Search results for: mineral trioxide aggregate

Authors: Dheyaa Wajid Abbood, Mohanad Salih Farhan, Awadh E. Ajeel

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The basic goal of this study is the production of cheap building elements from plastic waste. environmentally friendly and of good thermal insulation. The study depends on the addition of plastic waste as aggregates to the mixes of concrete at different percentages by weight (12 percentages) to produce lightweight aggregate concrete the density (1095 - 1892) kg/m3.The experimental work includes 120 specimens of concrete 72 cubes (150*150*150)mm, 48 cylinder (150*300) mm. The results obtained for concrete were for local raw materials without any additional materials or treatment. The mechanical and thermal properties determined were (compressive strength, static modulus of elasticity, density, thermal conductivity (k), specific heat capacity (Cp), thermal expansion (α) after (7) days of curing at 20 0C. The increase in amount of plastic waste decreases the density of concrete which leads to decrease in the mechanical and to improvement in thermal properties. The average measured static modulus of elasticity are found less than the predicted static modulus of elasticity and splitting tensile strength (ACI 318-2008 and ACI 213R-2003). All cubes specimens when exposed to heat at (200, 400, 600 0C), the compressive strength of all mixes decreases gradually at 600 0C, the strength of lightweight aggregate concrete were disintegrated. Lightweight aggregate concrete is about 25% lighter than normal concrete in dead load, and to the improve the properties of thermal insulation of building blocks.

Keywords: LWAC, plastic waste, thermal property, thermal insulation

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Authors: Ishan Arora, Anurag Rathore

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The primary objective of this work was to study the effect of resin chemistry, pH and molarity of binding and elution buffer on aggregate removal using Cation Exchange Chromatography and find the optimum conditions which can give efficient aggregate removal with minimum loss of yield. Four different resins were used for carrying out the experiments: Fractogel EMD SO3-(S), Fractogel EMD COO-(M), Capto SP ImpRes and S Ceramic HyperD. Runs were carried out on the AKTA Avant system. Design of Experiments (DOE) was used for analysis using the JMP software. The dependence of the yield obtained using different resins on the operating conditions was studied. Success has been achieved by obtaining yield greater than 90% using Capto SP ImpRes and Fractogel EMD COO-(M) resins. It has also been found that a change in the operating conditions generally has different effects on the yields obtained using different resins.

Keywords: aggregates, cation exchange chromatography, design of experiments, monoclonal antibodies

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Authors: Ana Paula Almeida Castaldelli Maciel, Gabriela Medeiros, Amanda de Souza Machado, Maria Clara Pilatti, Ralpho Rinaldo dos Reis, Silvio Cesar Sampaio

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This study evaluates the long-term effects of swine wastewater (SWW) on soil parameters in an agricultural area with years of crop cultivation. Three types of SWW (raw, after leaving the biodigester, and after the manure plant) were analyzed, both with and without mineral fertilization. The study found that the long-term use of SWW had significant effects on soil parameters. Principal Component Analysis (PCA) was used to summarize the data. The soil's calcium (Ca) and magnesium. (Mg), and cation exchange capacity (CEC) levels were higher in soybeans compared to other crops and natural soil. Similarly, the treatment with 0m3.ha-1 of pig manure and without mineral fertilization showed higher levels of these nutrients. In contrast, potassium (K) was found in greater quantities in oats, SWW from the biodigester, higher doses of manure, and mineral fertilization. The crops had a higher organic matter (OM) content compared to the natural soil, with corn and raw SWW showing the most significant increase.

Keywords: contamination, water research, biodigester, nutrients

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Kolela J Nyembwe, Darlington C. Ashiegbu, Herman J. Potgieter

