JP7119710B2 - Coke intensity control method - Google Patents

Description

TECHNICAL FIELD The present invention relates to a coke strength control method, and more particularly, to a technique for setting sampling conditions and quality test conditions for measuring coke strength as quality control of coke produced in the operation of a coke oven.

In order to control the quality of coke produced by carbonizing coal in a coke oven, the produced coke is periodically sampled and the quality of the obtained sample is analyzed. Based on the analysis results of the samples, it is confirmed whether or not the coke that satisfies the required quality is being produced, and the operation of the coke oven and the blending control of coal for producing coke are carried out.

For example, in Patent Document 1, as a method for quality control of dry quenching coke in a coke oven, an average residence time in coke dry quenching equipment (CDQ: Coke Dry Quenching) is obtained, and based on the average residence time For each kiln-out group Sampling the extinguished coke at the timing of sampling the coke in the kiln between the 3rd and 4th after the start of the kiln out of the group and the 3rd and 4th before the end of the kiln out, and based on the analysis results of the coke, the kiln out group It is disclosed to control the furnace temperature and recipe for each furnace. In Patent Literature 1, the average coke residence time in the coke dry fire extinguishing equipment is obtained, and based on this, the coke of a specific kiln output group is reliably sampled, thereby managing the coke oven for each kiln output group. This makes it possible to determine whether the cause of coke quality fluctuation is due to the combustion state or coal blending.

JP-A-59-179583

In Patent Literature 1, after being extinguished by the coke dry fire extinguishing equipment, the coke transported by the coke transport conveyor under the coke dry fire extinguishing equipment is sampled to analyze the quality of the produced coke. However, when coke quality fluctuations occur, there is no mention of factors other than combustion conditions and coal blending. That is, no mention is made of the sampling conditions for coke sampling and the quality test conditions for measuring the quality of samples, which are set for analyzing the quality of coke. For this reason, in the method described in Patent Document 1, if the reason for the coke grade fluctuation is in the sampling conditions or the quality test conditions, the factor cannot be specified, and measures to properly perform quality control are taken. can't take

Therefore, the present invention has been made in view of the above problems, and the object of the present invention is to set appropriate sampling conditions and quality test conditions in managing the quality of coke. To provide a coke strength control method.

In order to solve the above problems, according to one aspect of the present invention, there is provided a method for controlling the strength of coke produced in a coke oven, wherein multiple samples are prepared by sampling coke in the same lot, and a plurality of A sampling step of generating a sample, a coke strength measurement step of performing a coke strength test on the collected sample to measure the coke strength, intra-lot variation in the measured coke strength, and intra-lot variation In addition to calculating the test variability due to the test of the sample, based on the intra-lot variability and the test variability, the variability calculation step of calculating the non-test variability of the intra-lot variability other than the test variability, and the test variability and the non-test variability Analysis of test conditions for acquiring the relationship between the number of times of sampling for performing the sampling process and the number of times of testing for performing the coke strength measurement process for each sample collected in the sampling process, based on the variation within the lot, based on the factor variation. and a coke intensity management system is provided.

Here, the relationship between intra-lot variation σ ₁ , test variation σ _1-1 , non-test factor variation σ _1-2 , sampling number k, and test number m is expressed by the following equation.

In the test condition analysis step, the obtained relationship may be represented by creating a diagram showing the relationship between the number of sampling times and intra-lot variation according to the number of times of testing.

In addition, the coke strength control method sets the number of times of sampling and the number of tests that satisfy the target variation within the lot based on the relationship between the number of times of sampling and the number of tests regarding the variation within the lot obtained in the test condition analysis process. and a test condition setting step.

At this time, in the test condition setting step, the number of times of sampling and the number of times of testing may be set based on the ratio of the test variation and the non-test factor variation in the intra-lot variation.

As described above, according to the present invention, it is possible to set appropriate sampling conditions and quality test conditions in controlling the quality of coke.

