Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine

Wang, Huaiyu; Ji, Changwei; Cheng, Shen; Ge, Yiyao; Meng, Hao; Yang, Jinxin; Chang, Ke; Wang, Shuofeng

doi:10.1016/j.energy.2022.123611

Cited by 54 publications

(3 citation statements)

References 57 publications

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“…(2021) used the Low Emissions Analysis Platform (LEAP) model to analyze the carbon emissions of the Yangtze River Delta region in China from 2020 to 2050 under different energy transition scenarios. Wang et al . (2022) applied three advanced machine-learning methods to develop the prediction model of nitrogen oxide, carbon monoxide, and hydrocarbon to reduce emissions and enhance engine calibration efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Wang,

2023

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PurposeThis study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.Design/methodology/approachIn this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.FindingsThe results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.Originality/valueThe paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Wang,

2023

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“…The results showed that adding ether can improve the braking thermal efficiency, peak pressure in cylinder, and net heat release rate of engine. Wang et al 29 compared the accuracy of three machine learning algorithms to predict emissions and fuel consumption of a Wanker rotor engine. The results showed that different models have different advantages, among which the GPR model had very good generalization ability in scarce data sets.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Verification of Performance and Emission Characteristics of Diesel/Natural Gas Dual-Fuel Engine Based on Intelligent Algorithm

Wan

Huang

et al. 2023

ACS Omega

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With the development of computer application technologies, intelligent algorithm has been widely used in various fields. In this study, a coupled Gaussian process regression and feedback neural network (GPR-FNN) algorithm is proposed, and it is used to predict the performance and emission characteristics of a six-cylinder heavy-duty diesel/natural gas (NG) dual-fuel engine. Using the engine speed, torque, NG substitution rate, diesel injection pressure, and injection timing as inputs, an GPR-FNN model is established to predict the crank angle corresponding to 50% heat release, brake-specific fuel consumption, brake thermal efficiency, and carbon monoxide, carbon dioxide, total unburned hydrocarbon, nitrogen oxides, and soot emissions. Subsequently, its performance is evaluated using experimental results. The results show that the regression correlation coefficients of all output parameters are greater than 0.99, and the mean absolute percentage error is less than 5.9%. In addition, a contour plot is used to compare the experimental results with the GPR-FNN prediction data in detail, and the results show that the prediction model has high accuracy. The results of this study can provide new ideas for the research on diesel/natural gas dual-fuel engines.

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“…The optimization process can be accelerated by using the surrogate model approach, and has achieved excellent results in practical applications, especially in the optimization of experimental parameters. Statistical methods are very effective in the application of constructing surrogate models and are very effective in the engineering field [16][17][18]. Ji et al employed the genetic algorithm to optimize the engine performance based on support vector machine intelligent regression.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of the Flue Gas Heat Recovery of a Marine Dual-Fuel Engine Based on RSM and GA

2022

Self Cite

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Implementation of flue gas waste heat recovery is an effective way to improve the energy utilization of marine engines. This paper aims to model and optimize a marine four-stroke dual-fuel (DF) engine coupled with a flue gas waste heat recovery system. Firstly, the DF engine and waste heat recovery system were respectively modeled in GT-Power and Simulink environments and verified with experimental data. Then, a regression model was built using the response surface method, with the intake temperature, compression ratio, and pilot fuel injection timing as input parameters and parametric analysis was performed. Finally, multi-objective optimization of the waste heat recovery system was performed using a genetic algorithm. The result showed that the optimal solution is obtained when the intake temperature is 306.18 K, the geometric compression ratio is 14.4, and the pilot fuel injection timing is −16.68 °CA after the top dead center. The corresponding brake-specific fuel consumption was 155.18 g/kWh, reduced by 3.24%, and the power was 8025.62 kW, increased by 0.32%. At the same time, 280.98 kW of flue gas waste heat generation was obtained.

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Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine

Cited by 54 publications

References 57 publications

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Prediction and Verification of Performance and Emission Characteristics of Diesel/Natural Gas Dual-Fuel Engine Based on Intelligent Algorithm

Modeling and Optimization of the Flue Gas Heat Recovery of a Marine Dual-Fuel Engine Based on RSM and GA

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