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Over the past few decades, the demand for metals has increased significantly. This has led to a decrease and decline of high-grade ore over time and an increase in mineral complexity and matrix heterogeneity. In addition to that, there are rising concerns about greener processes and a sustainable environment. Due to these challenges, the mining and metal industry has been forced to develop new technologies that are able to economically process and recover metallic values from low-grade ores, materials having a metal content locked up in industrially processed residues (tailings and slag), and complex matrix mineral deposits. Several methods to address these issues have been developed, among which are ionic liquids (IL), heap leaching, and bioleaching. Recently, the gas phase extraction technique has been gaining interest because it eliminates many of the problems encountered in conventional mineral processing methods. The technique relies on the formation of volatile metal complexes, which can be removed from the residual solids by a carrier gas. The complexes can then be reduced using the appropriate method to obtain the metal and regenerate-recover the organic extractant. Laboratory work on the gas phase have been conducted for the extraction and recovery of aluminium (Al), iron (Fe), copper (Cu), chrome (Cr), nickel (Ni), lead (Pb), and vanadium V. In all cases the extraction revealed to depend of temperature and mineral surface area. The process technology appears very promising, offers the feasibility of recirculation, organic reagent regeneration, and has the potential to deliver on all promises of a “greener” process.

Keywords: gas-phase extraction, hydrometallurgy, low-grade ore, sustainable environment

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Authors: Moses C. Siame, Kazutoshi Haga, Atsushi Shibayama

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This study investigates the removal of silica, alumina and phosphorus as impurities from Sanje iron ore using wet high-intensity magnetic separation (WHIMS). Sanje iron ore contains low-grade hematite ore found in Nampundwe area of Zambia from which iron is to be used as the feed in the steelmaking process. The chemical composition analysis using X-ray Florence spectrometer showed that Sanje low-grade ore contains 48.90 mass% of hematite (Fe₂O₃) with 34.18 mass% as an iron grade. The ore also contains silica (SiO₂) and alumina (Al₂O₃) of 31.10 mass% and 7.65 mass% respectively. The mineralogical analysis using X-ray diffraction spectrometer showed hematite and silica as the major mineral components of the ore while magnetite and alumina exist as minor mineral components. Mineral particle distribution analysis was done using scanning electron microscope with an X-ray energy dispersion spectrometry (SEM-EDS) and images showed that the average mineral size distribution of alumina-silicate gangue particles is in order of 100 μm and exists as iron-bearing interlocked particles. Magnetic separation was done using series L model 4 Magnetic Separator. The effect of various magnetic separation parameters such as magnetic flux density, particle size, and pulp density of the feed was studied during magnetic separation experiments. The ore with average particle size of 25 µm and pulp density of 2.5% was concentrated using pulp flow of 7 L/min. The results showed that 10 T was optimal magnetic flux density which enhanced the recovery of 93.08% of iron with 53.22 mass% grade. The gangue mineral particles containing 12 mass% silica and 3.94 mass% alumna remained in the concentrate, therefore the concentrate was further treated in the second stage WHIMS using the same parameters from the first stage. The second stage process recovered 83.41% of iron with 67.07 mass% grade. Silica was reduced to 2.14 mass% and alumina to 1.30 mass%. Accordingly, phosphorus was also reduced to 0.02 mass%. Therefore, the two stage magnetic separation process was established using these results.

Keywords: Sanje iron ore, magnetic separation, silica, alumina, recovery

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Authors: Fanous Mohammadi, Majid H. Tangestani, Mohammad H. Tayebi

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This research aims to evaluate and compare Geographical Information Systems (GIS)-based fuzzy models for producing orogenic gold prospectivity maps in the Saqqez area, NW of Iran. Gold occurrences are hosted in sericite schist and mafic to felsic meta-volcanic rocks in this area and are associated with hydrothermal alterations that extend over ductile to brittle shear zones. The predictor maps, which represent the Pre-(Source/Trigger/Pathway), syn-(deposition/physical/chemical traps) and post-mineralization (preservation/distribution of indicator minerals) subsystems for gold mineralization, were generated using empirical understandings of the specifications of known orogenic gold deposits and gold mineral systems and were then pre-processed and integrated to produce mineral prospectivity maps. Five fuzzy logic operators, including AND, OR, Fuzzy Algebraic Product (FAP), Fuzzy Algebraic Sum (FAS), and GAMMA, were applied to the predictor maps in order to find the most efficient prediction model. Prediction-Area (P-A) plots and field observations were used to assess and evaluate the accuracy of prediction models. Mineral prospectivity maps generated by AND, OR, FAP, and FAS operators were inaccurate and, therefore, unable to pinpoint the exact location of discovered gold occurrences. The GAMMA operator, on the other hand, produced acceptable results and identified potentially economic target sites. The P-A plot revealed that 68 percent of known orogenic gold deposits are found in high and very high potential regions. The GAMMA operator was shown to be useful in predicting and defining cost-effective target sites for orogenic gold deposits, as well as optimizing mineral deposit exploitation.

