农业工程学报
農業工程學報
농업공정학보
2014年
8期
168-173
,共6页
生物柴油%含水量%近红外光谱%主成分分析%Random Frog%偏最小二乘回归%最小二乘支持向量机
生物柴油%含水量%近紅外光譜%主成分分析%Random Frog%偏最小二乘迴歸%最小二乘支持嚮量機
생물시유%함수량%근홍외광보%주성분분석%Random Frog%편최소이승회귀%최소이승지지향량궤
biodiesel%water content%near infrared spectroscopy%principal component analysis%random frog%partial least squares regression%least squares-support vector machine
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。
生物柴油是一種優質清潔柴油，可從各種生物質中提煉，其特有的優勢受到越來越廣汎的關註。該文應用可見-近紅外光譜技術原理對生物柴油的含水率進行瞭檢測。配置含水率分彆為0、2.5%、5.0%、7.5%和10.0%的試驗樣品併穫取可見-近紅外光譜，進行主成分分析，觀察不同含水率生物柴油的聚類性，併採用Random Frog算法進行特徵波段的提取，最後採用隨機蛙跳算法（Random Frog）挑選齣的特徵波段作為偏最小二乘迴歸（partial least squares regression，PLSR）和最小二乘支持嚮量機（least squares-support vector machine，LS-SVM）模型的輸入量，建立生物柴油含水量的預測模型。結果髮現：採用Random Frog 提取齣的8條特徵波段（563、560、642、565、562、493、559和779 nm）所建立非線性模型LS-SVM 所得到的結果較好，其中Random Frog-LS-SVM的結果中R均大于0.95，校正集均方根誤差RMSEC=0.722，預測集均方根誤差RMSEP=0.520。結果錶明採用Random Frog-LS-SVM模型可以準確的預測生物柴油的含水量，為實際應用提供參攷。
생물시유시일충우질청길시유，가종각충생물질중제련，기특유적우세수도월래월엄범적관주。해문응용가견-근홍외광보기술원리대생물시유적함수솔진행료검측。배치함수솔분별위0、2.5%、5.0%、7.5%화10.0%적시험양품병획취가견-근홍외광보，진행주성분분석，관찰불동함수솔생물시유적취류성，병채용Random Frog산법진행특정파단적제취，최후채용수궤와도산법（Random Frog）도선출적특정파단작위편최소이승회귀（partial least squares regression，PLSR）화최소이승지지향량궤（least squares-support vector machine，LS-SVM）모형적수입량，건립생물시유함수량적예측모형。결과발현：채용Random Frog 제취출적8조특정파단（563、560、642、565、562、493、559화779 nm）소건립비선성모형LS-SVM 소득도적결과교호，기중Random Frog-LS-SVM적결과중R균대우0.95，교정집균방근오차RMSEC=0.722，예측집균방근오차RMSEP=0.520。결과표명채용Random Frog-LS-SVM모형가이준학적예측생물시유적함수량，위실제응용제공삼고。
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.
