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AHP comprehensive evaluation of grain drying quality Abstract: the analytic hierarchy process is used to evaluate grain drying, and a comprehensive quantitative and qualitative hierarchical evaluation model and overall evaluation method are proposed. Firstly, the analytic hierarchy process (AHP) structure model and judgment matrix for optimal selection of grain drying quality are established, then the hierarchical ranking is carried out, and the consistency of the judgment matrix is tested, and the evaluation method is formed on the basis of the conversion of qualitative and quantitative indicators. The production test shows that this method is simple and feasible, and the conclusion is in line with reality

key words: analytic hierarchy process model; Vacuum drying; Steam drying; Hot air drying; Process optimization; The comprehensive evaluation system

solves the problem of grain drying, keeps the original color, aroma, taste, shape and nutritional components as much as possible, eliminates the problems of solute dispersion, surface hardening and quality decline in the traditional drying process, and achieves the three control goals of quality, energy conservation and environmental protection. Therefore, optimizing the drying process is always an important research topic. In view of the current situation of grain drying in China, it is of great significance to choose a reasonable drying process, improve the drying quality of grain and reduce the loss of grain drying through the comprehensive evaluation of 300t/d corn production test with three alternative schemes of vacuum low-temperature drying, steam drying and hot air drying

analytic hierarchy process is a powerful tool to analyze complex decision-making problems such as multi-objective and multi criteria. It has the characteristics of clear thinking, simple method, wide scope of application, strong systematization and easy promotion. In this paper, the grain drying quality is comprehensively analyzed by AHP method, in order to provide some basis for the selection of drying technology

The analytical hierarchy price (hereinafter referred to as AHP) was proposed by American operations research scientist and Professor ATY of the University of Pittsburgh in the 1970s. He first used AHP in the study of "emergency planning" for the U.S. Department of defense in 1971, and published the article "modeling of unstructured decision-making problems - analytic hierarchy process" at the international mathematical modeling conference in 1977, Since then, AHP has been applied in many fields of decision-making. At the same time, the theory that AHP will promote the rapid release of production capacity after the new fiber composite Changzhou plant is put into operation has also been deepened and developed. AHP was introduced into China in 1982the basic idea of analytic hierarchy process is to transform the overall judgment of the weights of multiple elements that make up complex problems into "pairwise comparison" of these elements, and then turn to the ranking judgment of the overall weights of these elements, and finally establish the weights of each element. The grain drying quality evaluation index system is a multi-level and multi index composite system. In this composite system, the relative importance of each level and index is different, which is difficult to determine scientifically. The commonly used empirical valuation method, expert determination method and other methods are difficult to work or even helpless. By constructing a judgment matrix, the analytic hierarchy process first clarifies the factors contained in the problem and their relationships, hierarchically serializes the problem to be solved, decomposes the problem into different constituent factors according to the nature of the problem and the goal to be achieved, and combines them hierarchically according to the interaction and subordination between the factors to form a hierarchical, orderly and hierarchical structure model. Secondly, the relative importance of each level of factors in the model is quantitatively expressed according to people's judgment of objective reality, and then the weight of the relative importance order of all factors at each level is determined by mathematical methods. Finally, by comprehensively calculating the weights of the relative importance of factors at each level, the combined weights of the relative importance order of the lowest level relative to the highest level are obtained, which can be used as the basis for evaluation and selection of schemes. The use of analytic hierarchy process can not only reduce the difficulty of work and improve the accuracy and scientificity of index weights, but also improve the reliability and validity of weight determination by taking measures such as consistency test of judgment matrix. At the same time, when calculating matrix eigenvectors, sum product method, power method, square root method and other ideas can be used, and computers can be used to process data, which has strong operability, It can achieve more satisfactory decision-making results. (3) Methods for ultrasonic flaw detection and quality rating of steel castings gb/t7233 (87)

Saaty et al. Suggested that the method of pairwise comparison of factors can be adopted to establish a pairwise comparison matrix, in which the two elements compare which is important and how important, and the importance degree should be assigned according to 1 ~ 9 (see Table 1 for the important scale value). All comparison results are expressed by positive reciprocal matrix:

Table 1 significance scale meaning table

Saaty suggests using the maximum characteristic root corresponding to the matrix（ λ） The feature vector of is normalized as the weight vector ω， That is, a ω=λω。 According to matrix theory, the matrix has a unique nonzero maximum eigenvalue, and ω It is the eigenvector corresponding to the largest eigenroot of the matrix, which is unique after normalization. Intuitively, because the eigenvalues and eigenvectors of the matrix also continuously depend on the matrix elements, it is shown that when the requirements of element consistency are not far away, the eigenvalues and eigenvectors of the matrix are not much different from the consistency. Saaty also gives a special quantity to calibrate the consistency index. When ci=0, the pairwise comparison matrix is the consistency matrix; The greater the CI value, the more serious the inconsistency. In order to further determine its allowable range, Saaty introduced the so-called average random consistency index RI (see Table 2). At that time, it was considered that the consistency of the judgment matrix was acceptable, otherwise the judgment matrix should be appropriately modified. Table 2 empirical value of the average random consistency index RI

2 AHP analysis of grain drying quality

through production tests and testing institutions, the product quality is evaluated and analyzed, in order to select the best production process from the three schemes (see Table 3). Table 3 three different drying process schemes

2.1 construct hierarchical structure

through production testing, after in-depth analysis of the problems, find out the various factors that affect the final drying quality of grain. At this time, the target level factors and scheme level factors are generally smaller and clearer than the shrinkage rate, while the criteria level factors are usually more, so it is necessary to carefully analyze their relationship, as well as the relationship between upper and lower levels and the same group. The specific level division of grain quality is shown in Figure 1