Keywords: mineral prospectivity mapping, fuzzy logic, GIS, orogenic gold deposit, Saqqez, Iran

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Authors: Samad Alipour, Khadije Mosavi Onlaghi

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Urmia Salt Lake (USL) is a hypersaline lake in the northwest of Iran. It contains halite as main dissolved and precipitated mineral and the major mineral mixed with lake bed sediments. Other detrital minerals such as calcite, aragonite, dolomite, quartz, feldspars, augite are forming lake sediments. This study examined the impact of weathering of this sediments collected from 1.5 meters depth and augite placers. The study indicated that weathering of tephritic and adakite rocks of the Islamic Island at the immediate boundary of the lake play a main control of lake bed sediments and has produced a large volume of augite placer along the lake bank. Weathering increases from south to toward north with increasing distance from Islamic Island. Geochemistry of lake sediments demonstrated the enrichment of MgO, CaO, Sr with an elevated anomaly of Eu, possibly due to surface absorbance of Mn and Fe associated Sr elevation originating from adakite volcanic rocks in the vicinity of the lake basin. The study shows the local geology is the major factor in origin of lake sediments than chemical and biochemical produced mineral during diagenetic processes.

Keywords: Urmia Lake, weathering, mineralogy, augite, Iran

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Authors: Vladimira Vytlacilova

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This study presents the latest research trend in the discipline of construction and demolition (C&D) waste management in Czech Republic. The results of research interest exhibit an increasing research interest in C&D waste management practices in recent years. Construction and demolition waste creates a major portion of total solid waste production in the world and most of it is used in landfills, for reclamation or landscaping all the time. The quality of recycled aggregates for use in concrete construction depends on recycling practices. Classifications, composition and contaminants influence the mechanical-physical properties as well as environmental risks related to its utilization. The second part of contribution describes properties of fibre reinforced concrete with the full replacement of natural aggregate by recycled one (concrete or masonry rubble).

Keywords: construction and demolition waste, fibre reinforced concrete, recycled aggregate, recycling, waste management

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Authors: Flóra Pomázi, Sándor Baranya, Zoltán Szalai

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A harmonized monitoring of the suspended sediment transport along such a large river as the world’s most international river, the Danube River, is a rather challenging task. The traditional monitoring method in Hungary is obsolete but using indirect measurement devices and techniques like optical backscatter sensors (OBS), laser diffraction or acoustic backscatter sensors (ABS) could provide a fast and efficient alternative option of direct methods. However, these methods are strongly sensitive to the particle characteristics (i.e. particle shape, particle size and mineral composition). The current method does not provide sufficient information about particle size distribution, mineral analysis is rarely done, and the shape of the suspended sediment particles have not been examined yet. The aims of the study are (1) to determine the particle characterisation of suspended sediment in the Danube River using advanced particle characterisation methods as laser diffraction and automated imaging, and (2) to perform a sensitivity analysis of the indirect methods in order to determine the impact of suspended particle characteristics. The particle size distribution is determined by laser diffraction. The particle shape and mineral composition analysis is done by the Morphologi G3ID image analyser. The investigated indirect measurement devices are the LISST-Portable|XR, the LISST-ABS (Sequoia Inc.) and the Rio Grande 1200 kHz ADCP (Teledyne Marine). The major findings of this study are (1) the statistical shape of the suspended sediment particle - this is the first research in this context, (2) the actualised particle size distribution – that can be compared to historical information, so that the morphological changes can be tracked, (3) the actual mineral composition of the suspended sediment in the Danube River, and (4) the reliability of the tested indirect methods has been increased – based on the results of the sensitivity analysis and the previous findings.

Keywords: advanced particle characterisation, automated imaging, indirect methods, laser diffraction, mineral composition, suspended sediment

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Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira

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Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.

Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing

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Authors: Jozef Junak, Nadezda Stevulova

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This paper is aimed to the use of different types of industrial wastes in concrete production. From examined waste (crushed concrete waste) our tested concrete samples with dimension 150 mm were prepared. In these samples, fractions 4/8 mm and 8/16 mm by recycled concrete aggregate with a range of variation from 0 to 100% were replaced. Experiment samples were tested for compressive strength after 2, 7, 14 and 28 days of hardening. From obtained results it is evident that all samples prepared with washed recycled concrete aggregates met the requirement of standard for compressive strength of 20 MPa already after 14 days of hardening. Sample prepared with recycled concrete aggregates (4/8 mm: 100% and 8/16 mm: 60%) reached 101% of compressive strength value (34.7 MPa) after 28 days of hardening in comparison with the reference sample (34.4 MPa). The lowest strength after 28 days of hardening (27.42 MPa) was obtained for sample consisting of recycled concrete in proportion of 40% for 4/8 fraction and 100% for 8/16 fraction of recycled concrete.

Keywords: recycled concrete aggregate, re-use, workability, compressive strength

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Authors: B. M. Pardeshi

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Introduction: Minerals and trace elements are chemical elements required by our bodies for numerous biological and physiological processes that are necessary for the maintenance of health. Medicinal plants are highly beneficial for the maintenance of good health and prevention of diseases. They are known as potential sources of minerals and vitamins. 30 to 40% of today’s conventional drugs used in the medicinal and curative properties of various plants are employed in herbal supplement botanicals, nutraceuticals and drug. Aim: The authors explored the mineral element content of some herbs, because mineral elements may have significant role in the development and treatment of gastrointestinal diseases, and a close connection between the presence or absence of mineral elements and inflammatory mediators was noted. Methods: Present study deals with the elemental analysis of medicinal plants by Instrumental Neutron activation Analysis and Atomic Absorption Spectroscopy. Medicinal herbals prescribed for skin diseases were purchased from markets and were analyzed by Instrumental Neutron Activation Analysis (INAA) using 252Cf Californium spontaneous fission neutron source (flux* 109 n s-1) and the induced activities were counted by γ-ray spectrometry and Atomic Absorption Spectroscopy (AAS) techniques (Perkin Elmer 3100 Model) available at Department of Chemistry University of Pune, India, was used for the measurement of major, minor and trace elements. Results: 15 elements viz. Al, K, Cl, Na, Mn by INAA and Cu, Co, Pb Ni, Cr, Ca, Fe, Zn, Hg and Cd by AAS were analyzed from different medicinal plants from India. A critical examination of the data shows that the elements Ca , K, Cl, Al, and Fe are found to be present at major levels in most of the samples while the other elements Na, Mn, Cu, Co, Pb, Ni, Cr, Ca, Zn, Hg and Cd are present in minor or trace levels. Conclusion: The beneficial therapeutic effect of the studied herbs may be related to their mineral element content. The elemental concentration in different medicinal plants is discussed.

Keywords: instrumental neutron activation analysis, atomic absorption spectroscopy, medicinal plants, trace elemental analysis, mineral contents

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Authors: Sergio Perez Rodriguez

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Coltan Artisanal and Small-Scale Mining (ASM) production from Africa's Great Lakes region has previously been addressed at large scales, notably from regional to country levels. The current findings address the unresolved issue of a production model of ASM of coltan ore by an average Democratic Republic of Congo (DRC) mineworker, which can be used as a reference for a similar characterization of the daily labor of counterparts from other countries in the region. To that end, the Fundamental Equation of Mineral Production has been applied, considering a miner's average daily output of coltan, estimated in the base of gross statistical data gathered from reputable sources. Results indicate daily yields of individual miners in the order of 300 g of coltan ore, with hourly peaks of production in the range of 30 to 40 g of the mineral. Yields are expected to be in the order of 5 g or less during the least productive hours. These outputs are expected to be achieved during the halves of the eight to ten hours of daily working sessions that these artisanal laborers can attend during the mining season.

