Establishment of grey-neural network forecasting model of coal and gas outburst

Yang, Shengqiang; Sun, Yan; Zu-yun, Chen; Yu, Bo; Quan, Xu

doi:10.1016/j.proeps.2009.09.025

Cited by 11 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang and Lowndes (2010) proposed a model coupling artificial neural networks and fault trees. Yang et al (2009) presented a grey neural network-based forecast model. Fu et al (2012) developed an intelligent computing platform for the prediction of outburst risks.…”

Section: Introductionmentioning

confidence: 99%

A rapid prediction technology for the outburst risk of coal uncovered in crosscuts and shafts and its application in the Panyi Coal Mine in Huainan, China

Chen

Tang

et al. 2018

Mining Technology: Transactions of the Institutions of Mining a

View full text Add to dashboard Cite

In this study, we propose a set of rapid prediction technologies for the outburst risk of coal uncovering in crosscuts and shafts (CUCS). These technologies are needed because of the ideal model to uncover coal in crosscuts and the law that coal seam outburst intensity increases as the initial expansion energy of released gas (IEERG) increases. Our technologies include rapid coal seam gas pressure determination under complex geological conditions, complete coal-core coring and the IEERG measurement. We successfully applied these technologies in the Panyi Coal Mine in Huainan. The field application results demonstrated that these technologies could accurately and rapidly measure coal seam gas pressure, greatly minimize human damages to coal samples in the coal coring process, and accurately predict outburst risks in CUCS.

show abstract

Section: Introductionmentioning

confidence: 99%

A rapid prediction technology for the outburst risk of coal uncovered in crosscuts and shafts and its application in the Panyi Coal Mine in Huainan, China

Chen

Tang

et al. 2018

Mining Technology: Transactions of the Institutions of Mining a

View full text Add to dashboard Cite

show abstract

“…Therefore there is a need for new class of anticancer agents in treating the cancer. Among the other heterocyclic pharmacophores, imidazoles,pyridines and benzimidazoles are the most promising heterocyclic moieties, which have active templates in treating various diseases including various cancers [3][4][5][6][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Novel 2-Butyl-1H-Benzo [4, 5] Imidazo [1, 2-A] Imidazo [4, 5-E] Pyridine-5-Carbonitrile Derivatives and Evaluation of Their Anticancer Activity

Rao¹,

Rayam²,

Shankaraiah³

et al. 2016

Orient. J. Chem

View full text Add to dashboard Cite

A series of new 2-butyl-1H-benzo [4,5]imidazo [1,2-a]imidazo [4,5-e]pyridine-5-carbonitrile derivatives were synthesized by the self cyclisation of (E)-2-(1H-benzo[d]imidazol-2-yl)-3-(2-butyl-4-chloro-1H-imidazol-5-yl)acrylonitrile in the presence of piperidine as catalyst, which in turn were prepared by the condensation of substituted N-alkyl 2-butyl-4-chloro-1H-imidazole-5-carbaldehydes with 2-cyanomethylbenzimidazole in the presence of catalytic amount of L-proline in ethanol or by using piperidine as a base. Newly synthesized compounds which incorporate a variety of N-substituent moieties were characterized by spectral data and screened for anticancer activity against MCF-7 breast cancer cell line. The results showed that compounds 3b, 3a and 4b possess significant anti proliferative activity with IC 50 values 26.59 and 27.98, 36.95 µM respectively.

show abstract

“…Fu Xuehai et al studied predicting technologies and methods of coal and gas outburst in the geological exploration period [5]. Yang Shengqiang et al founded grey correlation to analyze artificial neural network of coal and gas outburst prediction model through analyzing influencing factors of coal and gas [6]. Peng Shoujian et al built matter-element model of coal and gas outburst to predict hazards [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Gas Outburst Prediction in Coal Mining in China Based on a New Kind of SVM Algorithm

Zhang

Hong

2013

AMR

View full text Add to dashboard Cite

The research on predicting gas outburst hazard on the basis of the unbalanced problem of gas monitoring samples under usual circumstances is given in this paper. Combined with outburst-preventing monitoring parameters, a new kind of method of predicting gas outburst hazard based on v-SVM algorithm through analyzing features of real-time gas monitoring data and extracting parameters of gas concentration real-time variation trend, parameters of gas variation rates and feature parameters of gas emission is put forward in this paper. And the application shows that this algorithm for the prediction of actual gas emission in coal mines is effective.

show abstract

Establishment of grey-neural network forecasting model of coal and gas outburst

Cited by 11 publications

References 1 publication

A rapid prediction technology for the outburst risk of coal uncovered in crosscuts and shafts and its application in the Panyi Coal Mine in Huainan, China

A rapid prediction technology for the outburst risk of coal uncovered in crosscuts and shafts and its application in the Panyi Coal Mine in Huainan, China

Synthesis of Novel 2-Butyl-1H-Benzo [4, 5] Imidazo [1, 2-A] Imidazo [4, 5-E] Pyridine-5-Carbonitrile Derivatives and Evaluation of Their Anticancer Activity

Research on Gas Outburst Prediction in Coal Mining in China Based on a New Kind of SVM Algorithm

Contact Info

Product

Resources

About