| Autor: |
Qianqian Zheng, Yijun Fan, Zhi Zhou, Hongbo Jiang, Xiaolong Zhou |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Energies, Vol 16, Iss 14, p 5332 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en16145332 |
| Popis: |
In the refining process, a large amount of data are generated in daily production, and how to make full use of these data to improve the accuracy of simulation is the key to improving the operation level of refineries. At the same time, with the increasing environmental regulations and the improvement of gasoline and diesel quality standards, the ratio of diesel to gasoline is also changing with people’s demand for fuel consumption. Catalytic cracking light cycle oil (LCO) hydrogenation conversion technology (react LCO into gasoline, RLG) can produce modified diesel with high-octane gasoline, a high cetane number, and a low sulfur content, which improves the added value of the product. In this article, based on the production and operation data of a 1 million tons/year RLG device, a device yield prediction model was established using a deep neural network (DNN) algorithm, and the model was further optimized using a genetic algorithm (GA) to maximize the economic benefits of the device. As a result, the gasoline production yield increased by more than 3%. The experimental results show that the established model has a good reference value for improving the economic benefits of the RLG device. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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