Prediction Model of Refined Gasoline Blending Formula Based on PSO-DBN

摘要

To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry, the advantages of deep belief networks (DBNs) in feature extraction and nonlinear processing are considered, and they are applied to the prediction modeling of refined gasoline blending conservative formula. Firstly, based on historical measured data of refined gasoline blending and according to the characteristics of the data set, we use bootstrapping to divide the training data set and the test data set. Secondly, considering that parameter selection for the network is difficult, particle swann optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters, greatly improving optimization efficiency. In addition, the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network, so as to construct a more accurate prediction model for conservative formula. Finally, in order to evaluate the effectiveness of this method, the simulation results are compared with those of traditional modeling methods, which show that the DBNs has better prediction performance than error back propagation and support vector machines, and can provide production guidance for refined gasoline blending formula.

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