It is an explanatory view for explaining an alternate sampling method. FIG. 10 is a flow chart showing a process of obtaining a relationship between the number of times of sampling k and the number of times of testing m regarding intra _- lot variation σ1; FIG. FIG. 10 is an explanatory diagram showing test variation σ _1-1 and non-test factor variation σ _1-2 of each furnace in an example; 10 is a graph showing the relationship between the number of times of sampling k and the number of times of testing m with respect to intra-lot variation σ ₁ of each furnace in an example.

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description. In the present invention, a multiple sample adjusted by sampling coke in the same lot is basically a double sample, and hereinafter the multiple sample will be described as a double sample.

<1. Overview>
Quality control of coke is performed by sampling coke produced in a coke oven and analyzing the quality of the sample obtained by sampling. In this embodiment, coke strength is measured as a quality characteristic value representing the quality of coke, and the quality of coke is managed. In the coke strength control method according to one embodiment of the present invention, in controlling the quality of coke, appropriate sampling conditions and quality test conditions are set. For each sample collected by sampling, the relationship with the number of tests is acquired as a quality test condition. Based on this relationship, the sampling conditions (number of sampling times) and quality test conditions (number of tests) required to reach the target intra-lot variation are set, and coke quality control is performed accordingly.

In the present embodiment, in order to perform quality control, in a time unit (hereinafter also referred to as “lot time”) for collecting a sample for measuring coke strength at least once, it is manufactured within that time. A unit of coke production is defined as a “lot”. The lot time is usually set to about 1 day to 4 hours.

<2. Coke sampling and measurement of coke strength (quality measurement test)>
Before describing the coke strength control method according to the present embodiment, first, a quality measurement test for measuring coke sampling and coke strength will be described with reference to FIG. FIG. 1 is an explanatory diagram for explaining an alternate sampling method, which is one of coke sampling methods.

[2-1. Coke sampling]
Coke sampling is performed based on the method specified in JIS-M8811 (Coals and cokes-Sampling and sample preparation method). In this embodiment, the coke is sampled based on the alternate sampling method. In the alternate sampling method, as shown in FIG. 1, when coke is sampled at equal intervals for each sampling time in one lot, the sampled coke is alternately divided into two sample aggregates Ai and Bi. create. This sample set Ai, Bi is hereinafter referred to as "alternating samples". It is recommended that the number of times of sampling in one lot for forming alternate samples be 8 times or more, preferably 10 times or more.

Next, in this embodiment, a plurality of samples are created from the created alternate samples Ai and Bi. Specifically, first, the coke of each alternating sample Ai, Bi is separated into respective predetermined particle size categories. Although the particle size division to be separated is not particularly limited, for example, it is separated into four divisions of more than 75 mm, 75 to 50 mm, 50 to 38 mm, and 38 to 25 mm. Then, the proportion of coke in each section contained in the alternate samples is calculated, and a plurality of samples are prepared as specimens for strength measurement according to the proportion. For example, in the example of FIG. 1, two samples Ai-1 and Ai-2 are created from alternate sample Ai, and two samples Bi-1 and Bi-2 are created from alternate sample Bi. This pair of samples is called a double sample. A double sample may be created by creating three or more samples from the alternate samples Ai and selecting two of them.

As a specific example, for example, in FIG. 1, the lot time is set to 8 hours, and sampling is performed 8 times within the lot. Since it is preferable to set the sampling time at equal intervals, in the case of FIG. 1, it is recommended that the sampling time is set to, for example, one hour. 12 Kg of coke is collected in one sampling. As a result, as shown in FIG. 1, a 48 kg alternating sample Ai made of odd-numbered coke and a 48-kg alternating sample Bi made of even-numbered coke are produced. Thereafter, after separating the coke of each alternating sample Ai, Bi into respective predetermined grain size classes, similarly composed samples are produced based on the proportion of coke of each grain size class in the alternating samples. For example, if two 10 kg samples are made from alternating 48 kg samples, the proportion of coke in each grain size class in each 10 kg sample is adjusted to be similar to the proportion of coke in each grain size class in the alternating samples. .

Thus, based on the alternate sampling method, duplicate samples can be produced for coke strength measurements.

