可见-近红外光谱联合随机蛙跳算法检测生物柴油含水量

生物柴油是一种优质清洁柴油,可从各种生物质中提炼,其特有的优势受到越来越广泛的关注。该文应用可见-近红外光谱技术原理对生物柴油的含水率进行了检测。配置含水率分别为0、2.5%、5.0%、7.5%和10.0%的试验样品并获取可见-近红外光谱,进行主成分分析,观察不同含水率生物柴油的聚类性,并采用Random Frog算法进行特征波段的提取,最后采用随机蛙跳算法(Random Frog)挑选出的特征波段作为偏最小二乘回归(partial least squares regression,PLSR)和最小二乘支持向量机(least squares-support vector machine,LS-SVM)模型的输入量,建立生物柴油含水量的预测模型。结果发现:采用Random Frog提取出的8条特征波段(563、560、642、565、562、493、559和779 nm)所建立非线性模型LS-SVM所得到的结果较好,其中Random Frog-LS-SVM的结果中R均大于0.95,校正集均方根误差RMSEC=0.722,预测集均方根误差RMSEP=0.520。结果表明采用Random Frog-LS-SVM模型可以准确的预测生物柴油的含水量,为实际应用提供参考。

Measurement of water content in biodiesel using visible and near infrared spectroscopy combined with Random-Frog algorithm

Biodiesel (fatty acid methyl or ethyl esters) is made from vegetable oil or animal fat (triglycerides) reacting with methanol or ethanol using a catalyst (lye). It is safe, biodegradable, and produces less air pollutants than petroleum-based diesel or recycled restaurant greases. With the increasing demand of green energy source and the decreasing of fossil fuel, biodiesel has gained increasing attention as one of the alternative fuels. 100% biodiesel (B100) was used in this study. Experimental samples with water content of 0, 2.50%, 5.00%, 7.50% and 10.0% were set. There were 35 samples for every treatment with different water contents, and total 175 samples. 116 samples were selected for calibration set, and 58 samples for prediction set based with Kennard-Stone (K-S) method. Visible and near infrared spectra (Vis-NIR) technique which was a nondestructive and rapid method, was used to measure the water content in biodiesel. Samples were scanned using the ADS Handheld FieldSpec spectrometer and spectra of samples were acquired. Principal component analysis (PCA) was used to compress spectral data and observe the cluster's situation of biodiesel with different water contents. The scores plot showed a good cluster distribution and the total accumulated variance of PC-1 and PC-2 was up to 99.3%. Random Frog algorithm was applied to extract spectral feature. Then, 8 sensitive wavelengths (563, 560, 642, 565, 562, 493, 559 and 779 nm) were selected respectively. Spectral feature and different water contents were set as input values of partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models. It was showed that LS-SVM and PLSR with full spectra had good results, while the variables were too much (116×591) compared with the regression models (116×8). Results of the Random Frog-LS-SVM were better than the Random Frog-PLSR. R of the non-linear LS-SVM models with spectral feature extracted by Random Frog was higher than 0.965, RMSEC of 0.722, RMSEP of 0.520. Sensitive wavelengths extracted were good for eliminating the interfering spectral and improving the accuracy of the model. Results indicated that the Random Frog-LS-SVM as a satisfactory model can measure the water content in biodiesel accurately, which could provide a reference for practical application.