CN108504374B - Coking chemical product yield prediction model - Google Patents
Coking chemical product yield prediction model Download PDFInfo
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- CN108504374B CN108504374B CN201810309263.9A CN201810309263A CN108504374B CN 108504374 B CN108504374 B CN 108504374B CN 201810309263 A CN201810309263 A CN 201810309263A CN 108504374 B CN108504374 B CN 108504374B
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10B—DESTRUCTIVE DISTILLATION OF CARBONACEOUS MATERIALS FOR PRODUCTION OF GAS, COKE, TAR, OR SIMILAR MATERIALS
- C10B57/00—Other carbonising or coking processes; Features of destructive distillation processes in general
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Abstract
The invention relates to the technical field of coking industry, in particular to a coking chemical product yield prediction model, wherein the yields of tar and crude benzene in the model are related to combustible base volatile components, caking indexes and coal rock activity compositions, and meanwhile, clean gas is related to the yields of the tar and the crude benzene. The prediction model of the invention introduces the concept of influence of coal caking property and coal rock composition on the chemical production rate, and also considers the influence of parameters such as the width of a carbonization chamber, coking time and the like on the model, thereby achieving more scientific and accurate prediction result.
Description
Technical Field
The invention relates to the technical field of coking industry, in particular to a coking chemical product yield prediction model.
Background
The coking chemical industry plays an important role in the coal chemical industry. The coking chemical products are widely applied to the industries such as medical industry, chemical industry, national defense industry and the like, and can be used for civil use, and the coking industry is very important for the development of national economy in China, so that the coking industry product theoretical yield prediction model has great significance for improving the yield of the coking chemical products. In a traditional prediction formula, only the influence factors of the volatile components of the coal are considered in a yield prediction model of the tar, the crude benzene and the clean gas, and the influence of other indexes of the coal quality is not considered, so that the yield prediction of the chemical products has certain limitation and low accuracy.
The method comprises the following steps of: when V isdaf•mWhen =18% -30%
Tar Kd•g=〔-18.35+1.53Vdaf•m-0.026(Vdaf•m)2〕(100-Ad/100)
Crude benzene Kd•b=〔-1.61+0.144Vdaf•m-0.0016(Vdaf•m)2〕(100-Ad/100)
The chemical industry general factory of Anshan iron and Steel company proposes: when V isdaf•mIf the content is not less than 27.96% and not more than 30.37%,
coal tar Kd•g=〔-1.4+0.184Vdaf•m〕(100-Aa/100)
Crude benzene Kd•b=〔-0.64+0.065Vdaf•m〕(100-Ad/100)
Net gas yield prediction in the traditional prediction formula: q = α V coal = 3.10V coal
In the traditional prediction formula, the yields of tar, crude benzene and clean gas are not directly related, and in the actual production process, the three indexes are related, such as the selection of certain coal types can cause the increase of gas and the reduction of tar and crude benzene.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a coking chemical product yield prediction model, which solves the problems of inaccurate prediction result and large deviation of the existing coking chemical product prediction model.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a coking chemical product yield prediction model comprises prediction of tar yield, prediction of crude benzene yield and prediction of coal gas yield, and is characterized in that the prediction model comprises the following steps:
tar yield (%) [ a ] α1*Vd*（G+α2*E）+α3]/100
Crude benzene yield (%) < b > β1*Vd*（G+β2*E）+β3]/100
Net gas yield (Nm)3）：k*[Vd- γ x (tar yield + crude benzene yield)]/0.04849
In the formula, a is the influence coefficient of the furnace type and the coking time on the tar yield; b is the influence coefficient of the furnace type on the yield of the crude benzene; vdIs dry-based volatile component, G is coal caking index, E is active component in coal rock, α1、α2、α3、β1、β2、β3K and gamma are coefficients.
α1、α2、α3、β1、β2、β3K, γ are obtained by the following formula:
when Vd is more than or equal to 27 and less than 30, α1、α2、α30.22, 0.28 and 1.0 respectively;
β1、β2、β30.055, 0.12, 0.5, respectively;
k. gamma is 0.75 and 0.9 respectively;
when Vd is more than or equal to 24 and less than 27, α1、α2、α30.21, 0.28, 0.4 respectively;
β1、β2、β30.053, 0.10 and 0.3 respectively;
k. gamma is 0.73 and 0.9 respectively;
when Vd is more than or equal to 18 and less than 24, α1、α2、α30.18, 0.25, 0.8, respectively;
β1、β2、β3respectively 0.043, 0.18 and 0.6
k. Gamma is 0.69 and 0.95 respectively.