Keywords: coltan, mineral production, production to reserve ratio, artisanal mining, small-scale mining, ASM, human work, Great Lakes region, Democratic Republic of Congo

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Authors: Svetlana V. Topolyanskaya, Tatyana A. Eliseeva, Olga N. Vakulenko, Leonid I. Dvoretski

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Introduction: Limited data are available on osteoporosis in centenarians. Therefore, we evaluated bone mineral density in long-living patients with coronary artery disease (CAD). Methods: 202 patients hospitalized with CAD were enrolled in this cross-sectional study. The patients' age ranged from 90 to 101 years. The majority of study participants (64.4%) were women. The main exclusion criteria were any disease or medication that can lead to secondary osteoporosis. Bone mineral density (BMD) was measured by dual-energy X-ray absorptiometry. Results: Normal lumbar spine BMD was observed in 40.9%, osteoporosis – in 26.9%, osteopenia – in 32.2% of patients. Normal proximal femur BMD values were observed in 21.3%, osteoporosis – in 39.9%, and osteopenia – in 38.8% of patients. Normal femoral neck BMD was registered only in 10.4% of patients, osteoporosis was observed in 60.4%, osteopenia in 29.2%. Significant positive correlation was found between all BMD values and body mass index of patients (p < 0.001). Positive correlation was registered between BMD values and serum uric acid (p=0.0005). The likelihood of normal BMD values with hyperuricemia increased 3.8 times, compared to patients with normal uric acid, who often have osteoporosis (Odds Ratio=3.84; p = 0.009). Positive correlation was registered between all BMD values and body mass index (p < 0.001). Positive correlation between triglycerides levels and T-score (p=0.02), but negative correlation between BMD and HDL-cholesterol (p=0.02) were revealed. Negative correlation between frailty severity and BMD values (p=0.01) was found. Positive correlation between BMD values and functional abilities of patients assessed using Barthel index (r=0,44; p=0,000002) and IADL scale (r=0,36; p=0,00008) was registered. Fractures in history were observed in 27.6% of patients. Conclusions: The study results indicate some features of BMD in long-livers. In the study group, significant relationships were found between bone mineral density on the one hand, and patients' functional abilities on the other. It is advisable to further study the state of bone tissue in long-livers involving a large sample of patients.

Keywords: osteoporosis, bone mineral density, centenarians, coronary artery disease

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Authors: Byomakesh Debata, Jitendra Mahakud

Abstract:

The recent financial crisis has been characterized by massive monetary policy interventions by the Central bank, and it has amplified the importance of liquidity for the stability of the stock market. This paper empirically elucidates the actual impact of monetary policy interventions on stock market liquidity covering all National Stock Exchange (NSE) Stocks, which have been traded continuously from 2002 to 2015. The present study employs a multivariate VAR model along with VAR-granger causality test, impulse response functions, block exogeneity test, and variance decomposition to analyze the direction as well as the magnitude of the relationship between monetary policy and market liquidity. Our analysis posits a unidirectional relationship between monetary policy (call money rate, base money growth rate) and aggregate market liquidity (traded value, turnover ratio, Amihud illiquidity ratio, turnover price impact, high-low spread). The impulse response function analysis clearly depicts the influence of monetary policy on stock liquidity for every unit innovation in monetary policy variables. Our results suggest that an expansionary monetary policy increases aggregate stock market liquidity and the reverse is documented during the tightening of monetary policy. To ascertain whether our findings are consistent across all periods, we divided the period of study as pre-crisis (2002 to 2007) and post-crisis period (2007-2015) and ran the same set of models. Interestingly, all liquidity variables are highly significant in the post-crisis period. However, the pre-crisis period has witnessed a moderate predictability of monetary policy. To check the robustness of our results we ran the same set of VAR models with different monetary policy variables and found the similar results. Unlike previous studies, we found most of the liquidity variables are significant throughout the sample period. This reveals the predictability of monetary policy on aggregate market liquidity. This study contributes to the existing body of literature by documenting a strong predictability of monetary policy on stock liquidity in an emerging economy with an order driven market making system like India. Most of the previous studies have been carried out in developing economies with quote driven or hybrid market making system and their results are ambiguous across different periods. From an eclectic sense, this study may be considered as a baseline study to further find out the macroeconomic determinants of liquidity of stocks at individual as well as aggregate level.