According to JIS-M8811, the alternate sampling method is to be performed a plurality of times (at least 10 times). Since it is preferable to perform sampling by selecting a day on which coal, which is the raw material of coke, has the same composition and the same operating conditions, it is preferable to perform alternate sampling continuously. However, alternate sampling does not necessarily have to be performed continuously, and may be performed intermittently, for example.

[2-2. Coke strength index]
As an index of coke strength, the drum strength described in JIS-K2151 is used. The drum strength was measured by charging a predetermined amount of coke (10 kg) into a cylindrical drum with a diameter of 1500 mm and a length of 1500 mm, rotating it 30 or 150 times at 15 rpm, and then sieving it with 50 mm, 25 mm, 15 mm, and 6 mm sieves. The strength for each rotation is expressed as a percentage of the sieved mass to the charged mass. In general, the 15 mm sieve mass percentage (15 mm) index after 150 revolutions is widely used and is denoted as DI ¹⁵⁰ ₁₅ .

In the coke strength control method according to the present embodiment as well, double samples taken based on the alternate sampling method are used as samples, and the drum strength is measured and used as a coke strength index.

[2-3. Variation in coke strength]
In this embodiment, in order to properly control the coke quality, it is desirable that the measured quality, ie the coke intensity, has small variations. Therefore, the coke strength of the lot is determined so that the variation in coke strength of each sample made from the same lot of coke falls within the target intra-lot variation. The setting of the target intra-lot variation σ ₁ ′ and the method of calculating the intra-lot variation σ ₁ , the test variation σ _1-1 and the non-test factor variation σ _1-2 will be described below. Specifically, as shown in FIG. 1, a method of preparing duplicate samples from alternate samples taken based on the alternate sampling method, measuring the coke strength, and calculating the coke strength will be described.

(Target intra-lot variation σ ₁ ')
The target intra-lot variation σ ₁ ′ is determined, for example, based on the quality required for coke and the results of intra-lot variation for past operating conditions. Here, the intra-lot variation σ ₁ is divided into test variation σ _1-1 caused by the test of the sample and non-test factor variation σ _1-2 caused by factors other than the test variation σ _1-1 . The test variation σ _1-1 includes variation in sampling of coke, variation in preparation when making duplicate samples, variation in coke strength measurement test, and the like. On the other hand, the non-test factor variation σ _1-2 includes variation in the soundness (combustibility) of the coke oven body (in-kiln/between-kiln variation).

In the ideal situation, the intra-lot variation σ ₁ is such that the test variation σ _1-1 is at the “unavoidable variation of the drum test itself” and the variation due to factors other than the test σ _1-2 (that is, the coke oven body (kiln ) is extremely small. However, it is difficult to continuously realize such a situation at the operation site. For this reason, we conducted an alternate sampling test to obtain the intra-lot variation σ ₁ during the period when operation was stable in a furnace group with a relatively sound furnace body, such as a new furnace. It is realistic to set the internal variation to σ ₁ ′.

(Intra-lot variation σ ₁ )
The intra-lot variation σ ₁ is calculated from the following formula (1) based on JIS-M8811.

Here, n' is the number of increments forming alternating samples Ai and Bi, and n'=4 in the example shown in FIG.
R is the average value of the range of paired measured values R _i and is represented by the above formula (1-1). The range R _i of the paired measured values is the absolute value of the difference between the coke intensity DI _(Ai) of the alternate sample Ai and the coke intensity DI _(Bi) of the alternate sample Bi, as represented by the above formula (1-2). be.
n is the number of ranges R _i and corresponds to the number of times a test based on a series of alternating sampling methods for measuring coke strength was performed using samples sampled at the lot times shown in FIG. For example, if a series of alternating sampling tests measuring coke strength for samples sampled at the lot times of FIG.
_d2 is a coefficient that estimates the standard deviation from the range R; For example, in the case of two data, 1/d ₂ =0.8862.

