Optimizing oil palm fiberboard properties using neural network

The shortage of rubber wood (RW) supply has increased the demand to reduce its composition in the Medium Density Fiberboard (MDF). Oil palm biomass such as empty fruit bunch (EFB) has been proven to be an excellent substitute to RW. An accurate percentage combination of RW and EFB will produce a high quality MDF. An MDF needs to be tested in terms of mechanical and physical properties so that it meets the required standard. These tests are costly for they involve high amount of resources. The aim of this research is to optimize the properties of MDF so that quality-testing procedures can be reduced. A prediction model will be used to make predictions on the MDF properties. A stepwise multiple linear regression selects the predictor variables to be used as inputs to the input nodes. With these variables, the multilayer perceptron neural network with various training criteria will train the data and finally produce the prediction. The results produced have shown that some of the property tests can be omitted.