Keywords: market liquidity, monetary policy, order driven market, VAR, vector autoregressive model

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Authors: Julian Wise

Abstract:

Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: B. M. Pardeshi

Abstract:

Introduction: Minerals and trace elements are chemical elements required by our bodies for numerous biological and physiological processes that are necessary for the maintenance of health. Medicinal plants are highly beneficial for the maintenance of good health and prevention of diseases. They are known as potential sources of minerals and vitamins. 30 to 40% of today’s conventional drugs used in the medicinal and curative properties of various plants are employed in herbal supplement botanicals, nutraceuticals and drug. Aim: The authors explored the mineral element content of some herbs, because mineral elements may have significant role in the development and treatment of gastrointestinal diseases, and a close connection between the presence or absence of mineral elements and inflammatory mediators was noted. Methods: Present study deals with the elemental analysis of medicinal plants by Instrumental Neutron activation Analysis and Atomic Absorption Spectroscopy. Medicinal herbals prescribed for skin diseases were purchased from markets and were analyzed by Instrumental Neutron Activation Analysis (INAA) using 252Cf Californium spontaneous fission neutron source (flux * 109 n s-1) and the induced activities were counted by γ-ray spectrometry and Atomic Absorption Spectroscopy (AAS) techniques (Perkin Elmer 3100 Model) available at Department of Chemistry University of Pune, INDIA, was used for the measurement of major, minor and trace elements. Results: 15 elements viz. Al, K, Cl, Na, Mn by INAA and Cu, Co, Pb, Ni, Cr, Ca, Fe, Zn, Hg and Cd by AAS were analyzed from different medicinal plants from India. A critical examination of the data shows that the elements Ca , K, Cl, Al, and Fe are found to be present at major levels in most of the samples while the other elements Na, Mn, Cu, Co, Pb, Ni, Cr, Ca, Zn, Hg and Cd are present in minor or trace levels. Conclusion: The beneficial therapeutic effect of the studied herbs may be related to their mineral element content. The elemental concentration in different medicinal plants is discussed.

Keywords: instrumental neutron activation analysis, atomic absorption spectroscopy, medicinal plants, trace elemental analysis, mineral contents

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Authors: Kyoungjin Kim

Abstract:

Nanoscale thermites such as the composite mixture of nano-sized aluminum and molybdenum trioxide powders possess several technical advantages such as much higher reaction rate and shorter ignition delay, when compared to the conventional energetic formulations made of micron-sized metal and oxidizer particles. In this study, the self-propagation of combustion wave in compacted pellets of nanoscale thermite composites is modeled and computationally investigated by utilizing the activation energy reduction of aluminum particles due to nanoscale particle sizes. The present computational model predicts the speed of combustion wave propagation which is good agreement with the corresponding experiments of thermite reaction. Also, several characteristics of thermite reaction in nanoscale composites are discussed including the ignition delay and combustion wave structures.

Keywords: nanoparticles, thermite reaction, combustion wave, numerical modeling

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Authors: Mohamed Nasraoui

Abstract:

Spatial ore distribution, ore heterogeneity and their links with geological processes involved in Niobium concentration are all factors for consideration when bridging field observations to extraction scheme. Indeed, mineralogy changes of Nb-hosting phases, their textural relationships with hydrothermal or secondary minerals, play a key control over mineral processing. This study based both on filed work and ore characterization presents data from several Nb-deposits related to carbonatite complexes. The results obtained by a wide range of analytical techniques, including, XRD, XRF, ICP-MS, SEM, Microprobe, Spectro-CL, FTIR-DTA and Mössbauer spectroscopy, demonstrate how geometallurgical assessment, at all stage of mine development, can greatly assist in the design of a suitable extraction flowsheet and data reconciliation.

Keywords: carbonatites, Nb-geometallurgy, Nb-mineralogy, mineral processing.

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Authors: S. N. Deepa, A. D. Srinivasan, K. T. Veeramanju, R. Sandeep Kumar, Ashwini Mathapati

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Power transformer is a vital element in a power system as it continuously regulates power flow, maintaining good voltage regulation. The working of transformer much depends on the oil insulation, the oil insulation also decides the aging of transformer and hence its reliability. The mineral oil based liquid insulation is globally accepted for power transformer insulation; however it is potentially hazardous due to its non-biodegradability. In this work efficient alternative biodegradable insulating fluid is presented as a replacement to conventional mineral oil. Dielectric tests are performed as distinct alternating fluid to evaluate the suitability for transformer insulation. The selection of the distinct natural esters for an insulation system is carried out by the laboratory investigation of Breakdown voltage, Oxidation stability, Dissipation factor, Permittivity, Viscosity, Flash and Fire point. It is proposed to study and characterize the properties of natural esters to be used in power transformer. Therefore for the investigation of the dielectric behavior rice bran oil, sesame oil, and sunflower oil are considered for the study. The investigated results have been compared with the mineral oil to validate the dielectric behavior of natural esters.