(test variation σ _1-1 )
The test variation σ _1-1 is calculated from the following formula (2) based on JIS-M8811.

where X is the measured difference in coke strength of the duplicate samples. _np is the number of pairs of double samples. For example, as shown in FIG. 1, duplicate samples Ai-1 and Ai-2 are made from alternating samples, where X is the coke strength measurement of duplicate sample Ai-1 and the coke intensity of duplicate sample Ai- It is expressed by the difference from the measured value of coke strength of No. 2. Also, _np is two. Similarly, X can also be determined from the difference between the measured coke strength of duplicate sample Bi-1 and the measured coke strength of duplicate sample Bi-2. That is, for each of the alternating samples Ai and Bi, the test variation σ _1-1 is calculated based on the formula (2), and the obtained values are averaged to obtain the final test variation σ _1-1 . good too.

Similarly, X can also be determined by the difference between the measured coke strength of duplicate sample Bi-1 and the measured coke strength of duplicate sample Bi-2. That is, for each of the alternate samples Ai and Bi, σ _1-1 is calculated based on the above equation (2), and the obtained values are averaged to obtain the final σ _1-1 .

(Non-test factor variation σ _1-2 )
Non-test factor variability σ _1-2 is calculated from the following formula (3) based on the intra-lot variability σ ₁ calculated from the above formula (1) and the test variability σ _1-1 calculated from the above formula (2). be done. The non-test factor variation σ _1-2 can be obtained by the following (3) because it is the within-lot variation σ ₁ other than the test variation σ _1-1 .

<3. Setting of sampling conditions and quality test conditions>
As a coke quality control, coke is sampled to measure the coke strength as described above, but the coke quality varies depending on the coke oven in which the coke is produced. Therefore, it is not appropriate to measure the coke strength under the same sampling conditions and the same quality test conditions for all coke ovens. Therefore, in the coke strength control method according to the present embodiment, the sampling conditions and quality test conditions that make the intra-lot variation σ ₁ the target intra _- lot variation σ ₁ ′ are set to the sampling number k and the test It is set by acquiring the relationship with the number of times m. A process for acquiring the relationship between the number of times of sampling k and the number of times of testing m for the intra-lot variation σ ₁ will be described below with reference to FIG. 2 .

In order to obtain the relationship between the number of samples k and the number of tests m related to the intra-lot variation σ ₁ , first, the intra-lot variation σ ₁ , the test variation σ _1-1 , and the variation due to factors other than the test, which are necessary for obtaining the relationship. Calculate σ _1-2 . That is, as shown in FIG. 2, first, coke is sampled within one lot to prepare a sample (S10: sampling step). In this embodiment, for example, as shown in FIG. 1, coke is sampled by an alternate sampling method to create a double sample. Next, the coke strength is measured for each of the double samples prepared in step S10 (S20: coke strength measurement step). As the strength of the coke strength, the drum strength described in JIS-K2151 mentioned above is used.

When the coke strength of each sample is measured in step S20, based on the results of the sampling step of step S10 and the coke strength measurement step of step S20, the intra-lot variation σ ₁ , the test variation σ _1-1 , and Non-test factor variation σ _1-2 is calculated (S30, S40: variation calculation step). First, the intra-lot variation σ ₁ and the test variation σ _1-1 are calculated (S30). The intra-lot variation σ ₁ is calculated from the above equation (1), and the test variation σ _1-1 is calculated from the above equation (2). Then, using the calculated intra-lot variation σ ₁ and test variation σ _1-1 , non-test factor variation σ _1-2 is calculated from the above equation (3) (S40).

After that, based on the intra-lot variation σ ₁ , the test variation σ _1-1 , and the non-test factor variation σ _1-2 calculated in steps S30 and S40, the number of sampling times k and the number of testing times m for the within-lot variation σ ₁ is acquired (S50: test condition analysis step). The test variation σ _1-1 can be reduced by increasing the number of tests, and the non-test factor variation σ _1-2 can be reduced by increasing the number of samplings. From this, when the number of times of coke sampling in one lot is k and the number of tests for measuring the coke strength using the sampled coke is m, the intra-lot variation σ ₁ , the test variation σ _1-1 , and the test The difference between the non-factor variation σ _1-2 can be expressed by the following equation (4).

