Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement

Ali Alahmer, Hegazy Rezk, Wail Aladayleh, Ahmad O. Mostafa, Mahmoud Abu-Zaid, Hussein Alahmer, Mohamed R. Gomaa, Amel A. Alhussan and Rania M. Ghoniem
Additional contact information
Ali Alahmer: Department of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, P.O. Box 179, Tafila 66110, Jordan
Hegazy Rezk: Department of Electrical Engineering, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, AI-Kharj 16278, Saudi Arabia
Wail Aladayleh: Department of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, P.O. Box 179, Tafila 66110, Jordan
Ahmad O. Mostafa: Department of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, P.O. Box 179, Tafila 66110, Jordan
Mahmoud Abu-Zaid: Mechanical Engineering Department, Mutah University, Alkarak 61710, Jordan
Hussein Alahmer: Department of Automated Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan
Mohamed R. Gomaa: Mechanical Engineering Department, Faculty of Engineering, Al-Hussein Bin Talal University, Ma’an 71111, Jordan
Amel A. Alhussan: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Rania M. Ghoniem: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Mathematics, 2022, vol. 10, issue 3, 1-29

Abstract: Biodiesel is considered to be a promising alternative option to diesel fuel. The main contribution of the current work is to improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used to evaluate the output performance of the compression ignition engine, as follows: brake torque (BT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE), by varying two input parameters (engine speed and fuel type). The engine speeds were in the 1200–2400 rpm range. Three biodiesel blends, containing 20 vol.% of vegetable oil and 80 vol.% of pure diesel fuel, were prepared and tested. In all the experiments, pure diesel fuel was employed as a reference for all biodiesel blends. The experimental results revealed the following findings: although all types of biodiesel blends have low calorific value and slightly high viscosity, as compared to pure diesel fuel, there was an improvement in both BT and brake power (BP) outputs. An increase in BSFC by 7.4%, 4.9%, and 2.5% was obtained for palm, sunflower, and corn biodiesel blends, respectively, as compared to that of pure diesel. The BTE of the palm oil biodiesel blend was the lowest among other biodiesel blends. The suggested work strategy includes two stages (modeling and parameter optimization). In the first stage, a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine. The particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters. To confirm the distinction of the proposed strategy, the obtained outcomes were compared to those attained by response surface methodology (RSM). The coefficient of determination ( R 2 ) and the root-mean-square-error (RMSE) were used as comparison metrics. The average R 2 was increased by 27.7% and 29.3% for training and testing, respectively, based on the fuzzy model. Using the proposed strategy in this work (integration between fuzzy logic and PSO) may increase the overall performance of the compression ignition engine by 2.065% and 8.256%, as concluded from the experimental tests and RSM.

Keywords: optimization; fuzzy model; response surface methodology; diesel engine performance; biodiesel (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (10)

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