Hevea brasiliensis oil epoxidation: hybrid genetic algorithm–neural fuzzy–Box–Behnken (GA–ANFIS–BB) modelling with sensitivity and uncertainty analyses

• Published in: Multiscale and Multidisciplinary Modeling, Experiments and Design Vol. 4; no. 2; pp. 131 - 144
• Main Authors: Oke, Emmanuel O., Nwosu-Obieogu, Kenechi, Okolo, Bernard I., Adeyi, Oladayo, Omotoso, Agbede O., Ude, Chiamaka U.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021
• Subjects: Characterization and Evaluation of Materials; Engineering; Mathematical Applications in the Physical Sciences; Mechanical Engineering; Numerical and Computational Physics; Original Paper; Simulation; Solid Mechanics; Rubber seed oil; Epoxidation; GA-ANFIS-BBD; Mean square error
• ISSN: 2520-8160; 2520-8179
• DOI: 10.1007/s41939-020-00086-y

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–Behnken (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 R 2 (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 R 2  = 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 51.76% 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.