Mining Automation Research Articles

A Study on the Role of Automation and AI in Operational Efficiency and Sustainable Mining

One of the major pillars for world’s economic growth, the mining industry today is facing challenges in maintaining efficiency and productivity while following environmental regulations, at an ever-growing demand. There is limited incorporation of technology in Indian mining practices. To address this gap, the study explores the integration of automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and promote sustainable mining. The objective of the research is to evaluate and assess the contribution of advanced technology towards achieving sustainability in mining, benefits derived out of real-time monitoring, and the impact of automation on the safety of work. It is conducted with the qualitative research design approach, which has made an attempt to capture the metamorphosis of mining technologies, benefits out of integration across value chains, and areas that pose challenges through interviews and focus group discussions, carried with industry experts in various parts across India. It also compares the role of technology in mining practices across leading countries and India. These provide evidence that automation and AI are forces in the efficient management of environments and workforce security. IoT devices are quite useful in real- time monitoring and are designed for environmental footprints and resource conservation management. The paper acknowledges, however, some of the methodological limitations, which in turn places additional demands on ongoing innovation. Lastly, the research shows how work can be conducted in the future to integrate state-of-the-art technologies, blockchain, and advanced AI for further value chain enhancement in mining, focusing much on how this industry keeps adapting. Keywords: Sustainable mining, automation and AI, productivity, operational efficiency, integrating technology, real-time monitoring, worker safety, environmental footprint.

Equipment and Operations Automation in Mining: A Review

The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation in this critical industry. A key focus of this paper is the impact of automation on the safety and efficiency of mining operations. Highlighting the successful implementation of Automated Haul Truck Systems (AHSs) in surface mining. Additionally, this paper explores the development of automation in underground mining and its challenges, particularly limitations in communication and localization, which hinder the development and deployment of fully autonomous systems. It also provides an exploration of the challenges associated with widespread automation adoption in mining, including high initial investment costs, concerns about job displacement, and the need for specialized skills and training. Looking toward future advancements in enabling technologies will be critical for furthering automation in mining. Machine learning and AI will play an increasingly critical role in intelligent automation, enabling autonomous systems to adapt to dynamic environments, optimize processes, and make informed decisions. This paper provides a look into human–robot collaboration in the future of mining. As the industry transitions toward greater automation, it is essential to consider the evolving roles of human workers to foster a collaborative work environment. This involves prioritizing human safety, providing adequate training, and addressing concerns about job displacement to ensure a smooth transition toward a more automated future.

Bridging depths and data in mines: 5G-based point cloud mapping in unstructured environments

Underground mines pose significant challenges, including hazardous working conditions, limited access, and the need to ensure the safety of human workers. Digital transformation through the integration of modern technologies is essential to mitigate these challenges and enhance the overall safety and efficiency of mining operations. This paper addresses the pressing need for 5G connectivity for the digital transformation of underground mines and demonstrates its application through a live 3D point cloud mapping by a mobile robot. The results of the experiments conducted to validate the network’s performance for such a use-case are presented. The first experiment involved testing the latency of the network over a test drift at various loads. The second experiment involved mapping the drift and streaming the 3D point cloud map of the drift over the 5G network. These initial experiments emphasize the potential of the 5G-enabled automation in underground mines and holds promise in digitalizing underground mining operations.

The innovativeness of technologies of Industry 4.0 in the modern mining environment

The article presents the results of Industry 4.0 technologies in the modern mining environment. The study is aimed at identifying trends in interest in the topic of innovativeness of Industry 4.0 technologies in the modern global mining environment. To solve the problem of implementing innovations in the context of Industry 4.0 in the modern mining environment, particularly those related to mining science, two approaches were used: the Google Trends search service and the VOSviewer software. The first stage of the study identified the level of interest in mining science and Industry 4.0 in the world. According to the results of the second stage of the study, 8 strategic directions for the development of the mining industry within Industry 4.0 were identified in accordance with the qualitative composition of clusters by keywords in scientific publications, the Scopus scientometric database on the topic of innovativeness of Industry 4.0 technologies in the modern mining environment. It is proposed through amending the mining legislation in accordance with the development of Industry 4.0 technologies for the automation and digitalization of mining significantly increase interest in transformational changes.

