Application of Elman and Cascade neural network (ENN and CNN) in comparison with adaptive neuro fuzzy inference system (ANFIS) to predict key fuel properties of ABE-diesel blends

ABSTRACT

Today, a growing interest to use Acetone-Butanol-Ethanol (ABE) as a biofuel has emerged. Fuel properties play important roles to determine engine’s performance, combustion, and emission behaviors. Yet, the determination of fuel properties is expensive and time-consuming. Previous studies on ABE did not provide information on how to predict its fuel properties. This study developed an Elman and Cascade neural networks (ENN and CNN) and compared their results with adaptive neuro inference system (ANFIS) to predict ABE’s key fuel properties. Three properties, i.e., calorific value, density, and kinematic viscosity were used as the target outputs, while ABE, acetone, butanol, and ethanol ratio were selected as the input parameters. The ENN and CNN models were trained using 10 different training algorithms, while the ANFIS model was examined using eight unique membership functions. To evaluate the prediction accuracy of each model, six different parameters were employed. Results showed that, compared to ENN and CNN, the ANFIS model gave the best performance accuracy with the least errors to predict the key fuel properties of ABE-diesel blends. For calorific value, density, and kinematic viscosity prediction, the best results of the ANFIS model were given by triangular, Pi curve, and trapezoidal membership functions, respectively. Therefore, ANFIS gave the best model of all the investigated models in this study.

KEYWORDS:

• Acetone-butanol-ethanol (ABE)
• Fuel property
• Elman
• Cascade
• Artificial neural network
• Anfis

Acknowledgments

Financial support from the Universiti Teknologi Malaysia (UTM) under the research university grant Q.J130000.3509.06G97 is gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Universiti Teknologi Malaysia [Q.J130000.3509.06G97].