近红外检测模型在乙醇生产中的开发及应用
发布时间：2019-03-22 12:39
【摘要】：由于天冠燃料乙醇有限公司生产工艺的独特性,以及原料的复杂性,公司所使用的部分原料和生产的产品在市场上不通用,仪器生产厂家没有开发相关的产品模型,特别是在木薯和谷朊粉产品等方面也没有近红外模型开发的报道。乙醇公司亟需一种方便、快捷的质量检测方法来指导生产。本文采用化学法测定木薯、谷朊粉、DDGS酒糟饲料样品的化学成分,然后利用DA7200近红外测定仪采集样品近红外光谱,通过偏最小二乘法(PLS)、多元散射校正(MSC)和导数处理等光谱预处理方式建立了木薯、谷朊粉、DDGS酒糟饲料的快速检测模型,并经验证模型均符合标准要求,完全可以应用于木薯、谷朊粉、DDGS酒糟饲料的日常检测。经过乙醇公司长期实际生产运行验证,该方法快速、准确、清洁、高效、无损和低成本,符合要求乙醇长期连续生产对原料、中间产品,产成品检测的要求,可以有效地指导生产调整工作,为乙醇生产的质量控制带来有效的保障。
[Abstract]:Due to the uniqueness of the production process and the complexity of the raw materials, some of the raw materials used by the company and the products produced by the company are not commonly used in the market, and the instrument manufacturers have not developed the relevant product models. Especially in cassava and gluten products, there is no near infrared model development report. Ethanol companies need a convenient, fast quality inspection method to guide production. In this paper, the chemical composition of cassava, gluten and DDGS distillery grains was determined by chemical method. Then the near infrared spectra of the samples were collected by DA7200 near infrared detector, and the partial least square method (PLS),) was used to determine the chemical composition of the samples. The rapid detection models of cassava, gluten and DDGS distillery grains were established by spectral pretreatment with multiple scattering correction (MSC) and derivative treatment. The model was verified to meet the standard requirements and could be applied to cassava and gluten meal, and the model could be used in cassava and gluten meal, and could be used in cassava and gluten meal. Routine detection of DDGS distiller's grains feed. This method is fast, accurate, clean, efficient, nondestructive and low cost, and meets the requirements of long-term continuous production of ethanol for raw materials, intermediate products and finished products, which has been verified by the long-term practical operation of ethanol company. It can effectively guide the production adjustment and bring effective guarantee for the quality control of ethanol production.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TQ223.122;O657.33
本文编号：2445595
[Abstract]:Due to the uniqueness of the production process and the complexity of the raw materials, some of the raw materials used by the company and the products produced by the company are not commonly used in the market, and the instrument manufacturers have not developed the relevant product models. Especially in cassava and gluten products, there is no near infrared model development report. Ethanol companies need a convenient, fast quality inspection method to guide production. In this paper, the chemical composition of cassava, gluten and DDGS distillery grains was determined by chemical method. Then the near infrared spectra of the samples were collected by DA7200 near infrared detector, and the partial least square method (PLS),) was used to determine the chemical composition of the samples. The rapid detection models of cassava, gluten and DDGS distillery grains were established by spectral pretreatment with multiple scattering correction (MSC) and derivative treatment. The model was verified to meet the standard requirements and could be applied to cassava and gluten meal, and the model could be used in cassava and gluten meal, and could be used in cassava and gluten meal. Routine detection of DDGS distiller's grains feed. This method is fast, accurate, clean, efficient, nondestructive and low cost, and meets the requirements of long-term continuous production of ethanol for raw materials, intermediate products and finished products, which has been verified by the long-term practical operation of ethanol company. It can effectively guide the production adjustment and bring effective guarantee for the quality control of ethanol production.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TQ223.122;O657.33
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