Therefore, in the present embodiment, the test variation σ _1-1 calculated in step S30 and the non-test factor variation σ _1-2 calculated in step S40 are substituted into the above equation (4), and the intra-lot Obtain the relationship between the _number of sampling times k and the number of testing times m for the variation σ1. Based on this relationship, it is possible to grasp one or more combinations of the number of times of sampling k and the number of times of testing m that make the intra-lot variation σ ₁ the target intra-lot variation σ ₁ ′. The relationship between the number of sampling times k and the number of testing times m with respect to the intra-lot variation σ ₁ is, for example, the sampling number k and the intra-lot variation σ when a specific value (m1, 2, . ₁ , and the obtained relational expression may be shown as a diagram (for example, a graph). This makes it possible to easily recognize a change in intra-lot variation σ ₁ due to a change in the number of sampling times k in a certain number of tests.

Based on the relationship between the number of sampling times k and the number of testing times m for the within-lot variation σ ₁ obtained in step S50, the sampling number k and the testing number m are used to achieve the target within-lot variation σ ₁ ′ for the within-lot variation σ ₁ . If there is one combination of , set that combination, and if there are two or more, set any combination (test condition setting step). If there are two or more sampling times k and testing times m, it is preferable to select a combination that is more compatible. A positive integer is set for both the number of times of sampling k and the number of times of testing m. The set number of times of sampling k and the number of times of testing m may be reviewed as necessary, for example, once a year.

The processing of steps S40 and S50 can also be executed by an information processing device such as a computer by inputting the data obtained in steps S10 to S30. Alternatively, the information processing device executes the process of step S40 to obtain the relationship between the number of sampling times k and the number of testing times m for the intra _- lot variation σ1, and displays the obtained relationship in a graph or the like on a display or the like. You may do so. In this case, the operator may confirm the displayed relationship and determine the number of times of sampling k and the number of times of testing m.

Also, in setting the number of times of sampling k and the number of times of testing m, the ratio of the test variation σ _1-1 and the non-test factor variation σ _1-2 in the intra-lot variation σ ₁ may be considered. By setting the number of times of sampling k and the number of times of testing m so as to improve the variation that greatly affects the within-lot variation σ ₁ , the within-lot variation σ ₁ can be effectively reduced. For example, if the test variation σ _1-1 is larger than the non-test factor variation σ _1-2 , the number of times of testing m should be increased. Further, when the test variation σ _1-1 is larger than the non-test factor variation σ _1-2 , the number of times of sampling k should be increased.

The coke strength control method according to the present embodiment has been described above. According to this embodiment, duplicate samples are taken by alternate sampling within a lot and a coke strength measurement test is performed. Then, the intra-lot variation σ ₁ and test variation σ _1-1 of coke strength are calculated, and from the intra-lot variation σ ₁ and test variation σ _1-1 of coke strength, non-test factor variation σ _{1 -2} is calculated. From the obtained variations σ ₁ , σ _1-1 , and σ _1-2 , the relationship between the number of times of sampling k and the number of times of testing m within one lot is acquired. Based on this relationship, the number of times of sampling k and the number of times of testing m required to obtain the target intra-lot variation σ ₁ ′ are set, and subsequent quality control is performed.

As a result, it is possible to easily obtain the number of times of sampling k and the number of times of testing m required to achieve the target intra-lot variation σ ₁ ', without increasing the number of times of sampling k and the number of times of testing m more than necessary. It is possible to set the optimum sampling conditions and quality test conditions that are just right for each coke oven.

Based on the coke strength management method shown in FIG. 2 described in the above embodiment, for three different coke ovens (A oven, B oven, C oven), the intra-lot variation σ ₁ ', the number of times of sampling k and the number of times of testing m required to obtain .

For coke sampling, two alternate samples were prepared based on the alternate sampling method shown in FIG. 1, and duplicate samples were prepared from each alternate sample. Then, the coke strength of the obtained double samples was measured, and the intra-lot variation σ ₁ , test variation σ _1-1 , and non-test factor variation σ _1-2 of coke strength were calculated. The test variation σ _1-1 and the non-test factor variation σ _1-2 of each furnace at this time were as shown in FIG. 3 and Table 1 below. The target intra-lot variation σ ₁ ′ is set for each furnace as shown in Table 1 below.