Preferably, the influence coefficient a of the furnace type and the coking time on the tar yield and the influence coefficient b of the furnace type on the crude benzene yield are obtained according to the following formula:
coefficient of influence of oven type and coking time on tar yield a = c × d
Coefficient of influence of furnace type on crude benzene yield b =0.95 ×.c
Wherein c is a furnace type coefficient and d is a coking time coefficient.
The furnace type coefficient is shown in Table 1, and the coking time coefficient is shown in Table 2:
TABLE 1 furnace type factor
TABLE 2 coking time coefficient
| Coking time | 20h | 20.5h | 22.5h | 23h | 24h | 25h | 25.5h | 26h |
| Coefficient (d) | 0.9 | 0.9 | 0.85 | 0.83 | 0.82 | 0.8 | 0.78 | 0.78 |
The coal is different in high-temperature carbonization process of the coke oven and the structure of the coke oven along with the carbonization chamber, the overflow modes of the generated gas are different, the gas can be partially retained when meeting resistance, and the chemical yield is reduced; in addition, the longer the coking time, the longer the gas residence time in the furnace, and the reduced product yield.
Preferably, the predictive model incorporates coal petrography activity components.
Preferably, the net gas yield is correlated to tar yield, crude benzene yield.
Compared with the prior art, the invention has the beneficial effects that:
the prediction model of the invention introduces the concept of influence of coal caking property and coal rock composition on the chemical production rate, and also considers the influence of parameters such as the width of a carbonization chamber, coking time and the like on the model, thereby achieving more scientific and accurate prediction results and setting a target for chemical production management of coking enterprises.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A coking chemical product yield prediction model comprises prediction of tar yield, prediction of crude benzene yield and prediction of coal gas yield, and is characterized in that the prediction model comprises the following steps:
tar yield (%) [ a ] α1*Vd*（G+α2*E）+α3]/100
Crude benzene yield (%) < b > β1*Vd*（G+β2*E）+β3]/100
Net gas yield (Nm)3）：k*[Vd- γ x (tar yield + crude benzene yield)]/0.04849
Wherein a is the influence coefficient of the furnace type and the coking time on the yield of tar, b is the influence coefficient of the furnace type on the yield of crude benzene, and Vdα, wherein G is coal caking index, E is active component in coal rock1、α2、α3、β1、β2、β3K and gamma are coefficients.
Coefficient α1、α2、α3、β1、β2、β3K, γ are obtained by the following formula:
when Vd is more than or equal to 27 and less than 30, α1、α2、α30.22, 0.28 and 1.0 respectively;
β1、β2、β30.055, 0.12, 0.5, respectively;
k. gamma is 0.75 and 0.9 respectively;
when Vd is more than or equal to 24 and less than 27, α1、α2、α30.21, 0.28, 0.4 respectively;
β1、β2、β30.053, 0.10 and 0.3 respectively;
k. gamma is 0.73 and 0.9 respectively;
when Vd is more than or equal to 18 and less than 24, α1、α2、α30.18, 0.25, 0.8, respectively;
β1、β2、β30.043, 0.18 and 0.6 respectively;
k. gamma is 0.69 and 0.95 respectively.
Preferably, the influence coefficient a of the furnace type and the coking time on the tar yield and the influence coefficient b of the furnace type on the crude benzene yield are obtained according to the following formula:
coefficient of influence of oven type and coking time on tar yield a = c × d
Coefficient of influence of furnace type on crude benzene yield b =0.95 ×.c
Wherein c is a furnace type coefficient and d is a coking time coefficient.
The furnace type coefficient is shown in Table 1, and the coking time coefficient is shown in Table 2:
TABLE 1 furnace type factor
TABLE 2 coking time coefficient
| Coking time | 20h | 20.5h | 22.5h | 23h | 24h | 25h | 25.5h | 26h |
| Coefficient (d) | 0.9 | 0.9 | 0.85 | 0.83 | 0.82 | 0.8 | 0.78 | 0.78 |
The coal is different in high-temperature carbonization process of the coke oven and the structure of the coke oven along with the carbonization chamber, the overflow modes of the generated gas are different, the gas can be partially retained when meeting resistance, and the chemical yield is reduced; in addition, the longer the coking time, the longer the gas residence time in the furnace, and the reduced product yield.
Preferably, the predictive model incorporates the composition of the coal petrography.
Preferably, the net gas yield is correlated to tar yield, crude benzene yield.
Example 1:
the experimental furnace type is 7.63m, the coking time is 25 hours, and the dry-based volatile component Vd22.05, a coal caking index G of 78.1 and an active component E of 70.
In actual production, the tar yield was 2.56, the crude benzene yield was 0.70, and the net gas yield was 309.5.
