Development of an air attenuation model for noise prediction in surface mines and quarries

The need of developing appropriate noise prediction models for finding out the accurate status of noise levels (>90 dBA) generated from various opencast mining machineries is overdue. The measured sound pressure levels (SPL) of equipments are not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular noise prediction models e.g. VDI and ENM have been applied in mining and allied industries. Among these models, VDI2714 is simple and less complex model. In this paper, a neuro-fuzzy model is proposed to predict the machinery noise in an opencast coal mine. The proposed model is trained with VDI2714 and the model output is seen very closely to matching with VDI2714 output. The model proposed has a mean square error of 2.73%. This model takes CPU time of nearly 0.0625 sec where as it takes 0.5 sec for VDI2714 i.e. approximately twelve times faster.Int. J. Unc. Fuzz. Knowl. Based Syst. 2009.17:729-745. Downloaded from www.worldscientific.com by CHINESE UNIVERSITY OF HONG KONG on 02/06/15. For personal use only. R = source to receiver distance K 2 = atmospheric attenuation = 10 log(1 + 0.0015R)dB(A) K 3 = attenuation due to meteorological conditions = [(12.5/R 2 ) + 0.2] −1 dB(A) K 4 = ground effects = 10 log[3 + (R/160)] − K 2 − K 3 dB(A) K 5 = barrier value (0-10) = 10 log(3 + 20d) dB(A) d = barrier path difference K 6 = attenuation due to woodland areas K 7 = attenuation due to built-up areas.

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“…. R (5) for R. The process of fuzzification of input (L W ) and R is done with triangular membership function. This is shown in Figs.…”

Section: Selection Of Membership Functions For Inputsmentioning

confidence: 99%

Development of a Neuro Fuzzy Model for Noise Prediction in Opencast Mines

Nanda

Tripathy

Patra

2009

Int. J. Unc. Fuzz. Knowl. Based Syst.

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“…prediction in limestone quarry and mines of Ireland. The model was used to predict attenuation in air due to absorption [7].…”

Section: Machinery Noise Prediction In Opencast Minesmentioning

confidence: 99%

Machinery Noise Prediction in Opencast Mines Using Concawe Model: A Case Study

Nanda¹,

Tripathy

2010

Noise & Vibration Worldwide

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Noise is often regarded as a nuisance rather than as an occupational hazard in an opencast mining environment. Prolonged exposure to noise over a period of years generally causes permanent damage to the auditory nerve and/or its sensory components. To maintain the good working environment in mines, appropriate noise survey of machineries should be conducted. The measured sound pressure levels (SPL) of equipment are not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular noise prediction models e.g. CONCAWE, VDI and ENM have been applied in mining and allied industries to predict the machineries noise by considering all the attenuation factors. In this paper, authors have predicted machineries noise in an opencast mine using CONCAWE model.

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“…Air attenuation model was developed for noise prediction in limestone quarry and mines of Ireland. The model was used to predict attenuation in air due to absorption [8].…”

Section: Introductionmentioning

confidence: 99%

Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

Nanda¹,

Tripathy

2011

Advances in Fuzzy Systems

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Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN), polynomial perceptron network (PPN), and Legendre neural network (LeNN) were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

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Development of an air attenuation model for noise prediction in surface mines and quarries

Cited by 5 publications

References 6 publications

Development of a Neuro Fuzzy Model for Noise Prediction in Opencast Mines

Development of a Neuro Fuzzy Model for Noise Prediction in Opencast Mines

Machinery Noise Prediction in Opencast Mines Using Concawe Model: A Case Study

Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

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