6,809 research outputs found
Automation in sensing and raw material characterization - A conceptual framework
No full textThe use of sensor technologies for material characterization is rapidly growing and innovative advancement is observed. However, the use of sensor combinations for a raw material characterization in mining is very limited and automation of the material identification process using a combined sensor signal is not defined. Potential sensor technologies for raw material characterization were evaluated based on the applicability and technological maturity. To ensure a rapid implementation of the Real-time mining (RTM) project concept, mature technologies such as Red Green Blue (RGB) imaging, Visible Near Infrared (VNIR) hyperspectral imaging, Short Wave Infrared (SWIR) hyperspectral imaging, Fourier-Transform Infrared Spectroscopy (FTIR), Laser Induced Breakdown Spectroscopy (LIBS) and Raman were selected. Each selected technology was assessed for automation in sensing and applicability (for characterization of the test case materials). Based on the results the sensor data were further considered for data fusion. The proposed sensor combinations approach encompasses three levels of data fusion: low-level, mid-level and high-level. The data of the different sensors are fused together in order to acquire a wide range of mineral properties within each lithotype and an improved classification and predictive models. The preferred level of data fusion and preferred sensor data combinations will be used to develop a multi-variate statistical interpretation rule which relates combination of sensors signals with raw material properties. Thus a tool which integrates the combined sensor signal with materials properties will be developed and used to automate the material characterization process.Accepted Author ManuscriptResource Engineerin
PEMBANGUNAN SISTEM INFORMASI UMAT GEREJA BERBASIS WEB
No full textPendataan umat gereja membutuhkan fungsionalitas tertentu. Pendataan diharapkan dapat dilakukan dengan efektif dan efisien. Suatu sistem yang baik memerlukan suatu mekanisme untuk menjamin keamanan sistem. Berdasarkan masalah yang disebutkan maka penulis akan mencoba membangun sistem untuk menjawab permasalahan tersebut.
Sistem informasi berbasis web merupakan salah satu cara untuk menangani masalah yang dihadapi dalam melakukan pendataan umat gereja. Data umat gereja dapat diinputkan dimana saja dan kapan saja sehingga meningkatkan efektifitas. Pembangunan Sistem Informasi Umat Gereja Berbasis Web (SIMAG) juga menangani pengelolaan stasi, wilayah, lingkungan, mendukung keamanan sistem dengan SMS gateway untuk mengirimkan suatu kode melalui SMS kepada pengguna, dan juga menangani pencarian umat (searching). Aplikasi ini dikembangan dengan tools visual studio 2010 dan database menggunakan SQL Server 2008 menggunakan bahasa pemrograman C#. Dengan adanya SIMAG pengguna dapat melakukan pendataan umat gereja dengan lebih mudah dan aman tanpa dibatasi oleh waktu dan tempat. Selain itu, dengan adanya layanan pencarian(searching) informasi didapatkan dengan cepat dan muda
The Continuing Saga of Globalism: Comparing Ethiopia’s Developmental State Strategies to those of Malaysia
No full textUsing the conceptual framework of a developmental state, forwarded by the Economic Commission for Africa, it was found that Ethiopia’s democratic developmental state is unique and operates differently from the Malaysian developmental state model. Economically, Ethiopia has recorded staggering economic growth since it adopted the developmental state. The Malaysian developmental state was developed to be market-oriented and as a result Malaysia’s GDP grew at 5.23 percent from 2005-2011. Malaysia’s incidence of poverty declined from 49% in 1970 to less than 5% in 2000. Ethiopia has focused on a planned developmental state, without speeding the direction of industrialization, and has achieved an average 9.9 percent growth rate in GDP from 2005-2011. With economic growth, the poverty reduction measured by poverty head count in Ethiopia has declined from 41.9 in 2005 to 29.6 percent in 2011. Although Ethiopia’s Human Development Index (HDI) has increased by 16% from 2005 to 2011, its HDI score is about 22% less than the average score of sub-Saharan countries. The poverty ratio of people living on less than $1.25 a day in Ethiopia is very close to about 30 percent. It means that those with low incomes have not benefitted from the staggering economic growth that much of the country has achieved in the last seven