Comprehensive Evaluation of Quality and Quality of Grain Vacuum Drying by AHP

We use the analytic hierarchy process to evaluate the vacuum drying, steam drying and hot air drying of grain, and propose a comprehensive quantitative and qualitative hierarchical structure evaluation model and overall evaluation method. Firstly, the hierarchical analysis structural model and the judgment matrix for grain drying quality optimization are established, and then the hierarchy is sorted. Consistency tests are performed on the judgment matrix, and evaluation methods are formed on the basis of qualitative and quantitative index conversion. The production test shows that this method is simple and feasible, and the conclusion is in line with reality.
Keywords: Analytic hierarchy process Vacuum drying Steam drying Hot air drying Process optimization Comprehensive evaluation system
Solve the problem of grain drying, keep the original color, aroma, taste, shape, and nutrient composition as much as possible, eliminate problems such as solute loss, surface hardening, and quality degradation in the traditional drying process, and achieve the three control objectives of quality, energy saving, and environmental protection. Therefore, the preferred drying process is always an important research topic. In view of the current status of grain drying in China, three alternatives, vacuum low-temperature drying, steam drying and hot-air drying, were used to comprehensively evaluate the 300t/d corn production test, and a reasonable drying process was selected to improve the drying quality of grain and reduce the loss of grain drying. It is of great significance.
Analytic hierarchy process is a powerful tool for analyzing complex decision-making problems such as multi-objective and multi-criteria. It has the characteristics of clear thinking, simple method, wide application scope, strong system, and easy promotion. In this paper, AHP method is used to comprehensively analyze the quality of grain drying in order to provide a certain degree of basis for the selection of drying process.
1 Analytic Hierarchy and Analytic Hierarchy Prices (AHP) was proposed by the American operational researcher and Professor TLSaaty of the University of Pittsburgh in the 1970s. He first worked for the United States in 1971. The Ministry of National Defense used the AHP when studying the "Contingency Plan" and published the article "Modeling and Analytic Hierarchy Process without Structural Decision Problems" at the International Mathematical Modeling Conference in 1977. Since then, the AHP has been used in many areas of decision-making problems. At the same time, the theory of AHP has also been continuously deepened and developed. AHP was introduced to China in 1982.
The basic idea of ​​AHP is to convert the overall judgment of the weights of multiple elements that make up a complex problem into a "two-by-two comparison" of these elements, and then turn to judge the overall weight of these elements, and finally establish the elements. Weights. The quality assessment index system for grain drying quality is a multi-level and multi-index composite system. In this composite system, the relative importance of each level and index is different, it is difficult to determine scientifically, and commonly used empirical valuation methods are used by experts. Laws such as the determination method are hardly effective or even impossible. Analytic hierarchy process constructs a judgment matrix. First, it identifies the factors and their interrelationships involved in the problem, and hierarchically serializes the problems to be solved. According to the nature of the problem and the objectives to be achieved, the problem is decomposed into different components. , According to the mutual influence and affiliation between factors, they are layered and combined to form a hierarchical, orderly, hierarchical structure model. Secondly, the relative importance of each level factor in the model is based on a quantitative representation of people's judgment of objective reality, and then the mathematical method is used to determine the weights of the relative importance order of all factors at each level. Finally, by comprehensively calculating the weights of the relative importance of the factors in each layer, the combined weights of the relative importance order of the lowest level relative to the highest level are obtained as the basis for the evaluation and selection scheme. Using analytic hierarchy process, not only can reduce the difficulty of work, improve the accuracy and scientificity of index weight, but also by taking measures such as consistency check of the judgment matrix, it is beneficial to improve the reliability and validity of weight determination, and at the same time, the calculation matrix When eigenvectors are used, we can use sum-product method, power method and square-root method and other ideas, and can use computers to process data. It has strong operability and can achieve more satisfactory decision-making results.
Saaty et al. suggested that pairwise comparison matrices could be used to compare the factors in pairs, in which the two elements were compared, which was important and important, and the degree of importance was assigned from 1 to 9 (importance scale values ​​are shown in the table. 1). All comparison results are represented by a positive reciprocal matrix: A=(aij)m×n, where aij>0 and aji=1/aij.

Table 1 Significance Scale Meaning Table

Saaty suggests normalizing the eigenvectors corresponding to the matrix's largest eigenvalue (λ) as the weight vector ω, ie Aω=λω. According to the matrix theory, the matrix has a unique non-zero maximum eigenvalue λmax, and ω is the eigenvector corresponding to the largest eigenvalue of the matrix. The eigenvector is unique after normalization. Intuitively, because the matrix's eigenvalues ​​and eigenvectors are also dependent on the matrix elements continuously, the eigenvalues ​​and eigenvectors of the matrix are not much different from the consistency when the requirements of element consistency are not far. Saaty also gives a special quantity CI=(λmax-n)/(n-1) for calibrating the consistency index. When CI=0, the pairwise comparison matrix is ​​a consistent matrix; the larger the CI value, the greater the degree of inconsistency is. The more serious. In order to further determine its allowable range, Saaty introduced the so-called average random consistency indicator RI (see Table 2). When CR=CI/RI<0.10, it is considered that the consistency of the judgment matrix is ​​acceptable, otherwise the judgment matrix should be made appropriate. Corrected.

