Monitoring rubber factory malodour using artificial neural network

Malodour from rubber factory activities is a nuisance to the residence in the surrounding area. the volatile chemical compounds emitted from the factory are identified as the major source of the malodour. this research proposes a study of malodour monitoring for the rubber factory using electronic nose (e-nose) system to enable effective malodour monitoring. the proposed system will able to predict and monitor the malodour concentration in the surrounding area of the factory. the finding will be used as a guideline for monitoring the violations of environmental guidelines. the malodour and environment parameters such as temperature,wind speeds and directions are recorded. these parameters are measured on-site and monitored using wireless sensor network (wsn) system. the malodour concentration model of each location will generate using three types of artificial neural network (ann) i.e. multilayer perceptron (mlp),radial basis function (rbf) and probabilistic neural network (pnn). these indices will be used to generate the malodour concentration mapping model of the factory and surrounding areas. the initial results show that the system can be effectively used for real time rubber factory malodour monitoring. © 2015 penerbit utm press. all rights reserved.