An Industrial Data-Based Model to Reduce Octane Number Loss of Refined Gasoline for S Zorb Process

Chen, Bo; Wang, Jie; Liu, Song; Ouyang, Fusheng; Xiong, Da; Zhao, Mingyang

doi:10.1134/s0965544123010036

Cited by 2 publications

(2 citation statements)

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“…Each increase of RON can reduce fuel consumption by up to 1.4%, and the annual total oil saving of global gasoline vehicles can also reach amazing values. Therefore, improving RON also indirectly contributes to carbon https://doi.org/10.37358/RC.24.1.8580 peak and carbon neutrality, which is of great significance to fuel resource conservation and environmental protection [7].…”

Section: Introductionmentioning

confidence: 99%

“…Qin Qingtao and Gu HNA considered the nonlinearity and mutual strong coupling between the variables [14] and used the multivariate autoregression log-linear method to select the main variables to establish the RON prediction model. Chen Chan and Hu et al firstly selected 25 feature variables using the Pearson-MIC-random forest method, [7] then predicted and optimized the XGBoost and optimization model of retained RON loss value. The RMSE, MAE (which equals to the total absolute value of the difference between the target value and the predicted value [15]) and the coefficient of determination of the model were 1.3197, 0.3581, and 0.9981 respectively.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Octane Retention Prediction Model for Catalytic Cracked Gasoline Based on Ridge Regression Model and Gradient Descent Optimization

Lyu,

Yang,

Lyu

2024

Rev. Chim.

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On the basis of the given material, in order to increase the RON retention of the catalytic cracking unit, the prediction model of gasoline octane retention and the best operation variable inversion model were established based on the Ridge regression model and Gradient descent method. First, based on the Ridge regression model, the leave-one method is used to obtain the relative importance of the operational variables, and select the most important variables, so as to reduce the characteristic dimension of the model; Then, the RON retention prediction model is trained based on the Ridge regression model; Finally, based on the trained Ridge regression model and its weight parameters, the optimal operating variables were optimized separately using the gradient when the operation variable has a range or no range of value. The experimental results show that when 146 are selected from 361 operating variables, the model loss value stabilizes; when α is 0.6, the test set R2 is 0.9882, test set MSE is 0.0193, and the comprehensive performance is better than the random forest, support vector machine model; When the operation variable has two categories of value range and no value range, 2,000 times, the best inversion value of the operation variable makes the RON retention prediction value of the test sample similar to the expected value, and the MAE drops to 2.89999�10-3 and 7.62939�10-6, respectively. In conclusion, the RON retention prediction model proposed in this study has good results, and the best operating variable can be reversed, based on the given material parameters, making the optimal RON retention quantity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%