A Hybrid Intelligent Method for Predicting Gasoline Octane Number and Optimising Operation Parameters

Abstract

The Research Octane Number (RON) is a significant indicator for reflecting the combustion performance of gasoline, and the products of gasoline combustion provide a major influence on the atmospheric environment. It is crucial to measure RON during the process of gasoline refining. However, current methods generally use the instrument in the laboratory to measure RON, which is time-consuming and expensive. This paper proposes a hybrid intelligent method for predicting RON and optimising operating parameters to reduce RON loss, aiming to improve combustion performance and reduce pollutant emissions. Considering that the feature engineering and model optimision are time-consuming, the RON prediction model is generated and optimised automatically via automatic machine learning technique. To reduce RON loss, the particle swarm optimisation algorithm is applied to optimise the operation parameters based on predicted RON. Subsequently, a real-world industrial dataset is taken as an example to test the accuracy and effectiveness of the proposed model. The results suggest that the proposed model is more accurate than other advanced models, such as Support Vector Machine (SVM) and Decision Tree (DT), with mean absolute error and root mean squared error being 0.162 and 0.225, 46 % and 44 % reduction of the SVM and DT. The experiment also shows that the RON loss of data samples is reduced by more than 40 % after optimisation.