KR101972673B1 - Method for Predicting Spontaneous Ignition and Thermal Conductivity of Coal Storage Using HMM - Google Patents
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- 230000002269 spontaneous effect Effects 0.000 title claims abstract description 25
- 239000003245 coal Substances 0.000 title abstract description 11
- 238000000034 method Methods 0.000 title abstract description 11
- 238000007254 oxidation reaction Methods 0.000 claims abstract description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 4
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 4
- 239000001301 oxygen Substances 0.000 claims abstract description 4
- 238000010304 firing Methods 0.000 claims description 18
- 230000007704 transition Effects 0.000 claims description 11
- 229910052709 silver Inorganic materials 0.000 claims description 6
- 239000004332 silver Substances 0.000 claims description 6
- 229910052799 carbon Inorganic materials 0.000 description 3
- 239000000428 dust Substances 0.000 description 3
- 238000004880 explosion Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 239000011280 coal tar Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000008654 plant damage Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
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Abstract
Description
본 발명은 HMM(Hidden Markov Model)을 이용한 저탄장 자연발화 예측 방법에 관한 것이다.The present invention relates to a low-firing spontaneous ignition prediction method using a HMM (Hidden Markov Model).
저탄장(Coal stockpile)의 자연발화(Spontaneous ignition)에 의한 피해는 석탄 화력발전소의 저급탄 사용량이 증가에 비례하여 자주 발생하며 심각한 경제적 손해뿐만 아니라 화재 시 발생하는 유해가스로 인한 환경오염을 야기하는 대표적인 발전소 피해이다.The damage caused by spontaneous ignition of coal tar stockpiles is often caused by the increase in the amount of low carbon used in coal-fired power plants. It is not only serious economic damage but also a typical example of causing environmental pollution Power plant damage.
업계에서는 저탄장 자연발화 문제를 해결하고자 물리적 또는 화학적인 저탄장 자연발화 억제 방안이 제시되고 있음에도 고가의 설비구축 및 운용비용과 효과가 보장되지 않아 현장에서는 어려움을 겪고 있다. 일반적으로 석탄(Coal)을 연료로 전력을 생산하고 있는 화력 발전소에는 500MW당 대략 180ton/hr의 석탄을 연소하며 미분기 1대당 대략 37ton에 상당하는 석탄을 보일러에 공급한다. 석탄을 사용하는 500MW의 화력발전소에는 대략 500ton 용량의 석탄 저장소가 대략 6개가 설치되고, 그 중 5개는 정상적인 석탄의 공급이 이루어지며, 나머지 1개는 예비로 일정기간 동안 사용할 수 있는 석탄을 비축하는 저탄장으로 운영되고 있다. 석탄을 야외에 적재하면 분진가루가 날려 작업장의 환경을 오염 시키고 바람에 의해 원료가 일부 손실되는 문제점이 있어 근래에는 주로 밀폐된 저장고에 보관하고 있어 자연발화의 문제는 더 커지고 있다.In order to solve low-carbon spontaneous ignition problem, the industry has presented physical or chemical low-spontaneous spontaneous ignition restraint, but it has difficulty in the field because it does not guarantee the cost and effect of expensive facility construction and operation. Generally, a coal-fired thermal power plant burns about 180 tons / hr of coal per 500 MW and supplies about 37 tons of coal to each boiler. A 500 MW coal-fired thermal power plant will have approximately six 500-ton capacity coal reservoirs, five of which will be supplied with normal coal, and the other will have a reserve coal reserve And is operated with low-fidelity. The problem of spontaneous ignition is getting bigger because coal is put outdoors and dust dust is blown to pollute the environment of the workplace and a part of the raw material is lost by the wind.
