Predicting Boiler Emission by Using Artificial Neural Networks

Palm oil is produced in palm oil mills, where palm oil waste can be used (shell and fibre) as fuel for the boilers for generating steam power plants. Unfortunately, the combustion products of these materials cause severe atmospheric pollutions. The emission released through the chimney can be monitored by modeling its process of input (in fuel, turbine, boiler) and output of the pollutants. In this paper, Artificial Neural Networks (ANN) is used to model the emission from the palm oil mill boiler. Multiple Linear Regression (MLR) is also applied to find the coefficient of the contributing element to the pollution in order to make comparison and validate the ANN results. In conclusion, the prediction made by ANN is better than MLR but both agrees well with the actual values collected from the mill