May 24, 2024
Shoaib Khanmohammadi

Shoaib Khanmohammadi

Academic rank: Associate professor
Address: Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran
Education: Ph.D in Mechanical Engineering
Phone: 0833-8305001
Faculty: Faculty of Engineering

Research

Title
Comparative of various bio‐inspired meta‐heuristic optimization algorithms in performance and emissions of diesel engine fuelled with B5 containing water and cerium oxide additive blends
Type Article
Keywords
ALO, ANN, combustion modeling, diesel engine, GOA, GWO
Researchers Esmail Khalife، Mohammad Kaveh، Abdollah Younesi، Dhinesh Balasubramanian، Shoaib Khanmohammadi، Bahman Najafi

Abstract

In this study, a diesel engine combustion was modeled to estimate engine performance and emissions for the first time in the field of engine combustion. Four different algorithms including Grasshopper Optimization Algorithm, Ant Lion Optimizer, and Gray Wolf Optimization as well as Artificial Neural Network were employed to predict thermal efficiency, fuel consumption, CO, HC, and NOx emissions of a diesel engine fueled with diesel-biodiesel blend emulsions containing water and cerium oxide nano additives. The models proposed were developed using two inputs (fuel type and engine load). The results showed that the Gray Wolf Optimization led to maximum coefficient correlation (0.9940 and 0.9966) and minimum Mean Square Error compared with the other employed algorithms for brake thermal efficiency and brake-specific fuel consumption. The best results were obtained for Gray Wolf Optimization, Ant Lion Optimizer, and Grasshopper Optimization Algorithm, respectively. The same sequence was also found for estimating engine emissions. However, Gray Wolf Optimization showed slightly better result for estimation of engine emission than engine performance. In overall, the results of Gray Wolf Optimization were in perfect agreement with the experimental values compared to the other nature-inspired algorithms as well as Artificial Neural Network in predicting fuel combustion. The model proposed can find application in fuel and engine manufacturers.