years. The theory of a developmental state model proposes that professional bureaucratic elites design, manage, and run autonomously the short and long range plans of a country. In the Ethiopian “Democratic Developmental State” the short and long term plans are controlled by the ruling party, EPRD. The public and private sectors are minimally involved in the planning process. The economic growth rate which Ethiopia achieved from 2005 to 2011 doesn’t operate in a fully market-friendly environment. Far from operating in a developmental state, Ethiopia operates under a centralized system. Also, in an era of economic globalism, the Malaysian type of developmental state model is outdated and is no longer relevant for Ethiopia. Therefore, to achieve optimal sustainable development, and thereby feed its people, Ethiopia must focus on an integrated, environmentally sensitive, and cooperative agriculturally-based type of development model
Sensing and data fusion opportunities for raw material characterisation in mining: Technology and data-driven approach
No full textThe rising demands for mined products lead to the extraction of materials in geologically complex regions. This calls for mining process changes and interventions driven by technology and advanced data analytics. The dynamic development of state-of-the-art sensor technologies and their potential use in mining is projected to significantly reduce costs in the industry. However, despite rapid advances in sensor technologies, there is still a demand for novel data analytical approaches to enable accurate characterisation of material along the mining value chain, as advanced data analytics is key to gain knowledge from the complex sensor-derived data. Therefore, sensor technology, coupled with advanced data analytics is crucial for the rapid and accurate characterisation of material in mining operations. Access to rapid and accurate data on the key geological attributes (e.g., mineralogy and geochemistry) along the mining value chain has significant implications for the production process efficiency in commercial mines. Such data would greatly assist the improvement of deposit models, optimise ore processing, specify product quality and improve operational decision-making. Sensor technologies operate over a specific range of the electromagnetic spectrum and provide information on certain aspects of material properties that are of potential interest for mining extraction. However, a single sensor might not provide a sufficiently comprehensive description of a material’s composition. This introduces uncertainty into both resource estimation and requirements definition for mineral processing. Thus, it is necessary to utilise strategic sensor combinations to improve accuracy, minimise uncertainty, and enhance specific insights of material compositions. Combinations of sensors can be implemented using a data fusion approach. The fusion of sensed data can be realised at different levels: low-, mid-, and high-level, when the integration occurs at the data level, features level and decision level, respectively. This research aims to develop methods for the characterisation of raw materials using multiple sensor technologies and sensor combinations concept (data fusion at different levels), that can be potentially applicable to mining operations. The study involved the multispectral and hyperspectral imaging techniques, such as red-green-blue (RGB) imaging, visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging, and point spectroscopic techniques, such as mid-wave infrared (MWIR), long-wave infrared (LWIR) and Raman spectroscopy to acquire spectral information over a wider range of the electromagnetic spectrum. First, an investigation was conducted on the usability of the individual sensor technologies coupled with data analytics for the characterisation of a polymetallic sulphide deposit at different levels. The different levels of material characterisation aimed to allow mineral mapping, ore–waste discrimination, fragmentation analysis, and semi-quantitative analysis of elements and minerals. The positive outcomes of the use of the individual techniques led to the development of a data fusion framework that enables data integration (including multi-scale and multi-resolution data) at different levels (e.g., low-level and mid-level). The developed data fusion concept was implemented and validated using different test scenarios..
Intrinsic steady alternate bars in alluvial channels. Part 1: Experimental observations and numerical tests.