Table 2 Empirical Values ​​of the Average Random Uniformity Index RI

2 Grain Drying Quality AHP analysis is conducted through production testing and inspection agencies, and product quality is evaluated and analyzed. The purpose is to select the best production process from among the three options (see Table 3).

Table 3 Three Different Drying Process Plans

2.1 Construct Hierarchical Hierarchies Through production testing, after in-depth analysis of problems, identify the factors that affect the quality of the final dry quality of grain. At this time, the target-level factors and program-level factors are generally relatively clear, and the criteria-level factors are usually more. It is necessary to carefully analyze their mutual relations, and the relationship between the upper and lower levels and the same group. The specific levels of the quality of food quality are divided as follows.
Target layer A: (select food specific drying process);
Guidelines B: (appearance B1; nutrient loss rate B2; taste loss rate B3; texture characteristics B4; crack rate increase B5; total energy consumption B6);
Plan C: (vacuum drying process C1; steam drying process C2; hot air drying process C3)
2.2 Construction Judgment Matrix and Calculation Criterion Layer B occupies the weight of the target layer A. The six factors of the B layer are compared by C=6×(6-1)/2=15 times to form the following positive reciprocal inverse matrix:
A=[1 1 1 4 1 1/2; 1 1 2 4 1 1/2; 1 1/2 1 5 3 1/2; 1/4 1/4 1/5 1 1/3 1/3; 1 1 1/3 3 1 1;2 2 2 3 1 1]
Using Matlab6.5 software for data processing, use the eigs command in the toolbox to solve for the largest eigenvalue and eigenvector [X,Lamda]=eigs(A,1),Lamda= 6.4203
X=[0.363 0.4337 0.4537 0.1107 0.3443 0.5861]';
Normalization process X' = [0.1584 0.1892 0.198 0.0483 0.1502 0.2558]'
CI = (6.4203-6)/(6-1) = 0.0481, CR = CI/RI = 0.0481/1.24 = 0.0678 < 0.10 (where η = 6, RI = 1.24), which satisfies the consistency check, so ω can be used as Weight vector, the weight of criterion layer B for target layer A:
ω = [0.1584 0.1892 0.198 0.0483 0.1502 0.2558]';
The weight of the scheme layer C on the criteria layer B, constructing a positive reciprocal matrix
B1=[1 5 7;1/5 1 3;1/7 1/3 1];
B2=[1 1/5 1/7; 5 1 1/3; 7 3 1];
B3=[1 5 9;1/5 1 5;1/9 1/5 1];
B4=[1 5 9;1/5 1 3;1/9 1/3 1];
B5=[1 6 9;1/6 1 4;1/9 1/4 1];
B6=[1 5 7;1/5 1 4;1/7 1/4 1].
Calculate the weight vector and eigenvalues ​​in Table 4

Table 4 Weights and eigenvalues ​​of C vs. B

It can be seen from Table 4 that the consistency check of the six indicators is passed, and the combined weight of the program 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 the steam drying process C2 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;
The combined weight of the program 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 combined weight of the solution layer C for the target layer A is: ω = [0.6106 0.2111 0.1784]';
The combined 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;
CR=CI/RI=0.0475/0.58=0.0819<0.10 The results of the hierarchical ranking have satisfactory consistency.
3 Results analysis From the results of the total ranking of the solution layers, the weight of the vacuum low-temperature drying process (C1) (0.6106)> the weight of the steam drying process (C2) (0.2111)> the weight of the hot-air drying process (C3) (0.1784), therefore The final decision-making plan is to choose the vacuum cryogenic drying process.
For the six factors of criterion level B, the texture property (B4) has the lowest weight (0.0483), the total energy consumption (B6) weight (0.2558), the nutrient loss rate (B2) weight (0.1892), and the taste loss rate ( The weights (0.1980) of B3) are relatively high, the weight of appearance (B1) (0.1584) and the weight of crack rate (B5) (0.1502) are second, which indicates that the energy consumption, nutrition, and food quality are more important in decision-making.
From this, we can analyze the decision-making thinking, that is, the decision value more important is the total energy consumption, nutrition and food efficiency, not too much emphasis on the characteristics of the texture, so for specific factors, energy saving, nutrition and food has become the main consideration, for this The three factors are all better with the vacuum low-temperature drying process scheme.
After production tests and comparisons, the maize is subjected to vacuum drying at a low temperature to achieve rapid drying. Moisture content of 24% moisture content is dried to obtain a dry grain with a water content of 14.5%. When the same degree of drying is achieved, vacuum low-temperature drying is much less than the time required for atmospheric hot-air drying and steam drying; unit heat consumption is less than 5000kJ / kg • H2O, far lower than the hot air series and steam series drying process heat consumption, energy saving about 30%, with good drying quality, fast precipitation, high output, low energy consumption, easy operation, cost-effective and high cost advantages.
The Analytic Hierarchy Process (AHP) processes and processes people's thinking process, and proposes a method for systematically analyzing problems, which provides a more persuasive basis for scientific evaluation and decision-making. Analytic hierarchy process (AHP) methods for quality analysis of grain drying have a certain degree of certainty. Practical significance. However, AHP also has its limitations, which are mainly reflected in: (i) it depends to a great extent on people's experience, subjective factors have a great influence, and at most it can only eliminate serious inconsistencies in the thinking process. It cannot exclude the serious one-sidedness that policy makers may have. (ii) The process of comparison and judgment is rough and cannot be used for decision problems with high accuracy.

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