석탄은 다공성 물질이기 때문에 단위 무게 당 산소의 흡착량이 많아 산화 반응하기 쉬우며 열전도도가 낮아 발생열의 방출도 어렵다. 그리고 자연 발화 과정에서 생성되는 가스는 외부로 방출되지 않을 경우 가스 폭발의 원인이 되며 저장설비 내의 자연발화는 분진 폭발을 유도할 수도 있다. 이처럼 석탄을 보관하는 공간에는 자연발화방지를 위한 안전관리가 매우 중요하며 화재 발생에 대한 대책이 필요하다. Since coal is a porous material, the adsorption amount of oxygen per unit weight is large, so that oxidation reaction is easy and the thermal conductivity is low, so that the generated heat is difficult to release. And, the gas generated in the spontaneous ignition process causes gas explosion if not released to the outside, and spontaneous ignition in the storage facility may lead to dust explosion. In this way, safety management for prevention of spontaneous ignition is very important in the space where coal is stored and countermeasures against fire occurrence are needed.
상기와 같은 문제점을 해결하기 위한 본 발명의 목적은 저탄장에서 발생하는 자연발화를 사전에 방지하기 위한 HMM을 이용한 저탄장 자연발화 예측 방법을 제공하는 데 있다.An object of the present invention is to provide a method for predicting low-firing spontaneous firing using an HMM to prevent spontaneous firing occurring in low-firing.
상기와 같은 목적을 달성하기 위한 본 발명의 HMM을 이용한 저탄장 자연발화 예측 방법은 저탄장의 산소농도, 내외부 압축성 유동, 및 산화반응율을 측정하는 단계; 측정값을 마르코프 모델인 TPM에 입력하는 단계; 및 TPM 값을 마르코프 모델인 EPM에 입력하여 자연발화 확률을 예측하는 단계;를 포함하는 것을 특징으로 하는 한다.According to another aspect of the present invention, there is provided a method for predicting low-firing spontaneous ignition using HMM, the method comprising the steps of: measuring low oxygen concentration, internal and external compressible flow, and oxidation reaction rate; Inputting a measured value to a TPM which is a Markov model; And inputting the TPM value to the Markov model EPM to predict a spontaneous firing probability.
상기 자연발화 확률은 수학식 1을 통해 예측하는 것을 특징으로 한다.The natural firing probability is predicted through Equation (1).
여기서, 
이상과 같이, 본 발명에 따르면 자연발화를 방지하기 위해 HMM(Hidden Markov Model) 알고리즘을 이용해서 미리 예측하여 저탄장에서 발생하는 자연발화를 사전에 방지할 수 있다. As described above, according to the present invention, in order to prevent spontaneous ignition, a spontaneous ignition occurring in low-leaning can be prevented in advance by predicting using a HMM (Hidden Markov Model) algorithm.
도 1은 본 발명에 따른 HMM을 이용한 저탄장 자연발화 예측 방법을 나타낸 흐름도이다.1 is a flowchart illustrating a method for predicting low-firing spontaneous firing using an HMM according to the present invention.
아래에서는 첨부한 도면을 참고로 하여 본 발명의 실시예에 대하여 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 상세히 설명한다. 그러나 본 발명은 여러 가지 상이한 형태로 구현될 수 있으며 여기에서 설명하는 실시예에 한정되지 않는다.Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
이하에서는 본 발명의 일실시예에 따른 HMM을 이용한 저탄장 자연발화 예측 방법에 대하여 설명한다.Hereinafter, a low-firing spontaneous speech prediction method using the HMM according to an embodiment of the present invention will be described.
도 1은 본 발명에 따른 HMM을 이용한 저탄장 자연발화 예측 방법을 나타낸 흐름도이다.1 is a flowchart illustrating a method for predicting low-firing spontaneous firing using an HMM according to the present invention.
도 1을 참조하면, 본 발명의 HMM을 이용한 저탄장 자연발화 예측 방법은, 먼저 자연발화를 방지 및 예측하기 위해서 저탄장 산소농도, 내외부 압축성 유동(내외부 저탄장 공기 유동), 산화반응율을 측정한다(S100).Referring to FIG. 1, the low-carbon spontaneous ignition prediction method using the HMM of the present invention first measures low-carbon-oxygen concentration, internal and external compressible flow (internal and external low-air-entrained air flow), and oxidation reaction rate to prevent and predict spontaneous ignition (S100) .
