The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

Wang, Qifan; Yang, Ruomiao; Sun, Xiaodong; Liu, Zhentao; Fu, Jiahong; Li, Ruijie

doi:10.3390/pr10040717

Cited by 7 publications

(3 citation statements)

References 70 publications

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“…The SVR model [21,22] is known for its strong generalization ability when dealing with high-dimensional nonlinear problems with limited data. It has been widely employed in various applications such as transmission line discharge, battery life prediction, and load prediction.…”

Section: Svr Modelmentioning

confidence: 99%

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

Hou

2023

Processes

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A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) algorithm for load prediction. Using measured load data from a specific user-level IES at the Tempe campus of Arizona State University, USA, as a case study, a comparative analysis between the VTDS method and other approaches is conducted. The results demonstrate that the method proposed in this study achieved higher accuracy in short-term forecasting of the IES’s multiple loads.

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Section: Svr Modelmentioning

confidence: 99%

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

Hou

2023

Processes

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“…Одним із таких параметрів, що описують процес згоряння палива, є кут α 50 , який відноситься до моменту, коли згорає 50% палива. Цей кут безпосередньо впливає на вказаний середній ефективний тиск циркуляції що в свою чергу впливає на економічні та робочі параметри двигуна внутрішнього згоряння [20][21][22][23][24]. Кілька авторів сходяться на думці, що для досягнення найвищої ефективності та зазначеного середнього ефективного тиску ідеальний кут α 50 для двигунів з іскровим запалюванням знаходиться в діапазоні від 8 до 10° обертання колінчастого валу за верхньою мертвою точкою [22][23][24][25].…”

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“…Кілька авторів сходяться на думці, що для досягнення найвищої ефективності та зазначеного середнього ефективного тиску ідеальний кут α 50 для двигунів з іскровим запалюванням знаходиться в діапазоні від 8 до 10° обертання колінчастого валу за верхньою мертвою точкою [22][23][24][25]. Однак деякі автори розширюють цей інтервал до 5-11° [20]. Як видно з графіка (рис.…”

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Influence of the Composition of the Synthetized Gas on the Combustion Process of the Mixture in the Internal Combustion Engine

Chríbik¹,

Polóni²,

Magdolen³

et al. 2023

МіСАПР

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The article is devoted to the use of low-energy synthesized gas (synthesis gas) as a fuel for internal combustion engines, which in turn transmit mechanical energy to a cogeneration plant (combined production of heat and electricity). Such power plants allow you to achieve high values of total effective efficiency when using a conventional internal combustion engine with spark ignition. The analysed synthetic gases can be obtained by the method of gasification of household waste with access to air. In this work, synthesis gases were obtained in laboratory conditions. The composition of the components of the studied synthesis gases corresponds to several gas mixtures that are created during the artificial gasification of household waste of certain categories. The influence of the composition of the synthesis gas components on the internal parameters of the internal combustion engine was studied. The process of supplying fuel to the engine was controlled by a standard block with several sensors connected to it in the combustion chamber and the exhaust manifold. Engine operation on all synthesis gas mixtures was compared with operation on pure methane mixture. The analysis shows that the drop in efficiency indicators in the form of indicator mean effective pressure (IMEP) ranges from 10% to 40% compared to work on a pure methane mixture. With an increase in the proportion of hydrogen in the synthesis gas, the stiffness of the engine, as well as the rate of heat generation, increases. When the synthesis gas contained a high proportion of carbon monoxide, the stiffness of the engine was the lowest. The main combustion time of synthesis gas is reduced when hydrogen is added to the mixture, and due to an increase in the proportion of methane or carbon monoxide, it increases on the contrary. The presented results make it possible to analyses the processes occurring in the internal combustion engine, as well as the influence of the components of the synthesis gas produced from renewable energy sources. This will make it possible to adjust the gas production process in such a way as to achieve the highest possible energy and economic indicators of its utilization in the cogeneration plant. Keywords: internal combustion engine, synthesis gases, analysis of pressure

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Appraising machine learning algorithms in predicting noise level and emissions from gasoline-powered household backup generators

Giwa,

Nwaokocha,

Osifeko

et al. 2024

Int. J. Environ. Sci. Technol.

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Machine learning is presently receiving great attention. However, machine learning applications to gasoline engine research are limited. This paper investigated the implementation of various machine learning models in predicting the emissions (CO2, CO, and PM2.5) and noise levels of gasoline-powered household generators for the first time. Data of operating and installed capacity, efficiency (input) and emissions, and noise level (output) obtained from 166 generators were used in extreme gradient boosting, artificial neural network (ANN), decision tree (DT), random forest (RF), and polynomial regression (PNR) algorithms to develop predictive models. Results revealed high prediction performance (R2 = 0.9377–1.0000) of these algorithms marked with very low errors. The implementation of PNR followed by the RF exhibited the best models for predicting CO2, CO, PM2.5, and the noise level of generators. R2 of 1.000 and 0.9979–0.9994, mean squared error of < 10−6 and 2 × 10−5–8.6 × 10−5, mean absolute percentage error of 9.15 × 10−16–1.3 × 10−15 and 7.1 × 10−3–8.1 × 10−2, and root mean squared error of 3.3 × 10−16–5.4 × 10−16 and 4.4 × 10−3–9.3 × 10−2 were recorded for all the output parameters using PNR and RF respectively. DT models had the least prediction capacity for CO, CO2, and noise levels (R2 = 0.9493–0.9592) while ANN produced the least performance for PM2.5 (R2 = 0.9377). This study further strengthens machine learning applications in engine research for the prediction of various output parameters.

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The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method

Cited by 7 publications

References 70 publications

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

Influence of the Composition of the Synthetized Gas on the Combustion Process of the Mixture in the Internal Combustion Engine

Appraising machine learning algorithms in predicting noise level and emissions from gasoline-powered household backup generators

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