Keywords: alternative insulating fluid, dielectric properties, natural esters, power transformers

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Authors: Mary Anne Roa

Abstract:

Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

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Authors: Fahed Alrshoudi

Abstract:

Saudi Arabia is ranked the third of the largest suppliers of Dates worldwide (about 28.5 million palm trees), producing more than 2 million tons of dates yearly. These trees produce large quantity of dates palm seeds (DPS) which can be considered literally as a waste. The date seeds are stiff, therefore, it is possible to utilize DPS as coarse aggregates in lightweight concrete for certain structural applications and to participate at reusing the waste. The use of DPS as coarse aggregate in concrete can be an alternative choice as a partial replacement of the stone aggregates (SA). This paper reports the influence of partially replaced stone aggregates with DPS on the hardened properties of concrete performance. Based on the experimental results, the DPS has the potential use as an acceptable alternative aggregates in producing structural lightweight concrete members, instead of stone aggregates.

Keywords: compressive strength, tensile Strength, date palm seeds, aggregate

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Authors: Yekta Ülgen, Fırat Matur

Abstract:

Cole-Cole parameters of 40 post-menopausal women are compared with their DEXA bone mineral density measurements. Impedance characteristics of four extremities are compared; left and right extremities are statistically same, but lower extremities are statistically different than upper ones due to their different fat content. The correlation of Cole-Cole impedance parameters to bone mineral density (BMD) is observed to be higher for a dominant arm. With the post menopausal population, ANOVA tests of the dominant arm characteristic frequency, as a predictor for DEXA classified osteopenic and osteoporotic population around the lumbar spine, is statistically very significant. When used for total lumbar spine osteoporosis diagnosis, the area under the Receiver Operating Curve of the characteristic frequency is 0.875, suggesting that the Cole-Cole plot characteristic frequency could be a useful diagnostic parameter when integrated into standard screening methods for osteoporosis. Moreover, the characteristic frequency can be directly measured by monitoring frequency driven the angular behavior of the dominant arm without performing any complex calculation.

Keywords: bioimpedance spectroscopy, bone mineral density, osteoporosis, characteristic frequency, receiver operating curve

Procedia PDF Downloads 498

Authors: Agim Kukelii, Gevorg Sargsyan

Abstract:

This study, focusing on the base regional area (county level), estimates the effect of natural disaster damages on aggregate personal income, aggregate wages, wages per worker, aggregate employment, and aggregate income transfer. The study further estimates the dynamics of personal income, employment, and wages under natural disaster shocks. Southeast Texas, located at the center of Golf Coast, is hit by meteorological and hydrological caused natural disasters yearly. On average, there are more than four natural disasters per year that cane an estimated damage average of 2.2% of real personal income. The study uses the panel data method to estimate the average effect of natural disasters on the area’s economy (personal income, wages, employment, and income transfer). It also uses Panel Vector Autoregressive (PVAR) model to study the dynamics of macroeconomic variables under natural disaster shocks. The study finds that the average effect of natural disasters is positive for personal income and income transfer and is negative for wages and employment. The PVAR and the impulse response function estimates reveal that natural disaster shocks cause a decrease in personal income, employment, and wages. However, the economy’s variables bounce back after three years. The novelty of this study rests on several aspects. First, this is the first study to investigate the effects of natural disasters on macroeconomic variables at a regional level. Second, the study uses direct measures of natural disaster damages. Third, the study estimates that the time that the local economy takes to absorb the natural disaster damages shocks is three years. This is a relatively good reaction to the local economy, therefore, adding to the “resiliency” hypothesis. The study has several implications for policymakers, businesses, and households. First, this study serves to increase the awareness of local stakeholders that natural disaster damages do worsen, macroeconomic variables, such as personal income, employment, and wages beyond the immediate damages to residential and commercial properties, physical infrastructure, and discomfort in daily lives. Second, the study estimates that these effects linger on the economy on average for three years, which would require policymakers to factor in the time area need to be on focus.

