【摘要】：煤炭中的硫會(huì)對(duì)用煤企業(yè)的生產(chǎn)設(shè)備造成破壞,影響生產(chǎn)效率。此外,煤炭燃燒生成的硫氧化物會(huì)污染環(huán)境,危害人體健康,破壞生態(tài)系統(tǒng)。為了有效解決這些問(wèn)題,需要測(cè)定煤的硫含量。目前常用的測(cè)定方法都需要從煤場(chǎng)取樣然后拿到實(shí)驗(yàn)室進(jìn)行分析測(cè)定,耗時(shí)較長(zhǎng),分析結(jié)果存在一定的滯后性。近紅外光譜分析技術(shù)是一種快速、在線分析技術(shù),可以在極短的時(shí)間內(nèi)測(cè)定煤的硫含量,對(duì)及時(shí)優(yōu)化使用條件,實(shí)現(xiàn)煤炭的充分燃燒具有重要意義。本論文介紹了近紅外光譜分析技術(shù)的原理、特點(diǎn)、算法和評(píng)價(jià)指標(biāo),研究了利用近紅外光譜分析技術(shù)檢測(cè)煤中硫含量的快速無(wú)損檢測(cè)方法。在嚴(yán)格控制實(shí)驗(yàn)環(huán)境的條件下,采集了221個(gè)煤樣的近紅外漫反射光譜,并測(cè)定了每個(gè)煤樣的硫含量。這221個(gè)煤樣包含特低硫煤、低中硫煤和中硫煤三種煤,從中選取176個(gè)作為校正集,用于建立硫分的回歸模型,剩下的45個(gè)煤樣作為驗(yàn)證集,用于檢測(cè)模型的預(yù)測(cè)能力和回歸效果。本研究首先選用偏最小二乘回歸法建立硫分的回歸模型,取得了較好的預(yù)測(cè)效果。然后運(yùn)用不同異常點(diǎn)剔除方法優(yōu)化該模型,模型結(jié)果顯示馬氏距離優(yōu)化效果最佳。在此基礎(chǔ)上考察不同光譜預(yù)處理方法對(duì)模型的優(yōu)化程度,結(jié)果顯示標(biāo)準(zhǔn)化方法最適用于該模型。最后在兩次優(yōu)化之后,比較不同波段篩選方法對(duì)模型的影響,結(jié)果顯示相關(guān)系數(shù)法對(duì)模型有一定的優(yōu)化效果。利用偏最小二乘回歸法結(jié)合這三種最佳方法對(duì)煤樣的硫分進(jìn)行回歸建模,得到了回歸效果好、預(yù)測(cè)能力強(qiáng)的回歸模型。
[Abstract]:Sulfur in coal will destroy the production equipment of coal enterprises and affect the production efficiency. In addition, sulfur oxides from coal combustion will pollute the environment, endanger human health and destroy ecosystems. In order to solve these problems effectively, it is necessary to determine the sulfur content of coal. At present, the commonly used determination methods need to be sampled from the coal site and taken to the laboratory for analysis and determination, which takes a long time and there is a certain lag in the analysis results. Near infrared spectroscopy (NIR) is a rapid and on-line analysis technology, which can be used to determine the sulfur content of coal in a very short time. It is of great significance to optimize the operating conditions in time and realize the full combustion of coal. In this paper, the principle, characteristics, algorithm and evaluation index of near infrared spectroscopy are introduced, and the rapid nondestructive testing method of sulfur content in coal by near infrared spectroscopy is studied. Under the condition of strict control of the experimental environment, 221 coal samples were collected and the sulfur content of each coal sample was determined. The 221 coal samples include ultra-low sulfur coal, low medium sulfur coal and medium sulfur coal. 176 of them are selected as correction sets to establish the regression model of sulfur content, and the remaining 45 coal samples are used as verification sets to detect the prediction ability and regression effect of the model. In this study, the partial least square regression method was used to establish the regression model of sulfur content, and good prediction results were obtained. Then the model is optimized by different outliers elimination methods, and the results show that the Mahalanobis distance optimization effect is the best. On this basis, the optimization degree of different spectral preprocessing methods to the model is investigated, and the results show that the standardized method is the most suitable for the model. Finally, after two optimizations, the effects of different band selection methods on the model are compared, and the results show that the correlation coefficient method has a certain optimization effect on the model. The partial least square regression method combined with these three best methods is used to model the sulfur content of coal samples, and a regression model with good regression effect and strong prediction ability is obtained.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O657.33;TQ533

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 員文娥;;煤中全水分及灰分的近紅外測(cè)試方法研究[J];潔凈煤技術(shù);2016年03期

2 金澤華;胡瑞生;龔雪;胡佳楠;杜娟;李應(yīng)彤;李強(qiáng);;現(xiàn)代煤化工能源消耗限額標(biāo)準(zhǔn)體系分析[J];潔凈煤技術(shù);2016年02期

3 雷萌;陳凡;吳楠;徐志彬;李翠;;煤質(zhì)近紅外光譜分析系統(tǒng)設(shè)計(jì)[J];煤炭技術(shù);2016年02期

4 李玄懷;;煤中硫含量的近紅外光譜快速測(cè)定方法研究[J];潔凈煤技術(shù);2015年06期

5 牛嬋娟;王曉燕;;煤中硫的形態(tài)及全硫含量的測(cè)定[J];山西化工;2015年05期

6 楊陽(yáng);劉繼亮;;庫(kù)侖法測(cè)定煤中全硫的常見(jiàn)問(wèn)題探討[J];神華科技;2015年04期

7 馬公U，

本文編號(hào)：2497773