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- W4320733769 abstract "This paper describes a novel approach to model-based optimisation of dual-fuel LPG/diesel engine control maps. Optimal offline optimisation seeks to achieve low fuel consumption, low emissions, and high driveability at the same time. Experiments were performed with a single-cylinder, four-stroke, direct injection diesel engine with a rated power of 3.5 kW in dual fuel mode for a range of loads from 0 to 12 kg. First, using experimental data, engine performance and emissions were predicted in function of the engine load and the LPG/diesel fuel ratio by two architectures of neural networks: The multilayer perceptron and the radial basis function neural networks. Based on the experimental testing data, the root mean square error was found to be 2.75 × 10−5 for engine torque, 0.0024 for fuel consumption, 0.7 for CO2, and 0.8 for NOx. Then, a multi-objective optimisation environment was developed, which computes the basic control maps for the dual fuel engine settings based on the modelled emission behaviour of the engine. Results showed that engine torque increased by 0.1% and fuel consumption decreased by 20%. These results proved that the proposed method has the capability to develop optimised engine control maps for dual-fuel engines." @default.
- W4320733769 created "2023-02-15" @default.
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- W4320733769 date "2023-02-27" @default.
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- W4320733769 title "Multiobjective optimisation of feedforward control maps in dual fuel LPG/diesel engine management systems towards low consumption, low pollutants, and high torque" @default.
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- W4320733769 doi "https://doi.org/10.1080/01430750.2023.2180775" @default.
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