No full textAlternate bars in straight alluvial channels are migrating or steady. The currently accepted view is that they are steady only if the width-to-depth ratio is at the value of resonance or if the bars are forced by a steady local perturbation. Experimental observations, however, seem to indicate that steady bars are also present in cases of migrating bars in the absence of a persistent perturbation. The companion paper by Mosselman (2009) provides a theoretical explanation. We review some experimental observations as well as long-term numerical tests using a 2D depth-averaged morphological model of a straight channel with non-erodible banks. Small random variations in total discharge are imposed at the upstream boundary. Rapidly growing migrating bars are found to develop first, but slowly growing steady bars are found to evolve subsequently, starting either from upstream or from downstream. Since steady bars are seen as a prerequisite to explain meandering of alluvial rivers, our findings imply that neither resonant width-to-depth ratios nor steady local perturbations are necessary conditions for the onset of river meandering.Hydraulic EngineeringCivil Engineering and Geoscience
Image and point data fusion for enhanced discrimination of ore and waste in mining
No full textSensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data-driven strategies. In the present study, the fusion of image and point data is proposed, aiming for the enhanced classification of ore and waste materials in a polymetallic sulphide deposit at 3%, 5% and 7% cut-off grades. The image data were acquired in the visible-near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. The point data cover the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions. A multi-step methodological approach was developed for the fusion of the image and point data at multiple levels using the supervised and unsupervised classification techniques. Several possible combinations of the data blocks were evaluated to select the optimal combinations in an optimised way. The obtained results indicate that the individual image and point techniques resulted in a successful classification of ore and waste materials. However, the classification performance greatly improved with the fusion of image and point data, where the K-means and support vector classification (SVC) models provided acceptable results. The proposed approach enables a significant reduction in data volume while maintaining the relevant information in the spectra. This is principally beneficial for the integration of data from high-throughput and large data volume sources. Thus, the effectiveness and practicality of the approach can permit the enhanced separation of ore and waste materials in operational mines.Resource Engineerin
The use of RGB Imaging and FTIR Sensors for Mineral mapping in the Reiche Zeche underground test mine, Freiberg
No full textThe application of sensor technologies for raw material characterization is rapidly growing, and innovative advancement of the technologies is observed. Sensors are being used as laboratory and in-situ techniques for characterization and definition of raw material proper-ties. However, application of sensor technologies for underground mining resource extrac-tion is very limited and highly dependent on the geological and operational environment. In this study the potential of RGB imaging and FTIR spectroscopy for the characterization of polymetallic sulphide minerals in a test case of Freiberg mine was investigated. A defined imaging procedure was used to acquire RGB images. The images were georeferenced, mosaicked and a mineral map was produced using a supervised image classification tech-nique. Five mineral types have been identified and the overall classification accuracy shows the potential of the technique for the delineation of sulphide ores in an underground mine. FTIR data in combination with chemometric techniques were evaluated for discrimi-nation of the test case materials. Experimental design was implemented in order to identify optimal pre-processing strategies. Using the processed data, PLS-DA classification mo-dels were developed to assess the capability of the model to discriminate the three materi-al types. The acquired calibration and prediction statistics show the approach is efficient and provides acceptable classification success. In addition, important variables (wavel-ength location) responsible for the discrimination of the three materials type were identifi-ed.Resource Engineerin
Chemometric Analysis of Mid-Wave Infrared Spectral Reflectance Data for Sulphide Ore Discrimination
No full textDespite significant recent advancements in the sensor technologies, the use of sensors for raw material characterization in the mining industry remains limited. The aim of the present study was to assess the utility of applying the mid-wave infrared (MWIR) reflectance data acquired by the use of a handheld Fourier-transform infrared spectrometer (FTIR), combined with partial least squares-discriminant analysis (PLS-DA), for the characterization of a polymetallic sulphide ore deposit. In achieving the study objectives, focus was given to the MWIR portion of the FTIR dataset, as it is the least explored region of the infrared spectrum in mineral characterization studies. Three datasets—covering different wavelength ranges—were generated from the FTIR spectral data, namely the full FTIR range (2.5–15 µm), MWIR (2.5–7 µm) and long-wave infrared (LWIR: 7–15 µm), in order to investigate the associated information level of each defined wavelength region separately. Design of experiment was developed to determine the optimal data filtering techniques. Using the processed data and PLS-DA, a series of calibration and prediction models were developed for ore and waste materials separately. As the models applied to the MWIR data showed a successful classification rate of 86.3% for sulphide ore–waste discrimination, similarly using the full spectral FTIR dataset, a correct classification rate of 89.5% was achieved. This indicates that MWIR spectral range includes informative signals that are sufficient for classifying the material into ore or waste. The proposed approach could be extended for automating the sulphide ore–waste discrimination process, thus greatly benefiting marginally economical mining operations.Resource Engineerin