Keywords: natural disaster damages, macroeconomics effects, PVAR, panel data

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Authors: Nkiru I. Ibeakuzie, Paul D. J. Watson, John F. Pescatore

Abstract:

In roadway construction, the cost of soil-cement stabilization per unit area is significantly influenced by the binder content, hence the need to optimise cement usage. This research work will characterize the influence of soil fractal geometry on properties of cement-stabilized soil, and strive to determine a correlation between mechanical proprieties of cement-stabilized soil and the mass fractal dimension Dₘ indicated by particle size distribution (PSD) of aggregate mixtures. Since strength development in cemented soil relies not only on cement content but also on soil PSD, this study will investigate the possibility of reducing cement content by changing the PSD of soil, without compromising on strength, reduced permeability, and compressibility. A series of soil aggregate mixes will be prepared in the laboratory. The mass fractal dimension Dₘ of each mix will be determined from sieve analysis data prior to stabilization with cement. Stabilized soil samples will be tested for strength, permeability, and compressibility.

Keywords: fractal dimension, particle size distribution, cement stabilization, cement content

Procedia PDF Downloads 184

Authors: José Eugenio Ortiz, Pierre Plaza, Josefa Herrero, Iván Cabria, José Luis Blanco, Javier Gavilanes, José Ignacio Escavy, Ignacio López-Cilla, Virginia Yagüe, César Pérez, Silvia Rodríguez, Jorge Rico, Cecilia Serrano, Jesús Bernat

Abstract:

The development of Artificial Intelligence services in mining processes, specifically in aggregate quarries, is facilitating automation and improving numerous aspects of operations. Ultimately, AI is transforming the mining industry by improving efficiency, safety and sustainability. With the ability to analyze large amounts of data and make autonomous decisions, AI offers great opportunities to optimize mining operations and maximize the economic and social benefits of this vital industry. Within the framework of the European DIGIECOQUARRY project, various services were developed for the identification of material quality, production estimation, detection of anomalies and prediction of consumption and production automatically with good results.

Keywords: aggregates, artificial intelligence, automatization, mining operations

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Authors: Mkhize Xolile, Davies Theopheluis

Abstract:

The purpose of the study was to develop a variety of fruit, vegetable and indigenous wild herb (amaranth) based juices, which can increase mineral consumption (of Ca, Fe, K, Mg, Zn). Ten samples of juice varieties were developed. The concentration range for the standards was between 10 and 150 ppm. Standards and samples were analysed using Perkin Elmer Atomic Absorption Spectrophotometer and the AAnalyst 400 model was used. The indigenous herb based juice was the most nutritious than all the other varieties developed. Mg and Fe could contribute significantly in improving cardio vascular health, bone functionality and immunity of elderly.

Keywords: minerals, elderly, juice, hypertension, intervention

Procedia PDF Downloads 240

Authors: H. Yilmaz Atay

Abstract:

Huntite and hydromagnesite minerals have been used as additive materials to achieve incombustible material due to their inflammability property. Those fire retardants materials can help to extinguish in the early stages of fire. Thus dispersion of the flame can be prevented even if the fire started. Huntite and hydromagnesite minerals are known to impart fire-proofing of the polymer composites. However, the additives used in the applications led to deterioration in the mechanical properties due to the usage of high amount of the powders in the composites. In this study, by enriching huntite and hydromagnesite, it was aimed to use purer minerals to reinforce the polymer composites. Thus, predictably, using purer mineral will lead to use lower amount of mineral powders. By this manner, the minerals free from impurities by various processes were added to the polymer matrix with different loading level and grades. Different types of samples were manufactured, and subsequently characterized by XRD, SEM-EDS, XRF and flame-retardant tests. Tensile strength and elongation at break values were determined according to loading levels and grades. Besides, a comparison on the properties of the polymer composites produced by using of minerals with and without impurities was performed. As a result of the work, it was concluded that it is required to use beneficiated minerals to provide better fire-proofing behaviors in the polymer composites.

Keywords: flame retardant, huntite and hydromagnesite, mechanical property, polymer composites

Procedia PDF Downloads 212