RSM and ANN Modeling for Production of Newbouldia Laevies Fibre and Recycled High Density Polyethylene Composite: Multi Objective Optimization Using Genetic Algorithm

Abstract

In this study response surface methodology (RSM), artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) were used for modeling and multi objective optimization of Newbouldia Laevies Fibre and recycled high density polyethylene (RHDPE) composite for fiberboard application. The fiberboard properties optimized were modulus of rupture (MOR), modulus of elasticity (MOE), internal bonding (IB), thickness swelling (TS) and water absorption (WA) whose values vary in response to changes in production process variables namely: Fibre/RHDPE (%), press pressure, press time and temperature. An experimental design using central composite design (CCD) was used to develop an RSM model for modeling the variations in physical and mechanical properties of the fiberboard in response to variations in process parameters. An ANN model was equally used to predict the properties of the fiberboard. Subsequently the ANN was used as the fitness function for multi objective optimization of the fiberboard using NSGA-II. A comparative algorithm was later developed with a traditional multi objective optimization algorithm known as desirability function using the RSM model. The results of the study showed that RSM and ANN effectively modeled the properties of the fiberboard. The optimized Pareto front of the NSGA-II algorithm was found to be an excellent design guide for practical applications of the composite. The superiority of NSGA-II algorithm over the desirability function as a multi objective optimization tool was demonstrated.