Development of a new NIR-machine learning approach for simultaneous detection of diesel various properties

The computer aided detection of diesel multiple properties is an active field of energy and chemical research as a result of the need for quality control and brands management of diesel raw materials. Based on this premise, this paper aimed to detect the diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics using near infrared spectroscopy (NIRS) data combined with improved XY co-occurrence distance (ISPXY) and improved grey wolf optimized support vector regression (IGWO-SVR). The outcomes of average recovery, mean square error, mean absolute percentage error and determination coefficient of the proposed model are all better than other machine learning models. Further, this method is green, simple, effective, rapid, and can be embedded in the industrial network as a unit, which provides intelligent guidance for refineries to accurately control the quality of diesel oil.

Automated summary

The paper successfully detects the multiple properties of diesel using NIRS data combined with improved ISPXY and IGWO-SVR methods, with better performance than other machine learning models, providing a green and effective solution.