LIBS结合鲸鱼优化算法对石油焦中微量元素的快速定量分析研究

doi:10.3964/j.issn.1000-0593(2025)10-2796-08

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Abstract Petroleum coke is a carbonaceous solid product formed by the pyrolysis of heavy hydrocarbons in crude oil. Due to its high carbon content (80%~95%), high calorific value, and excellent electrical conductivity, it is widely used in electrolytic aluminum anode materials, metallurgical fuels, graphite electrodes, and lithium-ion battery anode materials. It has important application value in industrial production. In petroleum coke, transition metal elements such as Fe and Cu not only reduce the conductivity and thermal stability of petroleum coke, but also may cause side reactions of electrode materials, resulting in battery capacity attenuation.In this paper, a hybrid modeling method based on laser-induced breakdown spectroscopy (LIBS) technology, combined with the whale optimization algorithm, is proposed for the rapid quantitative analysis of Fe and Cu elements in petroleum coke. An efficient analysis model was constructed by integrating the high-throughput and non-destructive detection advantages of LIBS, the intelligent feature selection ability of whale optimization algorithm (WOA), and the high-dimensional data processing characteristics of partial least squares (PLS) regression.Firstly, by collecting LIBS spectral data of 19 groups of petroleum coke samples, the preprocessing strategy of spectral data was systematically investigated. It includes the combination optimization of normalization (Nor), multiple scattering correction (MSC), standard normal variate (SNV), first derivative (D1st), second derivative (D2nd), and wavelet transform (WT). Screening the most effective pretreatment combination for quantitative analysis of Fe and Cu elements.Through experimental verification, the SNV-D2nd-WT combined pretreatment significantly improved the model's prediction ability (cross-validation determination coefficient R²_CV=0.946 4, root mean square error RMSE_CV=12.95 mg·kg^-1; R²_CV=0.914 6, RMSE_CV=8.84 mg·kg^-1). The WOA algorithm was further combined to optimize the feature selection and model parameters (number of whales, number of iterations, threshold). After parameter optimization, the characteristic wavelengths of Fe and Cu elements were reduced from 5 784 to 88 and 23, respectively. The prediction accuracy of the final model was further improved: Fe element prediction setR²_P=0.947 0, RMSE_P=7.35 mg·kg^-1; Cu element R²_P=0.953 8, RMSE_P=6.31 mg·kg^-1. This method offers a rapid and efficient solution for detecting trace metals in petroleum coke.

Key words：Laser-induced breakdown spectroscopy; Chemometrics; Whale optimization algorithm; Petroleum coke; Trace element

Received: 2025-02-21 Accepted: 2025-05-08

ZTFLH:

O657.319

Corresponding Authors: LI Mao-gang, LI Hua E-mail: lmglmg1995@163.com; huali@nwu.edu.cn

Cite this article:

ZHANG Meng-fan,LI Mao-gang,LIU Yi-jiang, et al. Rapid Quantitative Analysis of Trace Elements in Petroleum Coke by LIBS Combined With Whale Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2796-2803.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)10-2796-08 OR https://www.gpxygpfx.com/EN/Y2025/V45/I10/2796

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