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Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning.
Liu, Shiyu; Wang, Shutao; Hu, Chunhai; Zhan, Shujie; Kong, Deming; Wang, Junzhu.
Afiliación
  • Liu S; Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Wang S; Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China. Electronic address: ssyy0204@stumail.ysu.edu.cn.
  • Hu C; Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Zhan S; Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Kong D; Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Wang J; Flow Measurement Technology Key Lab of Zhejiang Province, College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China.
Spectrochim Acta A Mol Biomol Spectrosc ; 277: 121261, 2022 Sep 05.
Article en En | MEDLINE | ID: mdl-35490664
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectroscopía Infrarroja Corta / Análisis de Datos Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectroscopía Infrarroja Corta / Análisis de Datos Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido