WO2020040332A1 - Method for predicting spontaneous ignition of coal stockpile using deep neural network - Google Patents

Method for predicting spontaneous ignition of coal stockpile using deep neural network Download PDF

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WO2020040332A1
WO2020040332A1 PCT/KR2018/009768 KR2018009768W WO2020040332A1 WO 2020040332 A1 WO2020040332 A1 WO 2020040332A1 KR 2018009768 W KR2018009768 W KR 2018009768W WO 2020040332 A1 WO2020040332 A1 WO 2020040332A1
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최원혁
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한서대학교 산학협력단
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  • the present invention relates to a low carbon spontaneous combustion prediction method using a deep neural network.
  • Renewable power plants such as solar power, wind power, and nuclear power are being developed for electricity generation.
  • Thermal power plants make up a significant portion of the country's main power plants.
  • Coal which is a main resource of thermal power plants, is used as a main energy source because it has a considerably higher price-to-price energy density and useful distribution and storage than other resources.
  • bituminous coal used in coal-fired power plants is known to cause spontaneous ignition problems when low coal is burned for a long time.
  • coal-fired power plants burn about 180 tonnes / hr of coal per 500 MW and supply about 37 tonnes of coal to the boiler.
  • Approximately six 500 tonnes of coal storage are installed in a 500 MW thermal power plant, five of which provide a normal coal supply, and one is operated as a low coal reserve that stocks coal for a period of time.
  • coal Since coal is a porous material, the amount of oxygen adsorbed per unit weight is high, so it is easy to oxidize and release heat generated due to low thermal conductivity.
  • the gas produced during spontaneous ignition may cause explosion if not released to the outside, and spontaneous ignition in the storage facility may induce dust explosion. As such, safety management to prevent spontaneous ignition is very important in the space where coal is stored and measures for fire occurrence are necessary.
  • An object of the present invention for solving the above problems is to provide a low-kine spontaneous ignition prediction method using a deep neural network that can prevent the spontaneous ignition occurring in low-charging in advance.
  • Low-charging spontaneous ignition prediction method using the deep neural network of the present invention for achieving the above object comprises the steps of: inputting the temperature and environmental information associated with low-charging spontaneous combustion into the input layer node of the deep neural network; And predicting low-charging spontaneous ignition using the Sigmoid activity function at the hidden layer 1, 2 nodes of the deep neural network and outputting the result to the output layer node.
  • the input of the input layer node is characterized in that the wind speed, temperature, humidity, atmospheric pressure, and low coal internal temperature.
  • the deep neural network may be composed of an input layer, a hidden layer 1, a hidden layer 2, and an output layer of 80, 20, 10, and 1 nodes, respectively.
  • FIG. 1 is a view showing a deep neural network for predicting low-charging spontaneous ignition according to the present invention.
  • FIG. 1 is a view showing a deep neural network for predicting low-charging spontaneous ignition according to the present invention.
  • the deep neural network input may use wind speed, temperature, humidity, atmospheric pressure, low-charging internal temperature.
  • the deep neural network may include 80 input layer nodes 10, 20 hidden layer 1 nodes 20, 10 hidden layer 2 nodes 30, and 1 output layer node 40.
  • the low carbon spontaneous ignition can be predicted using the Sigmoid activity function in the hidden layer 1, 2 nodes.
  • the arrows connecting the nodes of each layer indicate the flow of signals, and the data of the previous node is delivered to the next node connected by the arrow by providing the weight. Adding all the input data multiplied by the weighted weight results in a weight sum, which generates an output value through the active function.
  • the weight may be updated by an error backpropagation method using a stochastic gradient descent method.

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Abstract

The present invention relates to a method for predicting spontaneous ignition of a coal stockpile using a deep neural network, the method comprising the steps of: inputting temperature and environmental data associated with spontaneous ignition of a coal stockpile into an input layer node of a deep neural network; and predicting spontaneous ignition using a sigmoid activation function in hidden nodes 1 and 2 of the deep neural network, and outputting to an output layer node.

