Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms

Okeleye, Samuel Adeola; Thiruvengadam, Arvind; Perhinschi, Mario G.; Carder, Daniel

doi:10.1016/j.energy.2023.130117

Energy

2024

DOI: 10.1016/j.energy.2023.130117

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Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms

Samuel Adeola Okeleye,

Arvind Thiruvengadam,

Mario G. Perhinschi

et al.

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2024

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Cited by 3 publications

References 31 publications

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An experimental and ANN analysis of ammonia energy integration in biofuel powered low-temperature combustion engine to enhance cleaner combustion

Ramalingam,

Vellaiyan,

Gupta M

et al. 2024

Case Studies in Thermal Engineering

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An experimental and ANN analysis of ammonia energy integration in biofuel powered low-temperature combustion engine to enhance cleaner combustion

Ramalingam,

Vellaiyan,

Gupta M

et al. 2024

Case Studies in Thermal Engineering

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Optimal control of Hydrocarbon Reducer (HC) injection based on Trust-Region-Reflective Algorithm (TRRA) and physical models for Diesel Oxidation Catalyst (DOC)

Liu,

Gao,

You

et al. 2024

Journal of Hazardous Materials

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Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm

Yang,

Li,

Cheng

et al. 2024

Sustainability

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A high proportion of new energy has become a prominent feature of modern power systems. Due to the intermittency, volatility, and strong randomness in wind power generation, an accurate and reliable method for the prediction of wind power is required. This paper proposes a modified stacking ensemble learning method for short-term wind power predictions to reduce error and improve the generalization performance of traditional single networks in tackling the randomness of wind power. Firstly, the base learners including tree-based models and neural networks are improved based on the Bagging and Boosting algorithms, and a method for determining internal parameters and iterations is provided. Secondly, the linear integration and stacking integration models are combined to obtain deterministic prediction results. Since the modified stacking meta learner can change the weight, it will enhance the strengths of the base learners and optimize the integration of the model prediction to fit the second layer prediction, compared to traditional linear integration models. Finally, a numerical experiment showed that the modified stacking ensemble model had a decrease in MAPE from about 8.3% to 7.5% (an absolute decrease of 0.8%) compared to a single learner for the 15 min look-ahead tests. Changing variables such as the season and predicting the look-ahead time showed satisfactory improvement effects under all the evaluation criteria, and the superiority of the modified stacking ensemble learning method proposed in this paper regarding short-term wind power prediction performance was validated.

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Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms

Cited by 3 publications

References 31 publications

An experimental and ANN analysis of ammonia energy integration in biofuel powered low-temperature combustion engine to enhance cleaner combustion

An experimental and ANN analysis of ammonia energy integration in biofuel powered low-temperature combustion engine to enhance cleaner combustion

Optimal control of Hydrocarbon Reducer (HC) injection based on Trust-Region-Reflective Algorithm (TRRA) and physical models for Diesel Oxidation Catalyst (DOC)

Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm

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