scholarly journals New Approach Study on Dry Coal Cleaning System with Two-Stage Corona Electrostatic Processes for High-Sulfur Low-Grade Fine Coals

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1915
Author(s):  
Chengyuan Liu ◽  
Qingyue Wang
Keyword(s):  
Sulfur Content ◽  
Coal Ash ◽  
Ash Content ◽  
Inorganic Materials ◽  
Low Grade ◽  
Electrostatic Separation ◽  
Clean Coal ◽  
Two Stage ◽  
Second Stage ◽  
Coal Cleaning

Corona electrostatic separation can remove inorganic materials from coal, reduce coal ash content and sulfur content and improve coal quality, reduce air pollution caused by smoke dust, SOX, and COX. The performance of corona electrostatic separation technology in cleaning a middle ash medium-ash, high-sulfur coal was experimentally investigated. The electrode voltage, drum rotational speed, and feeding speed were tested, whereas other parameters were maintained constant during the experiment. The results indicate that the performance of this technology in cleaning medium-ash, high-sulfur coal can be improved by optimizing the process parameters. The results demonstrate that corona electrostatic separation is effective for the beneficiation of this grade coal. In addition, the efficiency of coal cleaning is significantly improved by adding the second stage beneficiation to clean the middlings out from the first stage beneficiation. In this study, the first stage of beneficiation recovered 38.00% (by weight) of clean coal (ash content below 20%), and the second stage recovered 48.58% (by weight) of clean coal, improving the overall separation efficiency from 0.69 to 1.74. Furthermore, the sulfur content was reduced from 4.71% (raw coal) to 3.53% (clean coal). Our result show that corona electrostatic separation can effectively reject inorganic sulfur from raw coal, and the two-stage separate is also very helpful for coal purification.

scholarly journals Studies on washability characteristics of low-volatile Indian coking coal by differential crushing

2020 ◽  
Author(s):  
Sugali Sekhar Chandra
Keyword(s):  
Specific Gravity ◽  
Coal Ash ◽  
Ash Content ◽  
Similar Degree ◽  
Clean Coal ◽  
Coking Coal ◽  
Relative Ease ◽  
Indian Origin ◽  
Two Parameters ◽  
Material Index

Abstract The effect of differential crushing on liberation characteristics has been studied for a low volatile coking coal of Indian origin through washability studies. Two parameters, namely “Index of Washability” (IW) and “Near Gravity Material Index” (NGMI), are used to describe the ease of washability. The ROM Coal is crushed to four different top sizes namely, 75 mm, 25 mm, 13 mm and 6 mm.On the basis of calculated IW it is observed that relative ease of washabiltiy increases with decrease in top size. From calculated IW values it may be said that this coal can be economically beneficiated using gravity process after crushing to -6 mm size. From the calculated NGMI values, the critical specific gravities have been estimated and the values for crushing to -75 mm,-25 mm,-13 mm and − 6 mm are 1.65, 1.68, 1.53 and 1.58 respectively. These critical specific gravity values suggest the separation at this specific gravity range is most difficult task using gravity methods. From NGMI analysis, it may be said that the NGMI values for coals crushed to -25 mm & -6 mm are identical (≈ 0.18) at 17% clean coal ash content. This suggests that with similar degree of difficulty, clean coal of 17% ash can be produced from these two different crushing sizes. In order to increase the yield for the clean coal of 17% ash, the decision on blending these two size coals may need to be taken.

On-line prediction of clean coal ash content based on image analysis

Measurement ◽  
2020 ◽  
pp. 108663
Author(s):  
Zhaoyu Qiu ◽  
Dongyang Dou ◽  
Deyang Zhou ◽  
Jianguo Yang
Keyword(s):  
Image Analysis ◽  
Coal Ash ◽  
Ash Content ◽  
Clean Coal ◽  
On Line

Study on Coal Separability for Wangcun Inclined Shaft in Chenghe

2013 ◽  
Vol 734-737 ◽  
pp. 1082-1085
Author(s):  
Xi Yuan Yang ◽  
Mei Li Du ◽  
Jin Ren Zhang ◽  
Jian Li Yang
Keyword(s):  
Particle Size ◽  
Coal Ash ◽  
Quality Characteristics ◽  
Ash Content ◽  
Test Results ◽  
Coal Seams ◽  
Clean Coal ◽  
Coal Quality ◽  
Heating Value ◽  
The Given

On the basis of the coal quality characteristics, the screening and drifting tests have been carried out to investigate the separability of the coals in the inclined shaft coal samples of Chenghe Wangcun coal mine. The results showed that the mine raw coal seams are medium ash, high sulfur and medium volatile coals. The screening test results showed that the water content and sulfur content of coals samples in each grade after sieving essentially remained the row coal characteristic. Heating value increased with the decrease of particle size, but ash content decreased with the particle size. The float-and-sink tests showed that when the given clean coal ash content was 10%13% and 15% respectively, the coal samples were in the extremely difficult degree, medium separability degree and medium separability degree respectively.

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

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