurr et al. [100] are widely used for high-throughput screening and relationship mining ...
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Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data. J.
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In this paper, a systematic review of the works carried out on the application of machine learning for innovative use of mineralogical information in mining and mineral studies was presented. The search strategy resulted in a …
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The deep integration of computer field and coal mining field is the only way to coal mine intellectualization. A variety of artificial intelligence tools have been applied in open-pit and shallow coal mines. However, with the geometric increase of coal demand, the contradiction between supply and demand is becoming more and more serious, and the exploitation of …
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This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application.
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Recent advances in material science and engineering with the aid of modern computational algorithms and devices [1] have greatly pushed the need of advanced materials that can be adopted to increasingly complex engineering applications and adapted to multiple functional and safety requirements.Among various types of advanced materials such as …
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process optimization, machine health, the safety of workers and asset management. IoT can assist real-time platforms in remotely monitoring and ... at a particular stage of mining (Ghasemi et al., 2014; Muduli et al., 2018; ... applications (Cai et …
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It concerns many sectors of a mining industry functioning, exactly in: work maintenance of underground part of extraction, assuring the supply of electrical power network or management with ...
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By being able to better explore the interconnectedness of different types of ecosystems and to quickly identify things such as the type of ground cover through AI-enabled image detection for earth observation, AI could contribute to more sustainable mining (Vinuesa …
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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The …
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Text Mining which is known as text analysis, is defined as the process to extract the proper text patterns from the unstructured text data, which are collected from different written resources.
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To identify all the records of interest, searches were made that connected COVID-19 with AI-related concepts. The most general concepts relating to artificial intelligence have been sought, as reviewed in recent literature [] and completed with specific reviews about emerging technologies [49,50].In addition to more general areas of application, we included several searches for …
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Digitalization in the mining industry has led to the generation and collection of large amounts of data. In the last decade, mining companies have deployed artificial intelligence and machine …
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Data mining has been widely used in the business field, and machine learning can perform data analysis and pattern discovery, thus playing a key role in data mining application. This paper expounds the definition, model, development stage, classification and commercial application of machine learning, and emphasizes the role of machine learning ...
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The Apriori algorithm is a typical association rule-based mining algorithm, which has applications in sequence pattern mining and protein structure prediction. Many machine learning algorithms in data mining are derived based on Apriori (Zhang et al., 2014). The basic method of association rule mining is through the use of Some metrics are used ...
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Application of Machine Learning in Text Mining Print Special Issue Flyer; Special Issue Editors ... set and query sample as proposed in this paper could be a promising way for boosting the classification performance in …
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A Real-World Application of Process Mining for Data-Driven Analysis of Multi-Level Interlinked Manufacturing Processes ... manufacturing process comprises interlinked events at the superordinate business process level and at the subordinate machine level, making its analysis based on PM challenging. ... Kotzab et al. (Ed.) â€" Dynamics in ...
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Keywords: fragmentation, blasting, mining, sublevel caving, laser scanning Introduction Rock fragmentation by blasting affects the efficiency, productivity and cost of downstream operations including loading, crushing and grinding (Dinis da Gama, 1990; Scott et al., 1999).
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In addition, Takahashi et al. analyzed data for 15,000 ABC 2 D perovskites (similar to compounds like BaNbO 2 N, RbTiO 2 F, PbTaO 2 N) and used the random forest (RF) classification model to predict the potential application of 9,328 perovskite materials for solar cell applications [54]. In their study, Li- and Na-based perovskite materials in ...
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Another system used by Saudi Aramco was reviewed by Amer et al. (2016), ASAS (Automated Services Assignment System) was used for scheduling and monitoring the fleet of drilling and workover rigs ...
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B.S. dos Santos et al. Data mining and machine learning techniques applied to public health problems: a bibliometric analysis from 2009 to 2018. Comput. Ind. ... the application of data mining has been paramount in many fields of studies (Sathasivam et al., 2020a; Sathasivam et al., 2020b; Srivastava et al., 2022; Mu et al., 2022; ShakorShahabi ...
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Almost all the coal is produced from open cut mines in Indonesia. As a consequence of open cut mine application, a great deal of coal is left out in the highwalls of the mined-out pits. Highwall mining systems can be used to recover this coal. The use of highwall mining systems has increasingly come into play in the US and Australia. However, it is not …
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If we want to factor environmental, cultural (i.e., language), governance, or community-level decisions and objectives into machine learning and other AI applications used in the mining industry, extra surveys and data collection may be in order. In addition, thorough data analysis for biases should be included to prevent any unjust outcomes.
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The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies ...
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