Monitoring and path optimization of catalytic reformer in a refinery: Principal component analysis and A* algorithm application. (15th December 2022)

• Record Type: Journal Article
• Authors: Li, Zhi; Ying, Yuhui; Yang, Minglei; Zhao, Liang; Zhao, Ling; Du, Wenli
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: Catalytic reformer -- Industrial data -- PCA projection and remapping -- Process monitoring -- Path optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118358
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: PCA projection and remapping methods for process monitoring. A* algorithm was used for process profit optimization. A case study in a refinery is carried out to verify the proposed method. Abstract: Catalytic naphtha reforming converts naphtha into gasoline or aromatics and is a vital petrochemical process in refineries. This paper developed a hybrid framework combining principal component analysis (PCA) and A* algorithm to find optimal operating conditions through process monitoring and path optimization. PCA is employed to develop an efficiency monitoring model using the process history data. The operating conditions with profit information are projected onto a two-dimensional plane using the first and second principal components. High- and low-profit intervals can be clearly distinguished on this map. The A* algorithm is then employed in finding an optimal trajectory from low-index to high-index on the projection map. Finally, the inverse transformation is used to obtain optimum variables for optimal operating conditions. The effectiveness of the proposed framework is verified by applying it to a Sinopec refinery.