Design of Metallic Catalytic Converter using Pareto Optimization to Improve Engine Performance and Exhaust Emissions

نویسندگان

چکیده

In this paper, Metallic Catalytic Converter (MCC) is installed in motorcycle exhausts to produce the minimum CO as well optimum engine power. The results from previous research were collected and then used predict best MCC design using Artificial Neural Network Multi-Objective Genetic Algorithm (ANN-MOGA). addition, ANN parameter tuning process was also carried out Taguchi method find initial weighting bias that able provide most stable performance design. two sets of solutions 70 Pareto obtained by ANN-MOGA. Those designs are emission multi-objective verification show tends be superior terms emissions emissions, gets a larger S/N ratio -10.98, while only an -11.21. Meanwhile, power, 16.13, 15.86 S/N. It line with ANOVA test which proven better than

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ژورنال

عنوان ژورنال: Automotive Experiences

سال: 2023

ISSN: ['2615-6202', '2615-6636']

DOI: https://doi.org/10.31603/ae.7977