Next, using the test variation σ _1-1 and the non-test factor variation σ _1-2 in Table 1, the relationship between the number of sampling times k and the number of testing times m for the intra-lot variation σ ₁ is obtained from the above equation (4). . Then, for each furnace, the relational expression between the intra-lot variation σ ₁ and the number of sampling times k is It was obtained and graphed as shown in FIG. The hatched area in each graph of FIG. 4 is an area that satisfies the target intra-lot variation σ ₁ ′. The points (P _A1 , P _B1 , P _C1 ) indicated by white circles represent the number of times of sampling and the number of times of testing currently set.

Looking at the results in FIG. 4, _first , for furnace A, the current number of sampling times and the number of testing times are both one. It is considered that it has not been done. Therefore, by increasing the number of times of sampling to 3 times and the number of times of testing to 2 times and slightly improving the conditions as shown at point _PA2 , for example, it is possible to satisfy the target intra-lot variation σ ₁ '.

For furnace B, the current number of samplings is four and the number of tests is one. Here, as shown in FIG. 3 and Table 1, for furnace B, the test variation σ _1-1 is larger than the non-test factor variation σ _1-2 . From this, it is considered that the within-lot variation σ ₁ can be greatly improved by increasing the number of tests. As shown in FIG. 4, when the target _intra -lot variation σ ₁ ′ is not reached by increasing the number of tests alone, the target lot It is possible to satisfy the internal variability σ ₁ ′.

For furnace C, the current number of sampling times is eight and the number of tests is one. As in the case of the B furnace, the test variation σ _1-1 is larger than the non-test factor variation σ _1-2 , as shown in FIG. 3 and Table 1. Therefore, by increasing the number of tests, for example, as at point P _C2 , the intra-lot variation σ ₁ can be greatly improved.

As described above, for each furnace, the number of sampling times k and the number of testing times m required to achieve the desired intra-lot variation σ ₁ ' can be easily obtained, and the number of sampling times k and the number of testing times m can be determined blindly. It is possible to set the optimum sampling conditions and quality test conditions that are just right for each coke oven without increasing the number.

Although the preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention belongs can conceive of various modifications or modifications within the scope of the technical idea described in the claims. It is understood that these also naturally belong to the technical scope of the present invention.

Claims (4)

A method for controlling the strength of coke produced in a coke oven,
A sampling step of sampling coke in the same lot to prepare multiple samples to generate a plurality of samples;
A coke strength measurement step of performing a coke strength test on the collected sample to measure the coke strength;
Calculate the within-lot variation of the measured coke strength and the test variation caused by the test of the sample among the within-lot variation, and based on the within-lot variation and the test variation, out of the within-lot variation a variation calculation step of calculating non-test factor variation other than the test variation;
Based on the test variation and the non-test factor variation, the number of times of sampling for performing the sampling step regarding the intra-lot variation, and the coke strength measurement step for each sample taken in the sampling step. A test condition analysis step of acquiring a relationship with the number of tests;
Test condition setting for setting the number of times of sampling and the number of times of testing that satisfy the target within-lot variation based on the relationship between the number of times of sampling and the number of times of testing related to the within-lot variation acquired in the step of analyzing test conditions. process and
coke intensity management methods, including; Claim 1, wherein the relationship between the intra-lot variation σ ₁ , the test variation σ _1-1 , the non-test factor variation σ _1-2 , the number of times of sampling k, and the number of times of testing m is expressed by the following formula: The coke strength control method described in .

3. The coke strength control method according to claim 1, wherein, in said test condition analysis step, a diagram representing a relationship between said number of times of sampling and said variation within a lot according to said number of times of testing is created. The test condition setting step according to any one of claims 1 to 3 , wherein the number of times of sampling and the number of times of testing are set based on a ratio of the test variation and the non-test factor variation in the intra-lot variation. coke strength control method.

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