Performance evaluation of adaptive neuro-fuzzy inference system, artificial neural network and response surface methodology in modeling biodiesel synthesis from palm kernel oil by transesterification

ABSTRACT

Modeling capabilities of an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and response surface methodology (RSM) were assessed in the transesterification of esterified palm kernel oil (PKO) with methanol in the presence of KOH as a catalyst. A central composite rotatable design (CCRD) of RSM was applied using methanol/oil ratio (0.25–0.50 v/v), catalyst loading (0.75–2.00 w/v) and reaction time (30–70 min) as the independent variables and palm kernel oil biodiesel (PKOB) yield as the response. Statistical performance indicators showed ANFIS (coefficient of determination, R² = 0.99, mean absolute error, MAE = 0.21 and mean relative percentage deviation, MRPD = 0.22%) and ANN (R² = 0.99, MAE = 0.23, MRPD = 0.24%) models describe the process with higher precision and accuracy compared to RSM (R² = 0.79, MAE = 1.05, MRPD = 1.12%). To maximize the PKOB yield, the process input variables investigated were optimized using an RSM optimization tool and genetic algorithm (GA) coupled with the developed ANFIS, ANN and RSM models. The best combination of the estimated process input variables (methanol/oil ratio 0.48 v/v, catalyst loading 0.86 w/v and reaction time 71.5 min) with highest PKOB yield (99.5 wt.%) was given by ANFIS-GA.

KEYWORDS:

• Transesterification
• adaptive neuro-fuzzy inference system
• artificial neural network
• response surface methodology
• genetic algorithm

Acknowledgements

The authors thankfully acknowledge technical assistance offered by O.O. Adeyemi, A.J. Adesina and O.O. Oyetunde.

Disclosure statement

The authors declare that there is no potential conflict of interest.