Hevea Brasiliensis Oil Epoxidation: Hybrid Genetic Algorithm-Neural Fuzzy- Box-Behnken (Ga-Anfis-Bb) Modelling With Sensitivity And Uncertainty Analyses

ABSTRACT

Convectional algorithms such as least-square and gradient descent for Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction of engineering process system is deficient by local optimum trapping problem. Therefore, this study is aimed at developing novel hybrid Genetic Algorithm (GA)-ANFIS- Box- Behken (BB) model for Hevea Brasiliensis Seed Oil Epoxidation (HBSOE) prediction. Computer codes for traditional ANFIS and hybrid GA-ANFIS modelling were written in Matlab 2015 environment. Whereas BB numerical optimization technique of Response Surface Methodology (RSM) of design expert V10 software was used to select optimum GA-ANFIS tuning parameters (Population Size (PS), Crossover Percentage (COP) and (MR): Mutation Rate). Sensitivity and uncertainty analyses on GA-ANFIS-BB model output (MSE) were investigated using Monte Carlo simulation in Crystal Ball software. ANFIS optimum result with gbell membership function gave R² (0.69651), MSE (0.0825). Optimum GA-ANFIS-BB parameters (PS =90, COP=0.162, and MR=0.305) gave minimised MSE = 0.0085 and R² = 0.998. The results showed that GA-ANFIS-BB predictability degree is higher than ANFIS; thus, GA-ANFIS-BB predicted HBSOE satisfactorily.  Mean Monte Carlo base case simulation gave 65.09% certainty of the MSE. Sensitivity analysis shows that COP and MR have 51.7.6% and 26.6% negative percentage contribution on MSE respectively; while PS shows a positive 21.7% contribution. Thus, GA-ANFIS-BB model in this study can be used as a precursor and predictive tool for HBSOE fuzzy-based controller system design