Partial least squares regression residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy. (1st February 2022)

• Record Type: Journal Article
• Authors: Wang, Haipeng; Chu, Xiaoli; Chen, Pu; Li, Jingyan; Liu, Dan; Xu, Yupeng
• Is Part Of: Fuel. Volume 309(2022)
• Publication Date: 2022-02-01
• Subjects: Near infrared spectroscopy -- Partial least squares regression -- Regression residuals -- Extreme learning machine -- Research octane number -- Gasoline blending; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2021.122224
• Languages: English
• ISSNs: 0016-2361

Abstract

Graphical abstract: Highlights: PLS was used in conjunction with ELM for fast determination of blended gasoline octane number with NIR spectroscopy. Allocation of relationship information (NIR spectrum and property) in between PLS and ELM can be adjusted adaptively. The proposed method exhibited better prediction accuracy over PLS or ELM alone. The proposed method can be well-suited for calibrating an analysis system with unknown degree of no-linearity. Abstract: Based on near-infrared (NIR) spectroscopy, a new quantitative calibration algorithm, called "Partial Least Squares Regression Residual Extreme Learning Machine (PLSRR-ELM)", was proposed for fast determination of research octane number (RON) for blended gasoline. In this algorithm, partial least square (PLS) cooperates with non-linear extreme learning machine (ELM) to separate the relationship information suitable for each other from the raw relationship information (between NIR spectrum and corresponding property) with the unknown degree of non-linearity, with aim of calibrating them respectively. Since the advantages of both PLS and ELM are fully utilized, it is expected that PLSRR-ELM can address the relationship information more effectively and leads to improved calibration performance over PLS and ELM alone. The calibration performance of PLSRR-ELM was evaluated by a set of on-line gasoline blending sample data from a refinery. As a result, it showed an enhanced prediction performance, e.g., about 13% or 11% decrease in the root mean squared error of test (RMSE-T) over PLS or ELM alone, respectively. In method comparison, the model performance of PLRR-ELM exceeds all other methods including PLS, Poly-PLS, KPLS, ELM, and ANN, demonstrating its superiority for fast prediction of gasoline RON.