A garbage classification method based on thermochemical kinetics

Abstract

The present invention discloses a waste classification method based on thermochemical kinetics, comprising: determining dry waste samples; obtaining thermochemical kinetic parameters of the dry waste samples based on the dry waste samples; selecting parameters as the first classification basis for the first classification based on the thermochemical kinetic parameters; and obtaining the second classification basis for the second classification based on the first classification basis in combination with the elemental distribution characteristics of the dry waste samples. The present invention helps to classify, process and utilize waste with different thermochemical kinetic characteristics, and improve the energy conversion efficiency of downstream waste.

Description

Technical Field

[0001] The present invention belongs to the cross - technical field of energy and environment, and particularly relates to a garbage classification method based on thermochemical kinetics. Background Art

[0002] The energy utilization of organic dry garbage is a common treatment method. However, since the components of organic dry garbage vary, it is difficult to meet the optimal conversion requirements of each component under a single working condition during energy utilization. Therefore, there is a phenomenon that the conversion rate of dry garbage is good in the laboratory, but the effect is poor in actual engineering applications, and the difference in conversion rate can reach more than 10%. This proves that the heterogeneity of organic dry garbage poses a hazard to downstream thermochemical utilization. Therefore, in the actual utilization process, it is necessary to classify dry garbage according to its characteristics in order to achieve the optimal conversion. For this purpose, the present invention proposes a method to solve the heterogeneity of organic dry garbage, classifying organic dry garbage based on the kinetic parameter characteristics in thermochemical utilization, hoping to achieve a one - to - one correspondence between the raw material components and their optimal energy utilization methods. Summary of the Invention

[0003] To solve the above - mentioned technical problems, the present invention proposes a garbage classification method based on thermochemical kinetics to classify dry garbage and improve the conversion rate of dry garbage.

[0004] To achieve the above object, the present invention provides a garbage classification method based on thermochemical kinetics, including the following steps:

[0005] Determine the dry garbage sample;

[0006] Based on the dry garbage sample, obtain the thermochemical kinetic parameters of the dry garbage sample; select parameters as the first classification basis for the first classification based on the thermochemical kinetic parameters;

[0007] Based on the first classification basis and combined with the element distribution characteristics of the dry garbage sample, obtain the second classification basis for the second classification.

[0008] Optionally, the dry garbage sample is several different types of organic dry garbage.

[0009] Optionally, the method for obtaining the thermochemical kinetic parameters of the dry garbage sample based on the dry garbage sample includes: obtaining the thermochemical kinetic parameters of the dry garbage sample by thermogravimetric analysis based on the dry garbage sample.

[0010] Optionally, the first classification basis includes: average activation energy, initial pyrolysis temperature, final temperature, peak temperature, and final residual amount.

[0011] Optionally, the second classification basis includes three classification boundaries, and the elemental distribution characteristics of the dry waste samples are C, H, O, N, and S, where,

[0012] The first classification boundary is: N + S > 4.0 wt.%;

[0013] The second classification boundary is: (C + H) - O > 30.0 wt.%, N + S < 4.0 w.t%;

[0014] The third classification boundary is: (C + H) - O < 30.0 wt.%, N + S < 4.0 wt.%.

[0015] Optionally, after the second classification, the method for verifying the rationality of waste classification includes:

[0016] The first classification basis has a significant correlation with the elemental composition of the dry waste sample. Obtain the Pearson correlation coefficient between the first classification basis and the elemental classification characteristics of the dry waste sample; based on the Pearson correlation coefficient and combined with the second classification basis, verify the rationality of waste classification.

[0017] Optionally, the method for obtaining the Pearson correlation coefficient between the first classification basis and the elemental classification characteristics of the dry waste sample is:

[0018]

[0019] where cov(x,y) is the covariance of datasets x and y, and σ x and σ y represent the standard deviations of datasets x and y.

[0020] Optionally, verifying the rationality of waste classification based on the Pearson correlation coefficient and combined with the second classification basis includes three cases, where,

[0021] The first case is: the initial pyrolysis temperature is 150 - 300 °C, the final pyrolysis temperature is 300 - 800 °C, the peak temperature is 200 - 500 °C, the activation energy is 60 - 200 kJ / mol, concentrated around 200 kJ / mol, and the final residue is 10 - 40% w / w;

[0022] The second case is: the initial pyrolysis temperature is 300 - 400 °C, the final pyrolysis temperature is 400 - 800 °C, the peak temperature is 370 - 490 °C, the activation energy is 80 - 350 kJ / mol, concentrated around 250 kJ / mol, and the final residue is 0 - 20% w / w;

[0023] The third case is as follows: the initial pyrolysis temperature is 200 - 300 °C, the final pyrolysis temperature is 350 - 800 °C, the peak temperature is 250 - 370 °C, the activation energy is 60 - 180 kJ / mol, concentrated around 150 kJ / mol, and the final residue is 15 - 30% w / w.

