Data on propylene/propane separation by the externally heat-integrated distillation column (EHIDiC) using data-driven analysis
Peng Qiu,
Bo Huang,
Zhenghua Dai,
Fuchen Wang
Affiliations
Peng Qiu
Key Laboratory of Coal Gasification of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China; Shanghai Engineering Research Center of Coal Gasification, East China University of Science and Technology, P.O. Box 272, Shanghai 200237, PR China
Bo Huang
Key Laboratory of Coal Gasification of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China; Shanghai Engineering Research Center of Coal Gasification, East China University of Science and Technology, P.O. Box 272, Shanghai 200237, PR China
Zhenghua Dai
Key Laboratory of Coal Gasification of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China; Shanghai Engineering Research Center of Coal Gasification, East China University of Science and Technology, P.O. Box 272, Shanghai 200237, PR China; Corresponding author at: Key Laboratory of Coal Gasification of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China.
Fuchen Wang
Key Laboratory of Coal Gasification of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China; Shanghai Engineering Research Center of Coal Gasification, East China University of Science and Technology, P.O. Box 272, Shanghai 200237, PR China
Nine hundred data were collected from rigorously simulated EHIDiC for propylene/propane separation using the Aspen Plus-Matlab communication platform. Statistical analysis was performed to give a further understanding of EHIDiC, a highly coupled system. The input variables, integrated stages, and the corresponding heat of two couples of external exchangers were generated stochastically in a wide range to cover the completely reasonable operation window. The results of blocks and streams in the flowsheet were calculated as the output data, including the flow rate, the temperature, and the pressure, as well as Total Annualized Cost (TAC). The data can be reused to design and optimize both the steady and dynamic schemes of EHIDiC, especially with machine learning methods. This manuscript gave a full description of collecting and analyzing of the data used in the research article “Data-driven analysis and optimization of externally heat-integrated distillation columns (EHIDiC)” [1].
