Open Cut

Live Noise Prediction Method for Australian Conditions

Open Cut » Environment

Published: May 13Project Number: C21026

Get ReportAuthor: Peter Sanderson | AECOM Australia

Discussion with various mining clients in the Hunter Valley has made it clear that the traditional approach to noise impact assessment is of limited day to day use on mine sites. It has been indicated that noise from mobile plant is a frequent and recurring cause for complaint in relation to mine site activity. Current assessment methodology does not allow for the inherent nature of mobile plant, nor the 'on the ground' operational decisions that can alter the relationship between mobile noise sources and noise sensitive receivers.


The current methodology for noise impact assessment employs a 'pre operational prediction/post start up monitoring' approach. This restricts noise assessment to pre-determined scenarios that represent a 'snap shot' view of the overall operations and prescribes a reactive response to complaints that can often take weeks or months to resolve.


A noise model that is constantly predicting the noise impact at sensitive receivers based on up-to-date GPS information from mobile sources on site, current meteorological data and the operational status of fixed plant could be used not only to determine compliance and assess complaints, but also to identify operational problems with specific plant items.


Originally, the primary objective for the project was to develop software that would predict 'live' noise levels at sensitive receivers, resulting from static and mobile plant operating on open cut mining projects. The model was also to consider existing meteorological conditions. This has not changed, but the methodology employed to realise the goal has evolved since the project inception. The objectives of the project are discussed below.


The key objective was 'the development of an end-user system based software application that uses mobile plant GPS data and pre-measured plant noise levels to predict the noise impact at noise sensitive receivers on an ongoing basis'.


Several changes took place during the course of the project, including the program used to develop the noise propagation algorithms and the switch from an end-user based system to a cloud based hosting system.


The revised objectives for the project are were:

- Develop a database of plant sound power levels;

- Develop noise propagation software using Python programming language and run successful propagation tests;

- Develop and interface between software and mine site operational software;

- Allow for import of meteorological data and GPS data;

- Test software and complete modelling runs;

- Calibrate the software predictions with data from the mine monitoring network.


During the course of the project there were various issues associated with obtaining site specific data. In order to ensure a robust test of the software and avoid timing issues, it was decided that a proxy database be used to test the software. Dummy databases (i.e. not receiving constant live GPS data) holding real GPS data and plant sound power data from Bulga mine were established and used to test the propagation algorithms.


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