期刊
PROCESSES
卷 10, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/pr10122583
关键词
hydrocracking; model predictive control; feedforward control; deep neural network
资金
- European Union
- [723523]
This study proposes a feedforward model predictive control structure for an industrial hydrocracker and compares different models in terms of reactor temperature decisions and diesel product quality predictions. The results highlight the importance of feed character measurements and demonstrate significant improvements in product quality and energy savings.
Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace.
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