이어서, 상기 측정값을 마르코프 모델인 TPM(Transition Probability Matrix)에 입력한다(S110). Then, the measured value is input to a TPM (Transition Probability Matrix) which is a Markov model (S110).
다음으로, 상기 TPM 값을 마르코프 모델인 EPM(Emission Probability Matrix)에 입력하여 수학식 1을 통해 자연발화 확률을 예측할 수 있다(S120).Next, the TPM value may be input to an EPM (Emission Probability Matrix), which is a Markov model, to predict a spontaneous firing probability (S120).
[수학식 1][Equation 1]
여기서, 
참고로, 상기 TPM은 히든 상태(Hidden State)가 전이할 확률이고, EPM은 Observable event가 전이할 확률이다. 이때, 히든 상태란 우리가 눈으로 직접 확인할 수 있는 값으로 히든 상태를 예측하는데 쓰이는 값이다. 히근 상태가 한 상태에서 다른 상태로 전이할 확률을 TPM이라 한다. 그리고 Observable event가 한 상태에서 다른 상태로 전이할 확률을 EPM이라 한다. For reference, the TPM is a probability that a hidden state will transition, and EPM is a probability that an observable event will transition. At this time, the hidden state is a value that can be directly visually confirmed by us, and is used to predict the hidden state. The probability of transition from one state to another is called the TPM. The probability that an observable event will transition from one state to another is called EPM.
이상에서는 본 발명의 바람직한 실시예에 대하여 도시하고 설명하였지만, 본 발명은 상술한 특정의 실시예에 한정되지 아니하며, 청구범위에서 청구하는 본 발명의 요지를 벗어남이 없이 당해 발명이 속하는 기술분야에서 통상의 지식을 가진자에 의해 다양한 변형실시가 가능한 것은 물론이고, 이러한 변형실시들은 본 발명의 기술적 사상이나 전망으로부터 개별적으로 이해되어져서는 안될 것이다.While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the invention as defined by the appended claims. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (2)
측정값을 마르코프 모델인 TPM에 입력하는 단계; 및
TPM 값을 마르코프 모델인 EPM에 입력하여 자연발화 확률을 예측하는 단계;를 포함하되,
상기 자연발화 확률은 수학식 1을 통해 예측하는 것을 특징으로 하는 HMM을 이용한 저탄장 자연발화 예측 방법.
[수학식 1]
여기서,
Inputting a measured value to a TPM which is a Markov model; And
And inputting the TPM value to the Markov model EPM to predict a spontaneous firing probability,
Wherein the spontaneous ignition probability is predicted through Equation (1).
[Equation 1]
here,
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| JP2003322629A (en) * | 2002-05-02 | 2003-11-14 | Hitachi Zosen Corp | Method for detecting natural ignition state of activated carbon |
| KR101364000B1 (en) * | 2012-11-14 | 2014-02-17 | 한국에너지기술연구원 | Coal spontaneous combustion measuring apparatus |
| JP2015132575A (en) * | 2014-01-15 | 2015-07-23 | 株式会社神戸製鋼所 | Coal spontaneous combustion prediction method |
| KR20170067292A (en) * | 2015-12-08 | 2017-06-16 | 한양대학교 산학협력단 | Device and method for estimating remaining life of mechanical system |
| JP2017138166A (en) * | 2016-02-02 | 2017-08-10 | 千代田化工建設株式会社 | Coal yard monitoring system and coal yard monitoring method |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003322629A (en) * | 2002-05-02 | 2003-11-14 | Hitachi Zosen Corp | Method for detecting natural ignition state of activated carbon |
| KR101364000B1 (en) * | 2012-11-14 | 2014-02-17 | 한국에너지기술연구원 | Coal spontaneous combustion measuring apparatus |
| JP2015132575A (en) * | 2014-01-15 | 2015-07-23 | 株式会社神戸製鋼所 | Coal spontaneous combustion prediction method |
| KR20170067292A (en) * | 2015-12-08 | 2017-06-16 | 한양대학교 산학협력단 | Device and method for estimating remaining life of mechanical system |
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