Simultaneous prediction of CO2, CO, and NOx emissions of biodiesel-hydrogen blend combustion in compression ignition engines by supervised machine learning tools

Liwu Zhang, Guanghui Zhu, Yanpu Chao, Liangbin Chen and Afshin Ghanbari

Energy, 2023, vol. 282, issue C

Abstract: This study mathematically inspects the effects of engine speed/load and biodiesel–hydrogen fuel blend composition on CO2, CO, and NOx emissions. Since this modeling task requires a basic knowledge of mechanics, chemistry, and combustion, the literature suggests no approach to anticipate these emissions. This multi-input and multi-output (MIMO) problem is so complex that can only be simulated by special types of machine-learning tools. Consequently, this study utilizes the multi-output least-squares support vector regression (MLS-SVR) and two efficient artificial neural networks (cascade feedforward and multilayer perceptron) to handle this complicated MIMO regression. These intelligent models are constructed and tested using 307 × 3 experimental emissions related to the combustion of different biodiesel–hydrogen blends at various engine speeds and loads. Extensive numerical analyses and ranking tests are conducted to confirm that MLS-SVR is the best approach to simultaneously predict the actual CO2, CO, and NOx emissions with the mean absolute relative error of 2.75%, 7.00%, and 2.62%, and regression coefficient of 0.9966, 0.9947, and 0.9979, respectively. The applicability domain inspection approves that only seven emission measurements may be problematic samples and all other 300 samples are reliable.

Keywords: Compression ignition engine; Biodiesel – hydrogen fuel blend; Emission; Multi-input and multi-out modeling; Machine learning tools (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023666

DOI: 10.1016/j.energy.2023.128972

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