Research on the Real-time PM Emission Prediction Method for the Transient Process of Diesel Engine based on Transformer Model

Chen, Ziqiang; Lu, Kangbo; Wang, Zhe; Shi*, Lei; Deng, Kangyao; Luo, Xiaochun

doi:10.4271/2023-32-0156

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…With the advent of machine learning, predictive models such as support vector machine (SVM) (Cortes and Vapnik, 1995), support vector regression (SVR) (Chen et al, 2013), Bayesian network (Das and Ghosh, 2015), Gaussian process (GP) (Rasmussen, 2004), and random forest (RF) (Breiman, 2001) began to outperform traditional statistical counterparts (Chen, 2021). Machine learning's innate ability in nonlinear modeling and generalization has substantially strengthened prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

Xie,

Li,

Yang

et al. 2023

Front. Mar. Sci.

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The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union’s incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port—a region characterized by high shipping activity—this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.

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