【摘要】：我國(guó)的經(jīng)濟(jì)目前正處于快速發(fā)展的階段中,且近幾年我國(guó)對(duì)于能源的要求和需求也在不斷地提高。2015年,我國(guó)提出要合理有效地利用已發(fā)現(xiàn)和正在開(kāi)發(fā)的新能源,并著重地要求實(shí)施改善能源體系利用結(jié)構(gòu),充分使用可再生能源替代原有石化能源的指導(dǎo)方針。生物質(zhì)能源是可再生能源中重要的一部分,在將來(lái)有可能變成最具有工業(yè)價(jià)值的能源原料之一。如何結(jié)合我國(guó)現(xiàn)有情況,開(kāi)發(fā)并合理地利用生物質(zhì)能源來(lái)緩解能源短缺問(wèn)題,也就變得至關(guān)重要。生物質(zhì)經(jīng)化學(xué)、物理以及生物化學(xué)等手段轉(zhuǎn)化為可利用的生物燃料時(shí),其轉(zhuǎn)化過(guò)程中所產(chǎn)生的經(jīng)濟(jì)效益會(huì)直接受轉(zhuǎn)化產(chǎn)出率所影響。但由于受制于繁瑣的檢驗(yàn)工藝和傳統(tǒng)化學(xué)技術(shù)的限制,無(wú)法滿足實(shí)驗(yàn)或者生產(chǎn)人員對(duì)生物質(zhì)成分配比進(jìn)行快速檢測(cè),因此很多生產(chǎn)過(guò)程都缺少明確配比的指導(dǎo),對(duì)產(chǎn)出率也不是非常明確。因此本文將以此為著手點(diǎn),采用脫豆之后的大豆秸稈作為研究對(duì)象,利用近紅外光譜結(jié)合大豆秸稈中的纖維素和半纖維素含量進(jìn)行圖譜擬合,以求探索快速檢測(cè)實(shí)現(xiàn)的可能性。主要進(jìn)行了如下研究工作:(1)在黑龍江省內(nèi)各個(gè)地區(qū),收集了不同品種的大豆秸稈173株,并對(duì)其所含有的纖維素和半纖維素含量進(jìn)行了定標(biāo)及其原始光譜的采集,通過(guò)對(duì)所采集的樣本使用正態(tài)直方分析統(tǒng)計(jì),以保證所選的樣本都具有一定的代表性。(2)采用導(dǎo)數(shù)和平滑預(yù)處理方法,對(duì)原始光譜做了簡(jiǎn)單的去噪處理,使用了基于X-Y殘差和杠桿值的3D視圖分析法剔除樣本中的異常,經(jīng)過(guò)分析發(fā)現(xiàn),校正模型的精度也有了大幅度的提升。(3)針對(duì)纖維素和半纖維素去噪后的光譜,進(jìn)行篩選最優(yōu)的特征波段,選用35和50間隔的間隔偏最小二乘(IPLS)、評(píng)估次數(shù)為50和100次的遺傳算法(GA)、21至51窗口大小的移動(dòng)窗口最小二乘法(MWPLS)和隨機(jī)噪聲變量100至1000的無(wú)信息變量消除法(UVE)進(jìn)行選擇。并對(duì)所使用的波段特征選取方法的參數(shù)進(jìn)行多種嘗試,然后建立了各特征波段的校正模型。最后得出,各個(gè)選取方法之間驗(yàn)證結(jié)果相差很大,但相對(duì)于全譜的校正模型,結(jié)果提升都很明顯。(4)建立半纖維素和纖維素各自的預(yù)測(cè)模型,建立PLS回歸模型,同時(shí)也利用BP神經(jīng)網(wǎng)絡(luò)的非線性擬合優(yōu)勢(shì)完成BP驗(yàn)證模型的建立,并且對(duì)預(yù)測(cè)結(jié)果進(jìn)行比較。半纖維素和纖維素在兩種預(yù)測(cè)模型下也分別看到了優(yōu)劣,半纖維素預(yù)測(cè)的過(guò)程中,我們可以看到PLSR的預(yù)測(cè)能力遠(yuǎn)遠(yuǎn)的強(qiáng)于BP神經(jīng)網(wǎng)絡(luò),而纖維素的預(yù)測(cè),結(jié)果卻是截然相反。綜上所述,本文研究的近紅外模型對(duì)大豆秸稈中纖維素和半纖維素的快速檢測(cè),具有一定的可行性,也解決了過(guò).去檢測(cè)方法中遇到的一些實(shí)質(zhì)性問(wèn)題,以期運(yùn)用該技術(shù)的日益發(fā)展和完善為日后生物燃料生產(chǎn)過(guò)程中的快速檢測(cè)提供新方法。
[Abstract]:Our country's economy is at the stage of rapid development. In recent years, our country's demand and demand for energy have also been increasing in.2015 years. Our country proposes to use the new energy that has been found and being developed effectively and effectively, and emphasizes the improvement of the structure of the energy system and the full use of renewable energy instead of the original. There is a guideline for petrochemical energy. Biomass energy is an important part of renewable energy and may become one of the most valuable energy sources in the future. It is also important to develop and rationally utilize biomass energy to alleviate the problem of energy shortage in the future. The economic benefits produced in the transformation process will be directly affected by the conversion output rate when the means of Biochemistry and biochemistry are converted into available biofuels. However, due to the restriction of tedious test technology and traditional chemical technology, it is impossible to meet the rapid detection of the ratio of biomass components by the experiment or the producer. Many of the production processes are lack of clear ratio guidance, and the output rate is not very clear. Therefore, this paper will take this as the starting point, using soybean straw after pea as the research object, using near infrared spectroscopy combined with the content of cellulose and hemicellulose in soybean straw to carry out atlas fitting, in order to explore the rapid detection and realization. The main research work is as follows: (1) 173 strains of different varieties of soybean straw were collected in various regions of Heilongjiang Province, and the content of cellulose and hemicellulose contained in the samples was calibrated and the original spectrum was collected. The samples were analyzed by direct normal analysis and statistics to ensure the selected samples. All of them have certain representativeness. (2) using the derivative and smoothing preprocessing method, the original spectrum is simply de-noised, and the 3D view analysis method based on X-Y residual and lever value is used to eliminate the anomaly in the sample. After analysis, the accuracy of the correction model has also been greatly improved. (3) De-noising for cellulose and hemicellulose. After the spectrum, the optimal feature band is selected, 35 and 50 spaced interval partial least squares (IPLS), the 50 and 100 times genetic algorithm (GA), the 21 to 51 window size moving window least squares (MWPLS) and the random noise variable 100 to 1000 free variable elimination method (UVE) are selected. The parameters of the feature selection method are tried, and then the correction model of each feature band is established. Finally, the results of each selection method are very different, but the results are very obvious compared with the full spectrum correction model. (4) to establish the prediction model of hemicellulose and fibrin and to establish PLS regression model, and at the same time Using the nonlinear fitting advantage of BP neural network, the BP validation model is established and the prediction results are compared. Hemicellulose and cellulose are also seen under the two prediction models. In the process of hemicellulose prediction, we can see that the prediction ability of PLSR is much stronger than that of the BP neural network, and the cellulose is predisposed. In summary, the near-infrared model studied in this paper has a certain feasibility for rapid detection of cellulose and hemicellulose in soybean straw. It also solved some substantive problems encountered in the detection method, in order to use the technology to develop and improve the production process of biofuel in the future. A new method of rapid detection is provided.
【學(xué)位授予單位】：東北農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：S216.2
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本文編號(hào)：2135611