Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm. (1st January 2023)

• Record Type: Journal Article
• Authors: Liu, Jintao; Chen, Liangchao; Xu, Wei; Feng, Mingfei; Han, Yongming; Xia, Tao; Geng, Zhiqiang
• Is Part Of: Energy. Volume 262:Part B(2023)
• Publication Date: 2023-01-01
• Subjects: Gated recurrent unit neural network -- Attention mechanism -- Uniform manifold approximation and projection -- Dimensionality reduction -- Output prediction optimization -- Gasoline production process; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125536
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Gasoline, as an extremely important petroleum product, is of great significance to ensure people's living standards and maintain national energy security. In the actual gasoline industrial production environment, the point information collected by industrial devices usually has the characteristics of high dimension, high noise and time series because of the instability of manual operation and equipment operation. Therefore, it is difficult to use the traditional method to predict and optimize gasoline production. In this paper, a novel production prediction model using an attention mechanism (AM) based gated recurrent unit (GRU) (AM-GRU) integrating the uniform manifold approximation and projection (UMAP) is proposed. The data collected in the industrial plant are processed by the box plot to remove the data outside the quartile. Then, the UMAP is used to remove the strong correlation between the data, which can improve the running speed and the performance of the AM-GRU. Compared with the existing time series data prediction method, the superiority of the AM-GRU is verified based on University of California Irvine (UCI) benchmark datasets. Finally, the production prediction model of actual complex gasoline production processes for energy saving and economic increasing based on the proposed method is built. The experiment results show that compared with other time series data prediction models, the proposed model has better stability and higher accuracy with reaching 0.4171, 0.9969, 0.2538 and 0.5038 in terms of the mean squared error, the average absolute accuracy, the mean squared error and the root mean square error. Moreover, according to the optimal scheme of the raw material, the inefficiency production points can be expected to increase about 0.69 tons of the gasoline yield and between about $645.1 and $925.6 of economic benefits of industrial production. Graphical abstract: Image 1 Highlights: A novel AM-GRU integrating the UMAP algorithm is proposed. A realistic gasoline production prediction model of actual industries is established. The proposed method can achieve state-of-the-art performance. The gasoline yield of low production points can be increased by about 0.69 tons.