Prediction of the Slagging Characteristics of Coal Ash Based on Vague Sets

2013 ◽  
Vol 668 ◽  
pp. 954-958
Author(s):  
Xiao Qiang Wen ◽  
Ling Fang Sun
Keyword(s):  
Pattern Recognition ◽  
Prediction Model ◽  
Coal Ash ◽  
Distance Measures ◽  
Recognition Model ◽  
Vague Sets ◽  
Coal Ashes

The distance measures of similarity between vague sets (values) have been developed to solve the problem of slagging characteristic of coal ash, and a slag-prediction model of coal ash based on vague sets was proposed. In the model, four single indices and their bounds are involved. The indices are ST, SiO2/Al2O3, B/A and G. At the same time, 25 coal ashes were selected as samples. the model was employed to predict the slagging characteristic of the 10 coal ashes. Through predicting, determining and comparison, it proved that the accuracy of the pattern recognition model was 90 percent and met the engineering requirements. So the model built was reasonable and feasible.

Prediction of the Slagging Characteristics of Coal Ash Based on Symmetric Fuzzy Cross Entropy and Vague Sets

2013 ◽  
Vol 302 ◽  
pp. 617-621
Author(s):  
Xiao Qiang Wen
Keyword(s):  
Pattern Recognition ◽  
Prediction Model ◽  
Coal Ash ◽  
Softening Temperature ◽  
Cross Entropy ◽  
Accuracy Rate ◽  
Recognition Model ◽  
Vague Sets ◽  
Acid Ratio ◽  
Sets Theory

A prediction model was built to predict the slagging characteristics of coal ash based on the theory of symmetric fuzzy cross entropy and vague sets. Softening temperature, SiO2-Al2O3 ratio, Alkali-acid ratio, silicon percentage of value were selected as input vectors. Firstly, the vague values of the selected samples were calculated based on vague sets theory, and then the similarity degree was evaluated based on symmetric fuzzy cross entropy. The prediction results prove that the predicting accuracy rate of the new pattern recognition model is 90%. So, the model built here is reasonable and feasible and meets the requirement of engineering.

Width-based distance measures on interval-valued intuitionistic fuzzy sets

2021 ◽  
pp. 1-13
Author(s):  
Xi Li ◽  
Chunfeng Suo ◽  
Yongming Li
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Intuitionistic Fuzzy ◽  
Interval Valued ◽  
Dissimilarity Function ◽  
Better Than

An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures.

The fusion mechanism of complex minerals mixture and prediction model for flow temperature of coal ash for gasification

Fuel ◽  
2021 ◽  
Vol 305 ◽  
pp. 121448
Author(s):  
Wenju Shi ◽  
Marcel Laabs ◽  
Markus Reinmöller ◽  
Lingxue Kong ◽  
Stanislav V. Vassilev ◽  
...  
Keyword(s):  
Prediction Model ◽  
Coal Ash ◽  
Flow Temperature

scholarly journals A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

2017 ◽  
Vol 14 (3) ◽  
pp. 455-489
Author(s):  
Inyeneobong Ekoi Edem ◽  
Sunday Ayoola Oke ◽  
Kazeem Adekunle Adebiyi
Keyword(s):  
Pattern Recognition ◽  
Industrial Accident ◽  
Recognition Model

Studies on the Leaching and Weathering Processes of Coal Ashes

1983 ◽  
Vol 15 (11) ◽  
pp. 163-191 ◽  
Author(s):  
Hay Leim ◽  
Magnusöm Sandstr ◽  
Tom Wallin ◽  
Anders Carne ◽  
Ulla Rydevik ◽  
...  
Keyword(s):  
Power Plants ◽  
Coal Ash ◽  
Mass Action ◽  
Equilibrium Analysis ◽  
Metal Leaching ◽  
Magnetic Fraction ◽  
Fly Ashes ◽  
X Ray ◽  
Weathering Processes ◽  
Coal Ashes

Within the Swedish project Coal-Health-Environment (Kol-Hälsa-Mi1jö) leaching studies were made on several types of coal ashes, fly ashes, bottom ashes and scrubber sludges. Studies were made on the effects of pH, pCl, pCO3. pEDTA and pSO4, in the leaching solution on the leaching of metals from coal ashes. As a model for the leaching system, we studied the leaching of Cr, Co, Ni, tu, Zn, Mo, Se, As, Cd and Pb. The leaching of the metals was found to increase with pH for pH less than 2 and pH greater than 9, and to decrease for 2 > pH > 6. The metal leaching behaviour may in part be explained by the mass-action law. We have also made long-term leaching tests of several coal ashes from Danish and Finnish Power Plants using leaching water of different pH. The dominating crystalline phases in the different coal ashes have been identified by X-ray diffraction technique. The major species in the different coal ashes were found to be mullite (3A12.2SiO2), α-quartz (Sio2) and the iron oxides magnetite and hematite. Leaching and X-ray studies were also made on the magnetic fraction of the fly ashes. Equilibrium analysis were made on the system Me-OH-Cl-CO32− - SO42−, where Me = Cr, Co, Ni, Cu, Zn, Mo, Se, As, Cd and Pb, partly using the computer program HALTAFALL. A model for the metal leaching from a coal ash deposit will be discussed.

Information Entropy Based Fuzzy Pattern Recognition Model for Identification of Vulnerable Groups in Water Resource Conflicts

2014 ◽  
Vol 610 ◽  
pp. 316-319
Author(s):  
Li Feng Lv ◽  
Yan Ping Chen
Keyword(s):  
Pattern Recognition ◽  
Water Resource ◽  
Information Entropy ◽  
Water Rights ◽  
Liaoning Province ◽  
Vulnerable Groups ◽  
Recognition Model ◽  
Resource Conflicts ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern

Identification of vulnerable groups in water resource conflicts is to improve the identification of vulnerable groups in the allocation of water rights and water markets water rights system. There are two difficulties: one is how to determine the weight of evaluation indexes; another is how to effectively deal with the subjectivity of the evaluation process and the low resolution. Therefore, this paper proposes “Information Entropy Based Fuzzy Pattern Recognition Model for Identification of Vulnerable Groups in Water Resource Conflicts (EFPQ-VRWC)” according to the fuzzy pattern recognition based on the combination of the maximum entropy principle and genetic algorithms. And identifying vulnerable groups of Daling River Basin in Liaoning Province, it illustrates the method of application value. And evaluation results have continuity, comparability and versatility so that can accurately reflect the level of vulnerable groups in water resource conflicts.

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