Study on Dynamic Prediction Model of Gas Emission in Tunneling Working Face

2012 ◽

Vol 253-255 ◽

pp. 879-883

Author(s):

Xiang Lan Liu ◽

Xu Sheng Zhao ◽

Gui Gang Dong

Keyword(s):

Prediction Method ◽

Calculation Model ◽

Gas Content ◽

Gas Emission ◽

Dynamic Prediction ◽

Emission Standard ◽

Working Face ◽

The Difference ◽

Emission Index ◽

Emission Dynamic

Gas emission dynamic prediction is a non-contact and continuous outburst prediction method. It is based on the difference of gas emission characteristics whether the working face exists dangerous factors, selecting appropriate indicators to predict outburst danger. The indicators that reflect abnormal gas emission is divided into four types: gas content, desorption characteristics, gas emission fluctuation and gas emission trend change. This paper gives the calculation model of A index that reflect desorption gas content, B index that reflect coal structure and desorption gas characteristics, S index of gas emission standard deviation, Kv index of gas emission variation coefficient. The validity of the real-time prediction using gas emission index for the working face outburst danger is verified by typical cases.

Download Full-text

Research on dynamic prediction model of surface subsidence in mining areas with thick unconsolidated layers

Energy Exploration & Exploitation ◽

10.1177/0144598720981645 ◽

2021 ◽

pp. 014459872098164

Author(s):

Shenshen Chi ◽

Lei Wang ◽

Xuexiang Yu ◽

Weicai Lv ◽

Xinjian Fang

Keyword(s):

Prediction Model ◽

Mining Area ◽

Influence Coefficient ◽

Exponential Form ◽

Dynamic Prediction ◽

Mining Areas ◽

Subsidence Basin ◽

Subsidence Prediction ◽

Working Face ◽

Surface Dynamic

In order to improve the accuracy of the surface dynamic prediction model in mining areas with thick unconsolidated layers and improve Knothe time function, the influence coefficient was firstly changed into the coefficient in exponential form, and the influence coefficient of unconsolidated layer was added. Then, a subsidence basin prediction model for mining under thick unconsolidated layers was established. Next, the model was combined with the improved Knothe function, thus constructing a new mining subsidence prediction model. The new subsidence prediction model was applied in 1414 (1) working face in Huainan mining area. The results showed that the integrated model could better reflect the subsidence process, and the prediction values and the measured values agreed well.

Download Full-text

A Continuous Dynamic Prediction Model of Gas Pressure Based on Gas Emission at Excavation Face and its Engineering Application

10.5194/nhess-2015-322 ◽

2016 ◽

Author(s):

Chen Liang ◽

Wang Enyuan

Keyword(s):

Prediction Model ◽

Prevention And Control ◽

Engineering Application ◽

Dynamic Effect ◽

Gas Pressure ◽

Coal And Gas Outburst ◽

Gas Emission ◽

Dynamic Prediction ◽

Gas Outburst ◽

And Control

Abstract. Gas pressure is one of the necessary conditions for the occurrence of coal and gas outburst. Realization of continuous and dynamic gas pressure forecasting is of significance for prevention and control of coal and gas outburst. In this work, we established a gas pressure prediction model based on the source of gas emission with considering fluid-solid coupling process. The verified results showed that the predicted gas pressure was roughly consistent with the actual situation, indicating that the prediction model is correct. And it could meet the need of engineering projects. Coal and gas outburst dynamic phenomenon is successfully predicted in engineering application with the model. Overall, prediction coal and gas outburst with the gas pressure model can achieve the continuous and dynamic effect. It can overcome both the static and sampling shortcomings of traditional methods, and solve the difficulty of coal and gas outburst prediction at the excavation face. With its broad applicability and potential prospect, we believe the model is of great importance for improving prevention and control of gas disasters.

Download Full-text

Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

Processes ◽

10.3390/pr9071147 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1147

Author(s):

Jun Zeng ◽

Qinsheng Li

Keyword(s):

Prediction Model ◽

Coal Mine ◽

Prediction Error ◽

Prediction Accuracy ◽

Coal Mines ◽

Grey Prediction ◽

Gas Emission ◽

Grey Prediction Model ◽

Gray Prediction ◽

Working Face

In order to achieve the accuracy of gas emission prediction for different workplaces in coal mines, three coal mining workings and four intake and return air roadway of working face in Nantun coal mine were selected for the study. A prediction model of gas emission volume based on the grey prediction model GM (1,1) was established. By comparing the predicted and actual values of gas emission rate at different working face locations, the prediction error of the gray prediction model was calculated, and the applicability and accuracy of the gray prediction method in the prediction of gas gushing out from working faces in coal mines were determined. The results show that the maximum error between the predicted and actual measured values of the gray model is 2.41%, and the minimum value is only 0.07%. There is no significant prediction error over a larger time scale; the overall prediction accuracy is high. It achieves the purpose of accurately predicting the amount of gas gushing from the working face within a short period of time. Consequently, the grey prediction model is of great significance in ensuring the safety production of coal mine working face and promote the safety management of coal mine.

Download Full-text

Dynamic prediction for clinically relevant pancreatic fistula: a novel prediction model for laparoscopic pancreaticoduodenectomy

BMC Surgery ◽

10.1186/s12893-020-00968-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Runwen Liu ◽

Yunqiang Cai ◽

He Cai ◽

Yajia Lan ◽

Lingwei Meng ◽

...

Keyword(s):

Prediction Model ◽

Pancreatic Fistula ◽

Postoperative Pancreatic Fistula ◽

Laparoscopic Pancreaticoduodenectomy ◽

C Reactive Protein ◽

Dynamic Prediction ◽

Albumin Ratio ◽

Drainage Fluid ◽

Reactive Protein ◽

Baseline Factors

Abstract Background With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio. Methods We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point. Results The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively. Conclusions The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

Download Full-text