Major Issues Related to Underground Longwall Coal Mining Automation

Major Issues Related to Underground Longwall Coal Mining Automation

Automation and Artificial Intelligence Technology in Surface Mining: A Brief Introduction to Open-Pit Operations in the Pilbara

Automation and Artificial Intelligence Technology in Surface Mining: A Brief Introduction to Open-Pit Operations in the Pilbara

Assessing the reliability of the hardware and software complex of fault-tolerant control systems

Reliability assessment of hardware and software complexes is relevant due to the expansion of automation and intellectualization for sustainable development of mining and transport systems, energy complexes and mechanical engineering. This paper presents an approach to the reliability assessment of control systems based on functional block diagrams of software and control flows. When assessing the reliability of a control system, software is considered taking into account the close relationship with hardware. The article considers control systems that assume the performance of the required function periodically and repeatedly within the transportation and technological cycle of the system. This is typical for automation of mining and transportation systems, as well as energy complexes. It is taken into account that the main function within the transportation-technological cycle does not require a complex algorithm in contrast to the operating system. Thus, the proposed reliability assessment model allows to take into account the interaction between hardware and software of automated control systems, including the variant of their faulttolerant execution.

Examples of laser scanning application in JSW SA mines

Laser scanning offers timely assessments of mine sites despite challenges posed by operational environments. This article explores advancements in 3D scanning systems, data capture and processing techniques, and their primary applications in underground mining, using JSW SA mines as an example. While laser scanning has matured for tasks such as change detection, clearance measurements, and structure mapping, opportunities for enhancement exist in areas such as lithology identification, surface parameter measurements, and autonomous navigation. Limited infrastructure for data transfer and processing remains a constraint; however, laser scanners are increasingly pivotal in mine automation due to their affordability, accuracy, and mobility. Until recently, laser scanning was considered more of a curiosity than a mainstream measuring method. In Poland, creating maps for land surveying based on point clouds generated by laser scanners remains uncommon. Nevertheless, technological advances over the past decade have made surveyors, designers, architects, and conservators of historical monuments increasingly inclined and capable of utilizing point clouds generated by stationary scanners, which offer evergreater measurement accuracy. This article outlines how laser scanning, as an innovative measurement technique, has been integrated into the mining industry, particularly for shaft monitoring purposes.

Decentralised Motion Planning for Autonomous Mining Haulage Trucks using Prioritised Multi-Agent MPPI

This paper introduces a decentralised motion planning method for Autonomous Haulage Systems (AHS) using Prioritised Multi-Agent Model Predictive Path Integral Control (P-MA-MPPI). Our approach incorporates vehicle prioritisation, addressing a challenge that has previously limited decentralised MPPI frameworks, to enhance navigation safety and efficiency by exchanging future trajectories among agents. The experimental findings indicate a reduction in collision and deadlock rates when compared to an existing MPPI-based approach, alongside about a 20% increase in energy efficiency in complicated scenarios where three vehicles simultaneously enter an intersection. This research contributes to scalable and sustainable operation strategies, improving the efficiency and the environmental impact of future mine automation.

Longwall Mining Automation—The Shearer Positioning Methods between the Longwall Automation Steering Committee and China University of Mining and Technology

The shearer positioning method is of great significance to the automation of longwall mining. The research teams in the Longwall Automation Steering Committee (LASC) of Australia and China University of Mining and Technology (CUMT) have focused on shearer positioning and identified the shearer inertial navigation system, the measurement of longwall retreat and creep displacement, and the backward calibration of the shearer trajectory as three key technologies to obtain accurate shearer positioning information. In underground environments without GPS, due to the characteristics of inertial navigation system (INS) autonomous full-parameter navigation, shearer positioning based on INS is adopted by the LASC and CUMT, and error reduction algorithms are proposed to inhibit the rapid error accumulation of INS. In order to obtain the periodic calibration information when the shearer reaches the end of the longwall face, it is necessary to measure the retreat and creep displacements in order to back-correct the shearer trajectory. Finding a suitable measurement method for this task is challenging, especially in the presence of dust and moisture. The LASC used a scanning laser and FMR 250 microwave radar to measure these two displacements, while CUMT adopted an ultra-wideband (UWB) radar. In terms of the backward calibration method, minimum-variance fixed-interval smoothing (MFS) proposed by LASC and the global optimization model (GOM) for the shearer trajectory from CUMT are described in detail. The experiment demonstrates that the GOM outperforms MFS in terms of accuracy but requires more computational resources. Therefore, our next research objective is to develop an efficient and accurate algorithm for performing backward calibration on the shearer trajectory.

Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering

Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path planning method based on multiple hierarchical clustering was proposed. Morphology similarity and the coplanarity measurement method were defined to evaluate the similarity of clusters. The coal seam floor series in the face-advancing direction were clustered according to the morphology similarity and coplanarity, respectively. Taking the morphology-based and coplanarity-based cluster centers as generating lines and stretching angle, respectively, the coal seam floor was reconstructed. The reconstructed floor can be regarded as the cutting path. The coal seam geological model of the 18,201 longwall face was analyzed with the proposed cutting path planning method. Comparing the reconstructed floor and original floor, the amounts of coal left and cut gangue were 1999 m3 and 1856 m3, respectively, for the segmental floor. For the case of whole floor, the amounts of coal left and cut gangue were 5642 m3 and 5463 m3, respectively. The coal loss rates only were 0.57% and 0.87% for the segmental and whole coal seam, respectively.

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis.

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different trade-offs among labor, speed, and accuracy. We deploy this system to extract 26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the data set built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions about chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines.

Coal miner's day in India: a reflection on health risks and legislation

Coal mining is a hazardous profession in India that exposes workers to a range of health risks, including respiratory diseases, skin disorders, hearing loss, and injuries. The coal miner's day, observed on May 4th every year, commemorates the hard work and sacrifices of coal miners. The mines act, 1952, and its associated regulations mandate the provision of medical facilities, safety and health measures, and rescue stations and trained personnel for the rescue of workers. The coal mines (Conservation and development) act, 1974, mandates the conservation of coal resources and the development of coal mines while ensuring the safety and health of workers. However, the implementation of these laws has been inadequate in many cases, leading to a high prevalence of occupational diseases among coal miners. The lack of proper implementation of safety and health regulations has been identified as a key factor contributing to the high prevalence of occupational diseases. Additionally, the existing regulations do not address the specific safety and health issues associated with the use of automation and robotics in mining. To safeguard the health and safety of coal miners, more efforts are needed to ensure the effective implementation of these laws and to provide adequate medical support and rehabilitation services. Prioritizing the health and well-being of coal miners can pay tribute to their contribution to society while promoting a safer and healthier workplace for all.

Human Factors in the Development of the Hydraulic System of the Powered Roof Support

The growing demand for mineral resources, including hard coal, requires the continuous improvement and development of technologies to improve safety, productivity and performance. The implementation of automation and digitization in mines has been ongoing for several years. Machinery and equipment development and improvement is also ongoing. The authors discuss the development and improvement of powered roof support, which is a critical part of the longwall system. The powered roof support is responsible for the safety of equipment and people working in the longwall. It supports the face and immediate roof and moves the longwall system forward as the shearer removes the coal and the roof collapsing (goafing) behind the shields. The aim of this study was to examine the human factors involved in the development and improvement of the hydraulic support system of powered roof support. The research was carried out among employees in hard coal mines using mainly surveys. The research allowed the authors to examine and analyze the information provided by the miners concerning powered roof support in the era of Industry 4.0 (the Fourth Industrial Revolution). According to the research findings, miners expect improvement in the reliability of the powered roof support, and they want the key operating parameters, especially the required load capacity, to be monitored in real-time so adjustments and improvements can be made. The results of the research point out the needs and expectations of miners. Their opinions should determine the direction and any research gaps into powered roof support.

Deep learning implementations in mining applications: a compact critical review

Deep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.

Summary in General Summary of an Overview of Opinion Mining

The internet is a very effective resource for solving all problems in the present era. The world's population as a whole spends one-third of their time and money using the internet. People learn things from it in every aspect of life, including education, entertainment, communication, shopping, etc. In order to achieve this, consumers take use of websites and share comments or opinions about various goods, services, events, etc. based on their personal experiences. In this way, the input from those webs is composed into a sizable amount of textual data that can be investigated, assessed, and controlled for the decision-making process. Natural Language Processing (NLP) and the extraction of the key theme are both aspects of opinion mining (OM). It's crucial to get feedback on our ideas from people in the form of good, negative, and neutral remarks. Researchers are now presenting information in the form of summaries that will benefit a variety of people. Since the 1950s, the research community has generated automatic summaries, but there are two distinct types of automation: abstractive and extractive approaches. A brief summary of opinion mining's automation process approaches and summarization techniques is provided in this paper.

