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

Abstract

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.

Artificial Neural Networks and Advanced Fuzzy Techniques for Predicting Noise Level in the Industrial Embroidery Workrooms

“…The prediction errors of noise by the developed ANNs and GENFIS and ANFIS prediction models were lower than the subjective difference threshold for sound pressure level ±1 dB mentioned in ISO 3382 (ISO 3382-1 2008). The prediction error rate of developed techniques was at an acceptable level compared with similar studies that used AI to predict the sound level in architectural and environmental acoustics areas (Nanda, Tripathy, and Patra 2009;Black et al 2004;Nannariello, Hodgson, and Fricke 2001). The results confirm the high capabilities of AI approaches in improving the performance of acoustics prediction techniques compared with those of current empirical techniques developed using classical methods such as regression techniques in typical workrooms (Heerema and Hodgson 1999).…”

A genetic algorithm approach for optimization of machinery noise calculations

“…These statistical models are largely used to estimate noise in the mines and its allied industries. 3,4 Algorithm utilized as a part of these models depends for a larger part on measured data, which is a legitimate and beneficial method; however, their applications are constrained to sites which are more or less alike to those for which the measured data were integrated. 3 Numerous models were developed and broadly utilized for the evaluation of SPL and their attenuation in and around industrial complexes.…”

“…3 From the study, it was inferred that the prediction models could be utilized to distinguish the safe zones with respect to the SPL values in the mining and industries. [2][3][4] It was also concluded that the ISO 9613-2 model is the better model for prediction of noise in mining industries and other workplaces. All the noise prediction models consider noise as a function of distance, SWL and various attenuation factors.…”

“…These statistical models are largely used to estimate noise in the mines and its allied industries. 3,4…”

“…Numerous models were developed and extensively used for the assessment of SPL but less number of studies were carried out in finding the optimum noise levels. 2 , 4 , 12 One of the applicable techniques to analyse the measured data is to model the SPL of various noise sources using genetic algorithm (GA). 13 Mahesh et al 14 found that optimization carried out using GA yields the best optimal solution.…”