Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning.
Spectrochim Acta A Mol Biomol Spectrosc
; 277: 121261, 2022 Sep 05.
Article
in En
| MEDLINE
| ID: mdl-35490664
ABSTRACT
The rapid and accurate detection of diesel multiple properties is an important research topic in petrochemical industry that is conducive to diesel quality assessment and environmental pollution mitigation. To that end, this paper developed a new machine learning model for near infrared (NIR) spectroscopy capable of simultaneously determining diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics. The model combined improved XY co-occurrence distance (ISPXY) and differential evolution-gray wolf optimization support vector machine (DEGWO-SVM) to attain the goal of rapidity and accuracy. Experimental results indicated that the average recovery, mean square error, mean absolute percentage error and determination coefficient of the presented method outperformed those of the existing machine learning methods. The proposed hybrid model provides superior solution to the problem of low efficiency and high cost of diesel quality detection, and has the potential to be utilized as a promising tool for diesel routine monitoring.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Spectroscopy, Near-Infrared
/
Data Analysis
Language:
En
Journal:
Spectrochim Acta A Mol Biomol Spectrosc
Journal subject:
BIOLOGIA MOLECULAR
Year:
2022
Document type:
Article
Affiliation country:
China