A novel miniaturized sext‐band bandstop filter based on tree‐shaped branches loaded asymmetric multimode resonators

AbstractThe working mechanism of suggested tunable bandstop filtered with asymmetrical multiple‐mode resonator equipped with tree‐shape branches is described in this letter. Because of the established degree of freedom, the suggested bandstop filters’ (BSFs) centre frequencies may be modified based on the design approach. Meanwhile, the suggested BSFs have small dimensions and abrupt roll‐off skirts for transition bands. A technology demonstration filter with six stopbands placed at 1.91/2.38/3.54/5.19/5.79/6.85 GHz was constructed to verify the design and analyses. It is found that the measurement of a filter is consistent with the comparison of simulation data, which indicates that the proposed structure is an optional approach for BSF design. The filter proposed can be applied in the field of communication, information and coal mine automation.

Robust Time Synchronization for Industrial Internet of Things by H ∞ Output Feedback Control

Precise timing over timestamped packet-exchange communication is an enabling technology in the mission-critical industrial Internet of Things (IIoT), particularly when satellite-based timing is unavailable. The main challenge is to ensure timing accuracy when the clock synchronization system is subject to disturbances caused by the drifting frequency, time-varying delay, jitter, and timestamping uncertainty. In this work, a robust packet-coupled oscillators (R-PkCOs) protocol is proposed to reduce the effects of perturbations manifested in the drifting clock, timestamping uncertainty, and delays. First, in the spanning-tree clock topology, time synchronization between an arbitrary pair of clocks is modeled as a state-space model, where clock states are coupled with each other by one-way timestamped packet exchange (referred to as packet coupling), and the impacts of both drifting frequency and delays are modeled as disturbances. A static output controller is adopted to adjust the drifting clock. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> robust control design solution is proposed to guarantee that the ratio between the modulus of synchronization precision and the magnitude of the disturbances are always less than a given value. Therefore, the proposed time synchronization protocol is robust against the disturbances, which means that the impacts of drifting frequency and delays on the synchronization accuracy are limited. The one-hour experimental results demonstrate that the proposed R-PkCO’s protocol can realize time synchronization with the precision of 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> in a 21-node IEEE 802.15.4 network. This work has widespread impacts in the process automation of automotive, mining, oil, and gas industries.

Drill bit wear monitoring and failure prediction for mining automation

This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications. A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling. In this research in-situ vibration signals were analyzed in time-frequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence (AI) models. In addition to the signal statistical features, wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment. Backpropagation artificial neural network (ANN) models were designed, trained and evaluated for bit state classification. Finally, an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language: A Pilot Study.

In recent years, data science approaches have entered health-care systems such as radiology, pathology, and radiation oncology. In our pilot study, we developed an automated data mining approach to extract data from a treatment planning system (TPS) with high speed, maximum accuracy, and little human interaction. We compared the amount of time required for manual data extraction versus the automated data mining technique. A Python programming script was created to extract specified parameters and features pertaining to patients and treatment (a total of 25 features) from TPS. We successfully implemented automation in data mining, utilizing the application programming interface environment provided by the external beam radiation therapy equipment provider for the whole group of patients who were accepted for treatment. This in-house Python-based script extracted selected features for 427 patients in 0.28 ± 0.03 min with 100% accuracy at an astonishing rate of 0.04 s/plan. Comparatively, manual extraction of 25 parameters took an average of 4.5 ± 0.33 min/plan, along with associated transcriptional and transpositional errors and missing data information. This new approach turned out to be 6850 times faster than the conventional approach. Manual feature extraction time increased by a factor of nearly 2.5 if we doubled the number of features extracted, whereas for the Python script, it increased by a factor of just 1.15. We conclude that our in-house developed Python script can extract plan data from TPS at a far higher speed (>6000 times) and with the best possible accuracy compared to manual data extraction.