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Octane number is a measure of gasoline’s ability to resist detonation and combustion in the cylinder; the higher the value, the better the resistance to detonation. The accurate prediction of octane loss during gasoline refining could facilitate production management and ensure gasoline octane. The backpropagation neural network is a traditional method adopted for the octane loss prediction, but there exists the issues of low training accuracy and poor generalization in the traditional BP neural network model caused by randomly generated weights and thresholds at input. In this paper, we propose a novel approach to optimize the weights and thresholds for gasoline octane number prediction based on a self-adaptive genetic algorithm. The experimental result shows that the proposed model outperforms in accuracy and generalization in the competition with the traditional BP neural network. The coefficient of determination  R 2 of the performance index in the experiment is improved from 0.81502 to 0.95628, and the average prediction error among 10 groups of experiments was reduced from 0.0061 to 0.0041.

Applying Machine Learning to Chemical Industry: A Self-Adaptive GA-BP Neural Network-Based Predictor of Gasoline Octane Number