Optimization of Machinery Noise in a Bauxite Mine Using Genetic Algorithm

Noise levels produced by various noise sources in opencast mines by regular mining operations are high and exposure to such levels is considered harmful to worker's working in such an environment. Noise measurement program was exercised in the opencast bauxite mine according to the DGMS Technical Circular No. 18 of 1975 and No.5 of 1990. A number of studies have been carried out on machinery noise prediction using various statistical and soft computing techniques, but very few studies have been carried out to find global optimal value of sound pressure level (SPL). The aim of this article is to develop Genetic Algorithm (GA) to find an optimal SPL of the machinery taking into consideration of the distance, directivity index, sound power level (SWL) and other attenuation parameters under several noisy operating conditions according to ISO 9613-2:1996, ISO 6395:2008 and other related standards. Experimental results show that GA is able to converge and find the optimum values faster along with acceptable computational time. By comparing the predicted values with the measured values, it proves the effectiveness of the proposed model as a useful and efficient method for machinery noise optimization problems.