[0024] Technical effects of the present invention: The present invention discloses a waste classification method based on thermochemical kinetics. The three newly separated categories have significant differences in kinetic parameters. The first type of waste has good fuel properties and is suitable for producing energy products such as bio-oil with high yields. The second type of waste has high pollutant element contents, and more attention should be paid to pollutant control during energy utilization. The third type of dry waste has average fuel properties but low pollutant contents and can be directly incinerated. The present invention helps to classify and utilize waste with different thermochemical kinetic characteristics, improving the energy conversion efficiency of downstream waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0026] Figure 1 It is a schematic flow chart of the waste classification method based on thermochemical kinetics in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0028] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0029] As Figure 1 shown, in this embodiment, a waste classification method based on thermochemical kinetics is provided, including the following steps:

[0030] Determine the dry waste sample;

[0031] Based on the dry waste sample, obtain the thermochemical kinetic parameters of the dry waste sample; select the parameters as the first classification basis for the first classification;

[0032] Based on the first classification basis and combined with the element distribution characteristics of the dry waste sample, obtain the second classification basis for the second classification.

[0033] S1. Determine typical dry waste raw materials

[0034] The energy utilization targets organic dry waste. In this example, 66 samples of leather, hair, cotton, hemp, polyester fiber, rubber, plastic, wood, bamboo, and paper were selected as the research objects.

[0035] S2. Select the thermochemical kinetic parameters of the raw materials

[0036] Pyrolysis is an important thermochemical reaction method for treating waste and is the initial and accompanying reaction step of combustion and gasification. Therefore, the kinetic parameters of pyrolysis are selected as typical parameters. Thermal analysis can obtain the thermal behavior of the raw materials and provide kinetic information. Thermogravimetric analysis is one of the most widely used thermal analysis techniques and can be used to measure the pyrolysis kinetic parameters of raw materials. In this patent, five parameters, namely the average activation energy (Ea), initial pyrolysis temperature (To), final temperature (Te), peak temperature (Tp), and final residue, are selected as the classification basis.

[0037] S3. Cluster the samples to be tested according to the thermochemical kinetic parameters of the samples

[0038] Classify the dry waste raw materials again according to the kinetic distribution characteristics of the samples.

[0039] (1) There is a significant correlation between the thermochemical kinetic characteristics of the raw materials and the elemental composition (C, H, O, N, S) of the raw materials.

[0040] The Pearson correlation coefficient PCC is a linear correlation coefficient (-1 < PCC < 1) used to represent the linear correlation degree between two variables X and Y. The larger the absolute value, the stronger the correlation.

[0041]

[0042] where cov(x,y) is the covariance of the data sets x and y, and σ x and σ y represent the standard deviations of the data sets x and y.

[0043] Calculate the Pearson correlation coefficient of the elemental composition (C, H, O, N, S) and kinetic parameters (average activation energy (Ea), initial pyrolysis temperature (To), final temperature (Te), peak temperature (Tp), final residue) of the collected waste samples. The results show that there is a significant linear correlation between the elemental composition and kinetic parameters of the waste samples.

[0044] (2) Obtaining the thermochemical kinetic parameters of the raw materials by thermal analysis technology requires thermogravimetric testing, experimental calculation, etc., and the operation is cumbersome. Based on a method for quickly predicting the characteristics of solid waste constructed by us before, we can realize the reclassification of raw materials in thermochemical kinetics by classifying the raw materials based on their elemental composition.

[0045] Considering the C, H, O, N, and S distribution characteristics of the raw materials comprehensively, the waste is reclassified. First, the waste is classified according to N and S, and the boundary can be set at 4%. There are two reasons for this. On the one hand, the environmental effects during the energy utilization process are considered. When the N content in the raw material is higher than 0.6%, it will cause environmental problems related to N x O y emissions. At the same time, referring to the Chinese Coal Classification Standard (GB / T 15224.2 - 2010), raw materials with an S content higher than 1.0% can be regarded as S-rich coal. On the other hand, the influence of N and S atoms in biochar is considered. Waste with an N content higher than 3.0% can be defined as N-rich waste, which can be converted into N-doped biochar without other N sources. For S-doped biochar, the influence of S atoms in the material itself is relatively small, and different S-doping reagents are used to dope S atoms into biochar. Next, the second classification boundary is determined according to the C, H, O numerical distribution and calorific value of the waste. The calorific value of waste with (C + H) - O > 30.0 wt.% and (N + S) < 4.0 wt.% is higher than 20.0 MJ / kg, which is similar to the calorific value classification of coal in the national standard (GB / T 15224.3 - 2010). Thus, the waste can be divided into three categories: 1) N + S > 4.0 wt.%; 2) (C + H) - O > 30.0 wt.%, N + S < 4.0 w.t%; 3) (C + H) - O < 30.0 wt.%, N + S < 4.0 wt.%.

