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Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends

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  • Zandie, Mohammad
  • Ng, Hoon Kiat
  • Gan, Suyin
  • Muhamad Said, Mohd Farid
  • Cheng, Xinwei

Abstract

In this research, a multi-input multi-output artificial neural network (MIMO-ANN) is developed, in which 14 features associated with the engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline mixtures are meant to be modelled by a diverse combination of engine/combustion parameters. The selected targets comprise brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), combustion efficiency, coefficient of variance (COV), NOx, CO2, CO and HC emissions, exhaust temperature (Texh), in-cylinder pressure (Pcyl), maximum pressure rise rate (MPRR), heat release rate (HRR), combustion duration (CD) and ignition delay (ID). The inputs variables entail the load, speed, compression ratio, gasoline, biodiesel and diesel ratios, crank angle (CA), injection temperature (Tinj), injection pressure (Pinj), brake mean effective pressure (BMEP) and start of injection (SOI). Sensitivity analysis and outlier detection are applied in order to eliminate less-effective inputs/data points. The prepared data sets are then used to train and test the ANN model, in conjunction with benchmarking the model outcomes using coefficient of determination (R2), average absolute relative deviation (AARD) and relative mean squared errors (RMSE). The R2 ranged within 0.9804–0.9998, which is close to unity, proving that the proposed network is accurately capable of predicting the intended combustion characteristics.

Suggested Citation

  • Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023076
    DOI: 10.1016/j.energy.2022.125425
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    2. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    3. Zhu, Yizi & He, Zhixia & Xuan, Tiemin & Shao, Zhuang, 2024. "An enhanced automated machine learning model for optimizing cycle-to-cycle variation in hydrogen-enriched methanol engines," Applied Energy, Elsevier, vol. 362(C).
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    5. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).

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