Figure 1 Schematic diagram of the hierarchical structure of grain drying

2.2 construct the judgment matrix and calculate the weight of the criterion layer B to the target layer A. after several comparisons, the following positive and reciprocal matrix is formed:

use MATLAB6.5 software for data processing, and use the command eigs in the tool box to solve its maximum eigenvalue and eigenvector [x, lamda]=eigs (a, 1), lamda= 6.4203

(when n takes 6, ri=1.24), It meets the consistency test, so ω The value can be used as a weight vector. The weight of criterion layer B to target layer a is:

the weight of scheme layer C to criterion layer B, and a positive reciprocal matrix is constructed.

the calculated weight vector and eigenvalue are shown in table 4. The weight vector and eigenvalue of C to B are shown in Table 4. It can be seen from table 4 that the consistency test of six indicators has passed, and the combined weight of scheme vacuum low temperature drying process C1 is:

0.7306 × 0.1584+0.0719 × 0.1892+0.7352 × 0.1980+0.7514 × 0.0483+0.7626 × 0.1502+0.7223 × 0.2558=0.6106；

the combined weight of steam drying process C2 in the scheme is:

0.1884 × 0.1584+0.2790 × 0.1892+0.2067 × 0.1980+0.1782 × 0.0483+0.1763 × 0.1502+0.2050 × 0.2558=0.2111；

scheme the combined weight of hot air drying process C3 is:

0.0810 × 0.1584+0.6491 × 0.1892+0.0581 × 0.1980+0.0704 × 0.0483+0.0611 × 0.1502+0.0727 × 0.2558=0.1784；

therefore, the combination weight of scheme layer C to target layer a is:

the value of combination consistency index CI is:

0.0324 × 0.1584+0.0324 × 0.1892+0.0585 × 0.1980+0.0145 × 0.0483+0.0539 × 0.1502+0.0619 × 0.2558=0.0475；

the results of hierarchical ranking have satisfactory consistency

3 result analysis

from the results of the overall ranking of the scheme level, the weight of vacuum low-temperature drying process (C1) (0.6106)> steam drying process (C2) (0.2111)> hot air drying process (C3) (0.1784). Therefore, the final decision-making scheme is to choose the true air low-temperature drying process

for the six factors of criterion layer B, the weight of texture characteristics (B4) is the lowest (0.0483), the weight of total energy consumption (B6) (0.2558), the weight of nutrition loss rate (B2) (0.1892) and taste loss rate (B3) (0.1980) are relatively high, followed by the weight of appearance (B1) (0.1584) and crack rate increment (B5) (0.1502), indicating that energy consumption, nutrition and food quality are more valued in decision-making

from this, we can analyze the decision-making idea, that is, the decision-making pays more attention to the total energy consumption, nutrition and edible benefits, and does not pay much attention to the texture characteristics. Therefore, for specific factors, energy conservation and consumption reduction, nutrition and edible become the main considerations. For these three factors, it is better to adopt the vacuum low-temperature drying process scheme

through productive test and comparison, vacuum low-temperature drying of corn can achieve rapid drying. Dry grain with moisture content of 14.5% can be obtained by drying wet grain with moisture content of 24%. When reaching the same drying degree, vacuum low-temperature drying takes far less time than atmospheric hot air drying and steam drying; The unit heat consumption is less than 5000kj/kg · H2O, which is far lower than the heat consumption of hot air series and steam series drying process. It saves about 30% energy. It has the advantages of good drying quality, fast precipitation, high output, low energy consumption, convenient operation, and high economic performance price ratio

The analytic hierarchy process processes people's thinking process and puts forward a set of methods for systematic analysis of problems, which provides a more convincing basis for scientific evaluation and decision-making, The analytic hierarchy process has certain practical significance in the analysis of grain drying quality. Behind this development, there are two main driving forces: "1. Meaning. But analytic hierarchy process also has its limitations, mainly in: (I) It depends on people's experience to a large extent, and subjective factors have a great influence. At most, it can only exclude the serious inconsistency in the thinking process, but it can not exclude the serious one sidedness that may exist in the decision-maker. (II) the comparison and judgment process is relatively rough and cannot be used for decision-making problems with high accuracy requirementsreferences

[1] Peng Zuzhi, mathematical models and modeling methods, Dalian Maritime University Press, Dalian, 1997

[2] Ye Qixiao, college students' mathematical modeling competition counseling materials, Hunan Education Press, Changsha, 1993

[3] Zhang Zhiyong, proficient in MATLAB6.5 edition, Beijing University of Aeronautics and Astronautics Press, Beijing, 2003

[4] Fang Kaitai, practical multivariate statistical analysis [m], East China Normal University Press, 1989

[5] Wang Yingluo, system engineering, machinery industry press, 1997

[6] Zhao Xiangtao, research and design of new energy-saving, efficient and quality guaranteed grain drying process and equipment, Proceedings of the third academic annual meeting of China Grain and oil society, 2004, 9

[7] Zhao Xiangtao, application and development of vacuum technology in the grain industry, Sichuan: grain storage, 2006,4

Fund Project: "Research on Key Technologies of grain and oil product storage and quality testing" project of the national scientific and technological breakthrough in the Tenth Five Year Plan (2004ba523b01)

introduction to the author Zhao Xiangtao, male; Engaged in the research of vacuum technology, drying technology and mechanical manufacturing automation; Email:xiangtao588@；：； Address:

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