Abstract
In this study, the combination of artificial neural network (ANN) and non-dominated sorting genetic algorithm II (NSGA-II) has been implemented for modeling and reducing CO and NOx emissions from a direct injection dual-fuel engine. A multi-layer perceptron (MLP) network is developed to predict the values of the emissions based on experimental data. The controllable variables such as engine speed, output power, intake temperature, mass flow rate of diesel fuel, and mass flow rate of the gaseous fuel are considered as input parameters. In order to identify the uncertainties due to the experiments and the ANN-based model, uncertainty analysis is carried out. Finally, optimum values of intake temperature, mass flow rate of diesel and gaseous fuels are obtained for a desired output power and engine speed via NSGA-II. The use of the developed evolutionary optimization algorithm allows the calculation of the Pareto-optimal set of designs under any combination of engine speed and output power, defined in the range of the experiments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
