CAJ | 학술논문

为了探讨利用生物质秸秆工业分析指标预测生物质秸秆热值的可行性,建立高、低位热值的预测模型,采集了油菜、小麦、玉米和水稻4种不同作物秸秆总计172个样品,这4种作物秸秆的数量分别为31、36、86和19,按照美国材料与试验协会(ASTM)的标准方法分别测定样本的水分、挥发分、灰分和固定碳等工业分析指标,采用IKA C2000型量热仪测定热值.通过数据统计分析,挥发分和固定碳对热值有极显著的正相关性,而灰分对热值有极显著的负相关,并且水分、挥发分、灰分和固定碳等4项指标之间存在严重的共线性.利用主成分回归方法建立高、低位热值预测模型效果最优,高位热值预测模型的决定系数 R2为0.91,预测值标准差 SEP 为0.20 kJ/g,相对标准差RSD为1.25%;低位热值预测模型的决定系数R2为0.91,预测值标准差SEP为0.20 kJ/g,相对标准差RSD为1.33%.并用20个样品对预测模型进行了外部验证,高位热值和低位热值预测值标准差SEP分别为0.18 kJ/g和0.19 kJ/g,相对标准差RSD分别为1.09%和1.29%,取得较理想的预测结果.试验结果表明,主成分回归方法建立的基于工业分析指标的生物质秸秆热值预测模型可以较准确地预测生物质秸秆热值,利用生物质秸秆工业分析指标预测生物质秸秆热值是可行的,该研究可为生物质秸秆能源化利用提供参考.
위료탐토이용생물질갈간공업분석지표예측생물질갈간열치적가행성,건립고、저위열치적예측모형,채집료유채、소맥、옥미화수도4충불동작물갈간총계172개양품,저4충작물갈간적수량분별위31、36、86화19,안조미국재료여시험협회(ASTM)적표준방법분별측정양본적수분、휘발분、회분화고정탄등공업분석지표,채용IKA C2000형량열의측정열치.통과수거통계분석,휘발분화고정탄대열치유겁현저적정상관성,이회분대열치유겁현저적부상관,병차수분、휘발분、회분화고정탄등4항지표지간존재엄중적공선성.이용주성분회귀방법건립고、저위열치예측모형효과최우,고위열치예측모형적결정계수 R2위0.91,예측치표준차 SEP 위0.20 kJ/g,상대표준차RSD위1.25%;저위열치예측모형적결정계수R2위0.91,예측치표준차SEP위0.20 kJ/g,상대표준차RSD위1.33%.병용20개양품대예측모형진행료외부험증,고위열치화저위열치예측치표준차SEP분별위0.18 kJ/g화0.19 kJ/g,상대표준차RSD분별위1.09%화1.29%,취득교이상적예측결과.시험결과표명,주성분회귀방법건립적기우공업분석지표적생물질갈간열치예측모형가이교준학지예측생물질갈간열치,이용생물질갈간공업분석지표예측생물질갈간열치시가행적,해연구가위생물질갈간능원화이용제공삼고.
To build a prediction model of gross calorific value and net calorific value, this article discusses the influence of industrial analysis indexes of straw biomass on the calorific value and the feasibility of predicting calorific value. 172 straw samples has been collected, including 31 rape straws, 36 wheat straws, 86 rice straws, and 19 maize straws. Moisture, volatile matter, ash, fixed carbon, gross calorific value, and net calorific value were measured by standard methods. The statistics of measured values showed that the ranges for the above six indexes were 2.72%-8.04%、63.79%-76.25%、3.57%-16.97%、11.94%-17.03%、14.88-17.58 kJ/g, and 13.37-16.13 kJ/g respectively, and the means were 5.61%、69.53%、10.28%、14.58%、16.20 kJ/g、and 14.74 kJ/g respectively. The ash of rice straw is higher than that of rape straw、wheat straw and maize straw,and the calorific value was lower. According to a simple correlation analysis, we found that volatiles and fixed carbon have a very significant positive correlation to calorific value. Accordingly, a very significant negative correlation was achieved for ash with calorific value. Simultaneously, there is a high degree of correlation between volatile matter or ash and caloric value, but it a lower degree of correlation to fixed carbon;there is an important collinearity between the moisture、volatiles、ash, and fixed carbon, the influence of which must be reduced or eliminated. Among different approaches to establishing and comparing prediction models, the results indicated that principal component regression is the best method, because it (a) effectively eliminated the impact of collinearity between the industrial analysis indexes, (b) preserved the integrity of the information about industrial analysis indexes, and (c) attained the greatest accuracy of the final prediction model.. Using principal component regression to establish a prediction model of gross calorific value and net calorific value, the determination coefficient of the prediction model of gross calorific value was 0.91, the predicted standard deviation was 0.20 kJ/g, and the relative standard deviation was 1.25%. The determination coefficient of the prediction model of net calorific value was 0.91, the predicted standard deviation was 0.20 kJ/g, and the relative standard deviation was 1.33%. In the 20 samples used for the external validation, the predicted standard deviation of the gross calorific value was 0.18 kJ/g, and the relative standard deviation was 1.09%;the predicted standard deviation of the net calorific value was 0.19 kJ/g, and the relative standard deviation was 1.29%. The prediction result is obtained ideally. We concluded that a calorific value model of straw biomass based on industrial analysis indexes predicts the gross and net calorific values accurately, and that industrial analysis indexes of straw biomass can help in predicting the calorific value of straw biomass. Consequently, this study can provide a reference method for use in biomass straw energy utilization.