scholarly journals Classification of Ball Mill Acoustic for Predictive Grinding using PCA on Time and Frequency Domain Data

2020 ◽  
Vol 9 (3) ◽  
pp. 1934-1941
Keyword(s):  
Size Distribution ◽  
Ball Mill ◽  
Clustering Algorithm ◽  
Raw Materials ◽  
Mining Industry ◽  
Gaussian Mixture ◽  
Genetic Operators ◽  
Audio Signals ◽  
Fcm Clustering ◽  
In Transit

The process of comminution is nondeterministic in nature, so deriving out a designated size range on crushing by fixing the parameters of the mill is not possible in mining industry. Loss of materials in huge amount is an obvious phenomenon due to under sizing of materials in transit. The aim of the paper is to predict the state of grinding and the particle size distribution (psd) during any desired stage of crushing in the ball mill. The acoustic sensors have been used to capture audio signals at different running conditions of the ball mill and analyzed to develop the prediction model. In the proposed work first Genetic Algorithm (GA) based predictive procedure is applied on the fragmented signal to extract the parameters of genetic operators and store them in a table. We also apply Gaussian Mixture Model (GMM) to obtain the psd of each fragment and Fuzzy C-means (FCM) clustering algorithm is employed to classify the distributed signal. The psd of each fragment has been stored in another table. The experiment is conducted for different raw materials with different size distribution. At run time the material grinding procedure is operated and stopped automatically based on the trained controlled parameters corresponding to the desired stage of grinding. The psd of experimental output is verified with the desired psd obtained during training and stored in the table. The proposed method exhibits significant improvement in prediction performance and outcomes are verified with the experimental results.

Combustion Synthesis of β-SiAlON Using 3D Ball Milling

2017 ◽  
Vol 898 ◽  
pp. 1717-1723 ◽  
Author(s):  
Xue Mei Yi ◽  
Shota Suzuki ◽  
Xiong Zhang Liu ◽  
Ran Guo ◽  
Tomohiro Akiyama
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Combustion Synthesis ◽  
Ball Mill ◽  
Raw Materials ◽  
Raw Material ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Material Particle

Combustion synthesis (CS) of β-SiAlON was conducted using a 3D ball mill, with a focus on the effect of the 2D/3D ball mill premixing conditions on the CS raw material particle size as well as on the yield and grain shape of the final products. The results showed that the particle size distribution of the raw materials was significantly affected by the premixing conditions. Various particle sizes and particle size distributions could easily be obtained by using a 3D mill instead of a 2D mill due to the complex biaxial rotation movement of 3D milling. The particle size was more sensitive to the rotation ratio (vertical spin/horizontal spin, Vv/Vh) than the rotation rate when using 3D milling. Finally, β-SiAlON with less than 5 mass% unreacted Si was obtained using premix milling conditions of 135×200 [vertical spin (rpm) × horizontal spin (rpm)]. The grain shapes of the final products were clearly influenced by the particle size distribution of the raw mixtures.

Crystallization of barium hexaferrite

1992 ◽  
Vol 50 (1) ◽  
pp. 362-363
Author(s):  
Chung-kook Lee ◽  
Yolande Berta ◽  
Robert F. Speyer
Keyword(s):  
Size Distribution ◽  
Raw Materials ◽  
Barium Hexaferrite ◽  
Recording Media ◽  
Magnetic Recording Media ◽  
Promising Candidate ◽  
Crystallization Method ◽  
Temperature Heat ◽  
High Density Magnetic Recording ◽  
Temperature Heat Treatment

Barium hexaferrite (BaFe12O19) is a promising candidate for high density magnetic recording media due to its superior magnetic properties. For particulate recording media, nano-sized single crystalline powders with a narrow size distribution are a primary application requirement. The glass-crystallization method is preferred because of the controllability of crystallization kinetics, hence, particle size and size distribution. A disadvantage of this method is the need to melt raw materials at high temperatures with non-reactive crucibles, e.g. platinum. However, in this work, we have shown that crystal growth of barium hexaferrite occurred during low temperature heat treatment of raw batches.

scholarly journals A New Face of Mining Engineer—International Curricula to Sustainable Development and Green Deal (A Case Study of the Wrocław University of Science and Technology)

2021 ◽  
Vol 13 (3) ◽  
pp. 1393
Author(s):  
Karolina Adach-Pawelus ◽  
Anna Gogolewska ◽  
Justyna Górniak-Zimroz ◽  
Barbara Kiełczawa ◽  
Joanna Krupa-Kurzynowska ◽  
...  
Keyword(s):  
Higher Education ◽  
Sustainable Development ◽  
Raw Materials ◽  
Research Question ◽  
Science And Technology ◽  
Mining Industry ◽  
Mining Engineer ◽  
European Institute ◽  
Innovation And Technology ◽  
Development Goals

