A grey wolf optimization approach for evaluating the engine responses of various biodiesel blends in an internal combustion engine - Chukwuka Prosper Ozule; Adeyinka Oluwo; Nehemiah Sabinus Alozie; John Rajan; Swaminathan Jose; Sunday Ayoola Oke; Ugochukwu Sixtus Nwankiti (Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ)
The knowledge of the exact thresholds of parameters in the diesel engines, during combustion, is essential to simulate the combustion process, establish parametric values, reduce cost and predict exhaust emissions. Accordingly, the present paper applies the grey wolf optimization method to determine the optimal threshold of parameters and engine responses in a direct ignition engine. Twelve formulated linear equations of engine responses are introduced to the objective function of the grey wolf optimizer. A computer program in C++ was applied successfully using literature data to validate the grey wolf optimization procedure based on the encircling, hunting and attacking of prey by the wolf. The results show that load demand and turbocharge boast air pressure have the least and highest values of engine outputs, respectively. The blend ratio had its highest values when optimized alongside the main injection duration. The responses and parameters greatly improved from initial values to stopping criterion of 200 iterations. Instances reported include brake specific fuel consumption, which improved from 2.6468 to 1.0816 g/kWhr, blend ratio changes from 0.5031 to 0.4760%, speed drop from 0.0031 to 0.0010rpm, and load drop from 0.0017 to 0.0010%. The main contribution of this paper is to establish the optimal thresholds of engine responses using the grey wolf optimizer in a diesel engine combustion chamber. The development of a new method to optimize response and parameters of an internal combustion process using grey wolf optimizer is the novel aspect of this work. The results have essential practical significance to establish new emission profile for biodiesel. The practising engineers and researchers have a holistic insight into the problem’s solution and can utilize the results to enhance their engine responses.