[0046] (3) Summarize the distribution characteristics of kinetic parameters in the three new categories of waste above, and evaluate the rationality of the new classification of dry waste categories.

[0047] 1) The initial pyrolysis temperature is 150 - 300 °C, the final pyrolysis temperature is 300 - 800 °C, the peak temperature is 200 - 500 °C, the activation energy is 60 - 200 kJ / mol, concentrated around 200 kJ / mol, and the final residue is 10 - 40% w / w.

[0048] 2) The initial pyrolysis temperature is 300 - 400 °C, the final pyrolysis temperature is 400 - 800 °C, the peak temperature is 370 - 490 °C, the activation energy is 80 - 350 kJ / mol, concentrated around 250 kJ / mol, and the final residue is 0 - 20% w / w.

[0049] 3) The initial pyrolysis temperature is 200 - 300 °C, the final pyrolysis temperature is 350 - 800 °C, the peak temperature is 250 - 370 °C, the activation energy is 60 - 180 kJ / mol, concentrated around 150 kJ / mol, and the final residue is 15 - 30% w / w.

[0050] The data in this example shows a normal distribution, and the differences in kinetic parameters of the three new categories of dry waste obtained by one-way analysis of variance can be calculated.

[0051] The results of the analysis of variance calculated using the analysis of variance show that there are significant differences in the initial pyrolysis temperature, final pyrolysis temperature, activation energy, and final residue among the three new categories, and the P-values are 2.5E-3, 1.6E-13, 1.1E-3, 9.9E-15, and 4.8E-5 respectively.

[0052] The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A waste sorting method based on thermochemical kinetics, characterized in that, It includes the following steps: Determine the dry waste sample; Based on the dry waste sample, obtain the thermochemical kinetic parameters of the dry waste sample, and select parameters as the first classification basis for the first classification based on the thermochemical kinetic parameters; Based on the first classification basis and combined with the elemental distribution characteristics of the dry waste sample, obtain the second classification basis for the second classification; The second classification basis includes three classification boundaries, and the elemental distribution characteristics of the dry waste sample are C, H, O, N, and S respectively, where The first classification boundary is: N + S > 4.0 wt.%; The second classification boundary is: (C + H) - O > 30.0 wt.%, N + S < 4.0 wt.%; The third classification boundary is: (C + H) - O < 30.0 wt.%, N + S < 4.0 wt.%.

2. The waste classification method based on thermochemical kinetics according to claim 1, characterized in that The dry waste sample is several different types of organic dry waste.

3. The waste classification method based on thermochemical kinetics according to claim 1, characterized in that The method for obtaining the thermochemical kinetic parameters of the dry waste sample based on the dry waste sample includes: obtaining the thermochemical kinetic parameters of the dry waste sample by thermogravimetric analysis based on the dry waste sample.

4. The waste classification method based on thermochemical kinetics according to claim 1, characterized in that The first classification basis includes: average activation energy, initial pyrolysis temperature, final temperature, peak temperature, and final residual amount.

5. The waste classification method based on thermochemical kinetics according to claim 1, characterized in that After the second classification, the method for checking the rationality of waste classification includes: The first classification basis has a significant correlation with the elemental composition of the dry waste sample, and obtain the Pearson correlation coefficient between the first classification basis and the elemental distribution characteristics of the dry waste sample; check the rationality of waste classification based on the Pearson correlation coefficient and combined with the second classification basis.

6. The waste sorting method based on thermochemical kinetics according to claim 5, wherein The method for obtaining the Pearson correlation coefficient between the first classification basis and the elemental distribution characteristics of the dry waste sample is: Among them, cov(x, y) is the covariance of datasets x and y, and σ x and σ y represent the standard deviations of datasets x and y.

7. The waste classification method based on thermochemical kinetics according to claim 5, characterized in that Checking the rationality of waste classification based on the Pearson correlation coefficient and combined with the second classification basis includes three cases, where The first case is: the initial pyrolysis temperature is 150 - 300 °C, the final pyrolysis temperature is 300 - 800 °C, the peak temperature is 200 - 500 °C, the activation energy is 60 - 200 kJ / mol, concentrated around 200 kJ / mol, and the final residual amount is 10 - 40% w / w; The second case is: the initial pyrolysis temperature is 300 - 400 °C, the final pyrolysis temperature is 400 - 800 °C, the peak temperature is 370 - 490 °C, the activation energy is 80 - 350 kJ / mol, concentrated around 250 kJ / mol, and the final residual amount is 0 - 20% w / w; The third case is as follows: the initial pyrolysis temperature is 200 - 300 °C, the final pyrolysis temperature is 350 - 800 °C, the peak temperature is 250 - 370 °C, the activation energy is 60 - 180 kJ / mol, concentrated around 150 kJ / mol, and the final residue is 15 - 30% w / w.

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