C.Ö. Karacan and G.V.R. Goodman NIOSH, Pittsburgh Research Laboratory

1. Artificial Neural Networks to Determine Ventilation Emissions and Optimum Degasification Strategies for Longwall Mines C.Ö. Karacan and G.V.R. Goodman NIOSH, Pittsburgh Research Laboratory

2. Methane sources are diverse and complex in • longwall environment • Ventilation is the primary means of controlling • methane • Accurate prediction of ventilation emissions is important • Selection of auxiliary methane control system can be a complex task • Most predictive models require expertise and expensive software packages Introduction

3. To Bleeder Fan Tailgate Entries Headgate Entries Stopping Pillar Maximum allowable limit of methane is 1% at the face Schematics of Face Ventilation Path

4. Auxiliary Methane Control Measures

5. To develop an artificial neural network (ANN) based • prediction and decision tool for: • Predicting methane emissions from US longwall mines based on various parameters that may have impact on emissions • Helping the mines in their decisions of degasification type choice (N, G, HG, VHG) as a function of various important parameters that affect the selection criteria Objective

6. 63 longwall mines from 10 states were analyzed • between 1985-2005 for emissions and degasification • system used: • Operational parameters • Geological parameters • Geographical parameters • Productivity related parameters Methodology Niosh Research (IC 9067) Longwall census EPA reports

7. Longwall Mines: 1985-2005 63 mines from 10 states

8. Variables for ventilation emissions and degasification choice • Formed in 538 data rows as 18 columns Methodology

9. Schematics of a Longwall Mine

10. Identifying PC’s reduces the dimensionality and selecting • appropriate model inputs while retaining much of the variance • In each PC, variables and their weights are reported • Used to determine the relative importance of each variable • on emissions and degasification system used at the mines • In this work, 80% of the total variance was selected to remain • in the data • PCA revealed that 80% variance is retained in 5 PC’s. Methodology: Principle Component Analysis

11. MINING VARIABLES • Panel dimensions • Coal production • Cut • Conveyor speeds • METHANE CONTENT • Overburden • Total gas • Rank Pinnacle West Elk Input row • LOCATION • Basin • State North River

12. An ANN simulates the cognitive behavior of brain. • Weighted sum of the incoming signals are used with an • activation function. • A two-layer MLP-ANN type network was designed. • Input variables from PCA were used interchangeably. • Network parameters then changed. ANN MODELING

13. Activation: Hyperbolic Tangent Hyperbolic Tangent Hyperbolic Tangent Momentum: 0.6 0.6 0.6 Iteration: 1500 Final Model: MLP-ANNVentilation Emission Model • 9 parameter model with (Total Gas, Panel Width, Conveyor, • Stage L, Seam H, Cut H, Coal Prod, Entries, State) was • determined as best combination. Ventilation Emission 56 N 38 N 9 inputs

14. Test Result: MLP-ANNVentilation Emission Model R = 0.96 R2 = 0.92

15. Comparison of EmissionANN Model with Statistical Models Linear = a0+a1v1+ a2v2+….+a9v9 ANN R2= 0.54 N-Linear = a0+a1v1+a2v2+…. +a9v9+b1v12+b2v22…. ANN R2= 0.61 ANN R2= 0.92

16. None G 48 N 28 N 9 inputs HG VHG Activation: Hyperbolic Tangent Hyperbolic Tangent Soft Max. Axon Momentum: 0.7 0.7 0.7 Iteration: 1500 Final model for Degasification System Selection (Classification) Model • State, Seam H, Cut H, Entries, Panel W, Coal P, Total G, • OB, Emission)

17. Test Result: MLP-ANNDegasification System Selection Model • Result of test on 81 random, unseen data

18. Input row Result Execution window Modeling – ANN Modeling Modeling of ventilation emissions from U.S. LW mines and determination of degasification system (G, HG, VHG, N) • DLL’s were generated for MS-Access to distribute and use the models in any computer without the need for ANN model builder.

19. Conclusions • The results of PCA and ANN model search process showed • that ventilation emissions and degasification system • selection could be made by a number of variables • Based on PCA, methane content and mining parameters • are most influential on ventilation emissions • ANN model of ventilation emissions is more accurate than • statistical models, and may be one of the most practical and • accurate models to predict ventilation emissions in US • longwall mines

20. Conclusions • Results showed that the degasification systems commonly • used in US longwall mines can be determined effectively • using a classification network. • The approach and the results suggest that by incorporating • critical stratigraphic features, rather than geographical • information, the models may be applicable to other locations • with different geological layers.