Calculating the yield through a chemical product yield prediction model:
table 1 shows the coefficients of the effect of the oven profile and coking time on tar yield from a = c × d
a=0.85*0.8=0.68
Table 2 shows the influence of the furnace type on the crude benzene yield, as measured by b =0.95 × c
b=0.95*0.85=0.8075
The tar yield (%) was calculated: = 0.68 x [0.18 x Vd*（G+0.25*E）+0.8]/100
=0.68*[0.18*22.05*（78.1+0.25*70）+0.8]/100
=2.58
The crude benzene yield (%) = 0.8075 [0.043 ] V was calculatedd*（G+0.18*E）+0.6]/100
=0.8075*[0.043*22.05*（G+0.18*70）+0.6]/100
=0.694
Calculating the net gas yield (Nm)3）= 0.69*[22.05-0.95*（2.58+0.694）]/0.04849
=310.6
In this example, the volatile component V is on a dry basisdThe determination of the bonding index G and the active component E is carried out according to the relevant national standards or by the conventional detection means of bronze drums.
Although only the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art, and all changes are encompassed in the scope of the present invention.
Claims (1)
1. A coking chemical product yield prediction model is characterized in that: the method comprises the following steps of predicting tar yield, crude benzene yield and coal gas yield, wherein the prediction model comprises the following steps:
tar yield (%) [ a ] α1*Vd*(G+α2*E)+α3]/100
Crude benzene yield (%) < b > β1*Vd*(G+β2*E)+β3]/100
Net gas yield (Nm)3)：k*[Vd- γ x (tar yield + crude benzene yield)]/0.04849
In the formula, a is the influence coefficient of the furnace type and the coking time on the tar yield; b is the influence coefficient of the furnace type on the yield of the crude benzene; vdIs dry-based volatile component, G is coal caking index, E is active component mass percent in coal rock, α1、α2、α3；β1、β2、β3K, γ are coefficients;
coefficient α1、α2、α3；β1、β2、β3K, γ are obtained by the following formula:
when V is more than or equal to 27dWhen < 30, α1、α2、α30.22, 0.28 and 1.0 respectively;
β1、β2、β30.055, 0.12, 0.5, respectively;
k. gamma is 0.75 and 0.9 respectively;
when V is more than or equal to 24dWhen < 27, α1、α2、α30.21, 0.28, 0.4 respectively;
β1、β2、β30.053, 0.10 and 0.3 respectively;
k. gamma is 0.73 and 0.9 respectively;
when V is more than or equal to 18dWhen < 24, α1、α2、α30.18, 0.25, 0.8, respectively;
β1、β2、β30.043, 0.18 and 0.6 respectively;
k. gamma is 0.69 and 0.95 respectively;
the influence coefficient a of the furnace type and the coking time on the tar yield and the influence coefficient b of the furnace type on the crude benzene yield are obtained according to the following formula:
the influence coefficient of the furnace type and coking time on tar yield a ═ c × d
The influence coefficient b of the furnace type on the yield of crude benzene is 0.95 x c
Wherein c is a furnace type coefficient, and d is a coking time coefficient;
the furnace type coefficient is shown in Table 1, and the coking time coefficient is shown in Table 2:
TABLE 1 furnace type factor
TABLE 2 coking time coefficient
。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000077120A3 (en) * | 1999-06-10 | 2001-10-04 | Univ Wyoming Res Corp Doing Bu | Predicting proximity to coke formation |
| CN102044051A (en) * | 2010-12-07 | 2011-05-04 | 江苏沙钢集团有限公司 | Coking coal byproduct value prediction method |
| CN102175710A (en) * | 2011-02-25 | 2011-09-07 | 首钢总公司 | Method for predicting yield of coal tar |
| CN102890144A (en) * | 2012-10-22 | 2013-01-23 | 辽宁科技大学 | Method for predicting coke quality through nonlinear optimization coal blending based on coal rock vitrinite total reflectance |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000077120A3 (en) * | 1999-06-10 | 2001-10-04 | Univ Wyoming Res Corp Doing Bu | Predicting proximity to coke formation |
| CN102044051A (en) * | 2010-12-07 | 2011-05-04 | 江苏沙钢集团有限公司 | Coking coal byproduct value prediction method |
| CN102175710A (en) * | 2011-02-25 | 2011-09-07 | 首钢总公司 | Method for predicting yield of coal tar |
| CN102890144A (en) * | 2012-10-22 | 2013-01-23 | 辽宁科技大学 | Method for predicting coke quality through nonlinear optimization coal blending based on coal rock vitrinite total reflectance |
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