Neuro fuzzy-grey wolf optimization-based modelling and analysis of diesel engine using tire oil with different proportions of 2-EHN

Abstract

Globally, billions of automotive tires are discarded each year. However, managing their disposal is challenging. One of the options involves the pyrolysis of discarded tires to generate tire pyrolysis oil, as engine fuel. This study investigates the incorporation of 2-EHN into two distinct desulfurized TPO blends with advanced injection timing as a solution to counter the low cetane number of TPO. It examines three distinct concentrations of 2-EHN i.e. 0.15 %, 0.30 %, and 0.45 %, in TPO blends of 20 % and 40 %. The findings indicate the incorporation of 2-EHN into TPO led up to 9.8 % enhancement in thermal efficiency and 15 % reduction in ignition delay. Additionally, it resulted in reduced smoke and NOx by up to 29 % and 19 %, respectively, although a slight rise in HC and CO emissions was observed. However, further investigations into the engine’s behavior are constrained by the complexities involved, time limitations, and restrictions on experimental costs. To overcome these constraints, novel empirical models have been devised by integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the grey wolf optimization (GWO) technique. The R, R² values were near one, and low values of MAPE, MSE, and RMSE demonstrated strong alignment with the experimental findings.