.png){kind=icon}
Return
Application of multivariate statistical analysis in batch processes
.png){kind=icon}
PRE
AI
DOI：10.1021/ie000630+.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multivariate statistical analysis methods such as principal component analysis (PCA) and partial least squares (PLS) are powerful tools for chemical process modeling and monitoring. This paper applies PCA/PLS techniques and their variants, multiway PCA/PLS and orthogonal PCA, to an industrial batch process. The study utilizes an existing large historical process data set and combines multivariate statistical methods with batch time optimization calculations to identify possibilities for process improvement. The objective is to increase throughput by shortening the batch reaction time. The batch time optimization calculations provide feasible setpoint operational suggestions while maintaining the underlying data correlation structure. A pseudo-setpoint approach is also proposed to investigate the reaction period during which the setpoint profiles remain constant. Results for an industrial reactor indicate that the batch time can be shortened by approximately 4.3%.
Keywords:
PRINCIPAL COMPONENT ANALYSIS
CHARTS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W
Organization
No organization information available
Cited Papers
ShareSaveCarboxylic-substituted polymer design strategy enabled halogen-free solvent processed efficient organic solar cells.png)
Polymer
PolymerIF0
PREAI
ShareSave
Genomic Profiling of KRAS/NRAS/BRAF/PIK3CA Wild-Type Metastatic Colorectal Cancer Patients Reveals Novel Mutations in Genes Potentially Associated with Resistance to Anti-EGFR AgentsCancers
OAAI
CancersIF0
OAAIShareSave no more
no more