LAPSE:2023.28212
Published Article
LAPSE:2023.28212
Modeling and Optimization of the Para-Xylene Continuous Suspension Crystallization Separation Process via a Morphology Technique and a Multi-Dimensional Population Balance Equation
Zhenxing Cai, Jixiang Liu, Hui Zhao, Xiaobo Chen, Chaohe Yang
April 11, 2023
In this study, we carried out a para-xylene crystallization experiment at constant temperature and concentration levels. Throughout the process, the kinetics of nucleation, growth, breakage, and aggregation of para-xylene particles were measured and built using a morphological approach. An additional a three-stage continuous suspension crystallization separation experiment was carried out, the process for which was simulated using the population balance model based on correlated kinetic equations. The population balance equation was solved using an extended moment of classes algorithm, and the solving process was implemented in MATLAB. In this case, the predicted particle size distribution of the products matched well with the experiment. In order to provide references for the optimization of the industrial para-xylene crystallization process, a three-stage suspension crystallization separation experiment was designed and conducted, in which each crystallizer had a distinct operating temperature and mean residence time. The effects of operating parameters on the final product were investigated further. The proposed models and algorithms can also be applied in other cases and provide an alternative approach for optimizing continuous crystallization processes.
Keywords
crystallization kinetics, morphology, para-xylene, population balance, process optimization
Suggested Citation
Cai Z, Liu J, Zhao H, Chen X, Yang C. Modeling and Optimization of the Para-Xylene Continuous Suspension Crystallization Separation Process via a Morphology Technique and a Multi-Dimensional Population Balance Equation. (2023). LAPSE:2023.28212
Author Affiliations
Cai Z: State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Qingdao 266580, China
Liu J: State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Qingdao 266580, China [ORCID]
Zhao H: State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Qingdao 266580, China
Chen X: State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Qingdao 266580, China
Yang C: State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Qingdao 266580, China
Journal Name
Processes
Volume
11
Issue
3
First Page
770
Year
2023
Publication Date
2023-03-05
Published Version
ISSN
2227-9717
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PII: pr11030770, Publication Type: Journal Article
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LAPSE:2023.28212
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doi:10.3390/pr11030770
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