The mining industry in the world has undergone a major metamorphosis in recent years. These changes have forced higher education to modify the curricula in a thorough way to meet the mining entrepreneurs’ needs. The paper’s scope is to answer the research question—how to attract students and implement Sustainable Development Goals (SDGs) in higher education in mining engineering? Based on the case of international cooperation carried out at the Faculty of Geoengineering, Mining and Geology of the Wrocław University of Science and Technology (WUST) within the framework of educational projects co-financed by European Institute of Innovation and Technology (EIT) and EIT Knowledge and Innovation Communities Raw Materials (EIT RM), the authors prove that the idea of sustainable development can be introduced into the system of teaching mining specialists at every level of their higher education (engineering and master’s studies), through developing their new competencies, introducing new subjects taking into account innovative solutions and technologies, or placing great emphasis on environmental and social aspects. Examples of new curricula show a good way to change into the new face of a mining engineer.

scholarly journals Phase evolution, characterisation, and performance of cement prepared in an oxy-fuel atmosphere

2016 ◽  
Vol 192 ◽  
pp. 113-124 ◽  
Author(s):  
Liya Zheng ◽  
Thomas P. Hills ◽  
Paul Fennell
Keyword(s):  
Particle Size ◽  
Compressive Strength ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Carbon Capture ◽  
Raw Materials ◽  
Carbon Capture And Storage ◽  
Phase Evolution ◽  
Chemical Compositions ◽  
Phase And Elemental Compositions

Cement manufacture is one of the major contributors (7–10%) to global anthropogenic CO2 emissions. Carbon capture and storage (CCS) has been identified as a vital technology for decarbonising the sector. Oxy-fuel combustion, involving burning fuel in a mixture of recycled CO2 and pure O2 instead of air, makes CO2 capture much easier. Since it combines a theoretically lower energy penalty with an increase in production, it is attractive as a CCS technology in cement plants. However, it is necessary to demonstrate that changes in the clinkering atmosphere do not reduce the quality of the clinker produced. Clinkers were successfully produced in an oxy-fuel atmosphere using only pure oxides as raw materials as well as a mixture of oxides and clay. Then, CEM I cements were prepared by the addition of 5 wt% gypsum to the clinkers. Quantitative XRD and XRF were used to obtain the phase and elemental compositions of the clinkers. The particle size distribution and compressive strength of the cements at 3, 7, 14, and 28 days' ages were tested, and the effect of the particle size distribution on the compressive strength was investigated. Additionally, the compressive strength of the cements produced in oxy-fuel atmospheres was compared with those of the cement produced in air and commercially available CEMEX CEM I. The results show that good-quality cement can be successfully produced in an oxy-fuel atmosphere and it has similar phase and chemical compositions to CEM I. Additionally, it has a comparable compressive strength to the cement produced in air and to commercially available CEMEX CEM I.

Bentonite granulates as a seals for mobile flood barriers

2021 ◽  
Vol 11 (4) ◽  
pp. 16-23
Author(s):  
Piotr Wrona ◽  
Wojciech Panna ◽  
Stanisław Lipiński ◽  
Maciej Woźniak
Keyword(s):  
Comparative Analysis ◽  
Size Distribution ◽  
Raw Materials ◽  
Test Stand ◽  
Three Samples

Bentonites and other smectite raw materials are widely used in many industries. The authors of the study analyzed the suitability of swelling granulates for their use as a seals in mobile flood barriers. For this purpose, a comparative analysis of the swelling and granulation parameters of three samples available on the market was performed. This results was compared with a macroscopic swelling test, which was realized on the specially prepared test stand. The carried out research shows that not only the content of the swelling minerals – mainly smectite – affect on the sealing of the system, but also they are determine by granules size distribution and the type of smectite.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

A Human-Centered Approach to the Redesign of a Bitumen Trailer

2020 ◽  
Vol 29 (1) ◽  
pp. 4-13
Author(s):  
Sara Pazell ◽  
Robin Burgess-Limerick
Keyword(s):  
Travel Time ◽  
Design Process ◽  
Work Performance ◽  
Mining Industry ◽  
Design Philosophy ◽  
Electric Heat ◽  
In Transit ◽  
Reduced Risk

An electric heat-in-transit tanker (bitumen trailer) revolutionized the operation and design of bituminous tankers. It was developed using human-centered approaches and design philosophy, concepts, methods, and tools previously used in the mining industry. Task-based analysis was useful to contextualize opportunities and hazards. The new tanker improved efficiency in transit, reduced risk for exposure to hot bituminous product, improved access, reduced on-road travel time and risk for fatigue, optimized work performance, and challenged regulators to redefine safe transit requirements. The design process was opportunity based and highlighted the need to shift philosophy to consider performance-based needs of operators, not just equipment.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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