Study Of Mercaptobenzimidazoles As Inhibitors For Copper Corrosion: Down to the Molecular Scale
No full textThe initiation of corrosion can be triggered by defects in the adsorbed layer of organic inhibitors. A detailed knowledge of the intermolecular forces between the inhibitor molecules and the interfacial bonding will be decisive to unravel the mechanisms driving the corrosion initiation. In this work, adsorbed organic layers of 2-mercapto-5-methoxybenzimidazole (SH-BimH-5OMe) and 5-amino-2-mercaptobenzimidazole (SH-BimH-5NH2) were compared regarding their performance mitigating copper corrosion. Atomic force microscopy was used to address the stability and intermolecular forces of the self-assembled monolayers, using imaging and force measurement modes. For a film formed by amino-derivative molecules, a gold-coated tip frequently picked up individual molecules (molecular fishing) in force-distance measurements. For layers of the methoxy-derivative, no fishing events were observed, pointing to a constant functional layer. X-ray photoelectron spectroscopy revealed that SH-BimH-5OMe molecules form a stronger bond with the surface and more stable SAM layers on Cu surfaces as compared to SH-BimH-5NH2 molecules. Results of computational density functional theory modeling and electrochemical corrosion tests are in line with the microscopy and spectroscopy results. In particular, with aid of computational modeling the less ordered structure of the SH-BimH-5NH2 monolayer is attributed to dual bonding ability of SH-BimH-5NH2 that can adsorb with either S or NH2 groups.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Evaluation of sensor technologies for on-line raw material characterization in “Reiche Zeche” underground mine - outcomes of RTM implementation
No full textThe increasing advances in sensor technology have resulted in greater availability of sensor data for a wide range of applications. One such application is raw material characterization in mining operations. Sensor technologies operate over certain range of the electromagnetic spectrum and provide information on several aspects of material properties. The sensitivity and the material properties the instrument detects and measures varies from sensor to sensor. The purpose of this study was to synthesize and evaluate the use of sensor technologies for characterization of a polymetallic sulphide deposit in “Reiche Zeche” underground mine. This paper discusses the material characterization methodology using sensor technologies, demonstrates how it fits within the Real-Time Mining (RTM) framework, identifies the interface for both software and hardware requirements and defines the gaps and limitations of application of sensors. It provides a brief overview of the use of sensor and data fusion for material characterization to convey a high-level context in raw material characterization. The sensor technologies considered in this study include RGB imaging, visible–near infrared (VNIR), short wave infrared (SWIR), mid-wave infrared (MWIR), long-wave infrared (LWIR) and Raman spectroscopy.The required information from sensor data in mining operations is not limited to grade control applications. Information on co-occurring minerals or elements are also important for definition of requirements in mineral processing, to identify indirect proxies of elements/minerals of interest, to understand the formation of minerals, to define requirements for blasting parameters, to improve safety and to define requirements for environmental monitoring of toxic material. In view of these points, there is a need for combinations of sensors to achieve a near complete description of material composition and properties. The methodological approaches developed for information extraction from each sensor data and fused data are presented. This includes both direct mineral fingerprinting and indirect proxies using spectral data. The efficient sensor data processing methods and the acquired results from the use of individual sensor and the fused data are summarized. Overall, the acquired results from the use of each sensor technology and the data fusion approach significantly contributed to an improvement of data quality and illustrate the efficiency of use of sensors in the mining industry. However, some of the observed limitations include lack of system robustness, a need for test case specific mineral libraries, the need for development of an integrated principled tool for efficient data collection, processing and knowledge generation. Going forward, automated material characterization is possible with robust system design (exemplified by portable and ruggedized system) and efficient software (test case specific mineral libraries) that can be developed using a combined sensor signal.Resource Engineerin
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