APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN PREDICTING PALM OIL MILL EMISSION

I.A. Azid (School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia 14300 Nibong Tebal, Pulau Pinang, Malaysia)
A.R. Yusoff (School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia 14300 Nibong Tebal, Pulau Pinang, Malaysia)
K.N. Seetharamu (School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia 14300 Nibong Tebal, Pulau Pinang, Malaysia)
A.L. Ahmad (School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia 14300 Nibong Tebal, Pulau Pinang, Malaysia)

ASEAN Journal on Science and Technology for Development
Vol. 20 No. 1 (2003): ASEAN Journal on Science and Technology for Development (AJSTD)

The paper presents an approach to investigate and monitor the air pollution caused by the palm oil mill. A concept of dealing with the problem from its causes is used where the sources of pollution from the stack gases were examined. The main causes were from the combustion of shell fibre and of the palm oil. However, in the boiler itself, several parameters like steam load and pressure, fuel capacity and temperature also contribute to the pollution. The study uses Neural Network (NN) to simulate the process of combustion and stack gases. This neural network was trained by using the data on emission and combustion bed taken from local palm oil plant in Perak, Malaysia. The trained data by NN agrees well with the measured data, i.e. almost within 8% error for pollutants like CO, SO2, NO and particulate matters.