Development of Multiple Regression and Neural Network Models for Assessment of Blasting Dust at a Large Surface Coal Mine

Roy, Sangita; Adhikari, G. R.; Renaldy, T. Amrith; Jha, Anand K.

doi:10.3923/jest.2011.284.301

Cited by 33 publications

(10 citation statements)

References 9 publications

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“…As samplers were static during blasting, this approach required detailed site-blasting plans and favorable weather conditions to determine their interconnectivity. Under this configuration, neural network models performed better than multiple regression models in predicting outcomes [ 9 ].…”

Section: Blast-associated Air Samplingmentioning

confidence: 99%

Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites

Alvarado

Gonzalez

Fletcher

et al. 2015

Sensors

127

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Blasting is an integral part of large-scale open cut mining that often occurs in close proximity to population centers and often results in the emission of particulate material and gases potentially hazardous to health. Current air quality monitoring methods rely on limited numbers of fixed sampling locations to validate a complex fluid environment and collect sufficient data to confirm model effectiveness. This paper describes the development of a methodology to address the need of a more precise approach that is capable of characterizing blasting plumes in near-real time. The integration of the system required the modification and integration of an opto-electrical dust sensor, SHARP GP2Y10, into a small fixed-wing and multi-rotor copter, resulting in the collection of data streamed during flight. The paper also describes the calibration of the optical sensor with an industry grade dust-monitoring device, Dusttrak 8520, demonstrating a high correlation between them, with correlation coefficients (R2) greater than 0.9. The laboratory and field tests demonstrate the feasibility of coupling the sensor with the UAVs. However, further work must be done in the areas of sensor selection and calibration as well as flight planning.

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Section: Blast-associated Air Samplingmentioning

confidence: 99%

Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites

Alvarado

Gonzalez

Fletcher

et al. 2015

Sensors

127

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show abstract

“…Considering the adequacy of the multiple regression models one can estimate the performance of surface miners in different geotechnical conditions. b) ANOVA analysis: For a regression model to be useful as a predictor, observed F ratio must be greater than critical value of F as reported by Roy et al [13]. The statistical critical value for F(8,22) is 3.45 at 1% significance level.…”

Section: Validation Of Multiple Regression Modelsmentioning

confidence: 97%

“…a) Index of determination: Different researchers [11][12][13] used correlation coefficient (R) between measured and predicted values for the evaluation of model performance. Figs.…”

Section: Validation Of Multiple Regression Modelsmentioning

confidence: 99%

A new rock cuttability index for predicting key performance indicators of surface miners

Prakash

Murthy

Singh

2015

International Journal of Rock Mechanics and Mining Sciences

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“…The previous studies have predicted the vibration strength at different time delays and deduced the reasonable inter-hole time delay, using neural network method [1][2][3][4], numerical simulation [5][6][7] and wave superposition method [8][9][10]. Based on the single-hole blasting waveform measured in an actual subway tunnel, this paper constructs the superposed stress wave at different time delays by the linear superposition method, aiming to shed new light on actual tunnel blasting.…”

Section: Introductionmentioning

confidence: 99%

Vibration Control of Stress Waveform Superposition in Millisecond Blasting

Li¹,

Xue²,

Zhang³

2017

MMC_B

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Based on the measured signals of single-hole blasting, this paper constructs the superposed stress waveform of blasting at different time delays by the linear superposition method, and studies the distribution of vibration velocity and energy. The results show that the maximum superposed vibration velocity was apparently reduced when the time delay lasted no longer than half the cycle of a waveform. Owing to the complexity of waveforms, a half-cycle superposed velocity may not be the minimum velocity of the synthetic wave. When T/2

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Development of Multiple Regression and Neural Network Models for Assessment of Blasting Dust at a Large Surface Coal Mine

Cited by 33 publications

References 9 publications

Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites

Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites

A new rock cuttability index for predicting key performance indicators of surface miners

Vibration Control of Stress Waveform Superposition in Millisecond Blasting

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