Description

심층신경망을 이용한 저탄장 자연발화 예측방법Prediction of Low Carbon Natural Ignition Using Deep Neural Network
본 발명은 심층신경망을 이용한 저탄장 자연발화 예측방법에 관한 것이다. The present invention relates to a low carbon spontaneous combustion prediction method using a deep neural network.
전기 생산을 위해 태양광발전, 풍력발전, 원자력 발전 등 신재생 발전소가 꾸준히 개발되고 있다. Renewable power plants such as solar power, wind power, and nuclear power are being developed for electricity generation.
하지만 화력발전소는 국내 전기에너지를 생산하는 주력 발전소로 상당 부분을 차지하고 있다. 화력발전소의 주자원인 석탄은 다른 자원에 비해 가격대비 에너지 밀도가 상당히 높고 유통 및 저장성이 유용하기 때문에 주된 에너지 자원으로 사용되고 있다. 그러나 화력발전소에서 사용되는 유연탄은 장기간 저탄 시 자연발화의 문제가 발생하는 것으로 알려졌다.Thermal power plants, however, make up a significant portion of the country's main power plants. Coal, which is a main resource of thermal power plants, is used as a main energy source because it has a considerably higher price-to-price energy density and useful distribution and storage than other resources. However, bituminous coal used in coal-fired power plants is known to cause spontaneous ignition problems when low coal is burned for a long time.
석탄 화력발전소의 저탄장(Coal stockpile) 자연발화(Spontaneous ignition)에 의한 피해는 자주 발생하며 심각한 경제적 손실뿐만 아니라 환경오염을 일으키는 대표적인 발전소 피해이다.The damage caused by coal stockpile spontaneous ignition of coal-fired power plants is a frequent occurrence and is a representative power plant damage that causes environmental pollution as well as serious economic losses.
저탄장 자연발화 문제를 해결하고자 물리적 및 화학적인 방안이 제시되고 있음에도 고가의 설비 및 운용비용 때문에 현장에서는 어려움을 겪고 있다. 일반적으로 석탄(Coal)을 연료로 전력을 생산하고 있는 화력 발전소에는 500MW당 대략 180ton/hr의 석탄을 연소하며 미분키 1대당 대략 37ton에 상당하는 석탄을 보일러에 공급한다. 500MW의 화력발전소에는 대략 500ton 용량의 석탄 저장소가 대략 6개가 설치되고, 그 중 5개가 정상적인 석탄 공급이 이루어지며, 나머지 1개는 예비로 일정 기간 사용할 수 있는 석탄을 비축하는 저탄장으로 운영되고 있다. Although physical and chemical measures are proposed to solve the low coal spontaneous combustion problem, they are experiencing difficulties in the field due to the expensive facilities and operation costs. In general, coal-fired power plants burn about 180 tonnes / hr of coal per 500 MW and supply about 37 tonnes of coal to the boiler. Approximately six 500 tonnes of coal storage are installed in a 500 MW thermal power plant, five of which provide a normal coal supply, and one is operated as a low coal reserve that stocks coal for a period of time.
석탄은 다공성 물질이기 때문에 단위 무게당 산소의 흡착량이 많아 산화 반응하기 쉬우며 열전도도가 낮아 발생 열의 방출도 어렵다. 그리고 자연 발화 과정에서 생성되는 가스는 외부로 방출되지 않으면 폭발의 원인이 되며 저장설비 내의 자연발화는 분진 폭발을 유도할 수도 있다. 이처럼 석탄을 보관하는 공간에는 자연발화방지를 위한 안전관리가 매우 중요 하며 화재 발생에 대한 대책이 필요하다.Since coal is a porous material, the amount of oxygen adsorbed per unit weight is high, so it is easy to oxidize and release heat generated due to low thermal conductivity. In addition, the gas produced during spontaneous ignition may cause explosion if not released to the outside, and spontaneous ignition in the storage facility may induce dust explosion. As such, safety management to prevent spontaneous ignition is very important in the space where coal is stored and measures for fire occurrence are necessary.
상기와 같은 문제점을 해결하기 위한 본 발명의 목적은 저탄장에서 발생하는 자연발화를 사전에 예방할 수 있는 심층신경망을 이용한 저탄장 자연발화 예측방법을 제공하는 데 있다.An object of the present invention for solving the above problems is to provide a low-kine spontaneous ignition prediction method using a deep neural network that can prevent the spontaneous ignition occurring in low-charging in advance.
상기와 같은 목적을 달성하기 위한 본 발명의 심층신경망을 이용한 저탄장 자연발화 예측방법은, 저탄장 자연발화와 관련된 온도와 환경 정보를 심층신경망의 입력층 노드에 입력하는 단계; 및 상기 심층신경망의 은닉층1,2 노드에서 Sigmoid 활성함수를 사용하여 저탄장 자연발화를 예측하여 츨력층 노드로 출력하는 단계;를 포함하는 것을 특징으로 한다.Low-charging spontaneous ignition prediction method using the deep neural network of the present invention for achieving the above object comprises the steps of: inputting the temperature and environmental information associated with low-charging spontaneous combustion into the input layer node of the deep neural network; And predicting low-charging spontaneous ignition using the Sigmoid activity function at the hidden layer 1, 2 nodes of the deep neural network and outputting the result to the output layer node.
상기 입력층 노드의 입력은 풍속, 기온, 습도, 대기압력, 및 저탄장 내부온도인 것을 특징으로 한다.The input of the input layer node is characterized in that the wind speed, temperature, humidity, atmospheric pressure, and low coal internal temperature.
상기 심층신경망은 입력층, 은닉층1, 은닉층2, 출력층은 각각 80개, 20개, 10개, 1개 노드로 구성할 수 있다. The deep neural network may be composed of an input layer, a hidden layer 1, a hidden layer 2, and an output layer of 80, 20, 10, and 1 nodes, respectively.
상기와 같이, 본 발명에 따르면 심층신경망을 이용하여 저탄장에서 발생하는 자연발화를 미리 예측하여 사전에 예방할 수 있다.As described above, according to the present invention, it is possible to predict in advance the spontaneous ignition occurring in the low magazine using the deep neural network.
도 1은 본 발명에 따른 저탄장 자연발화 예측을 위한 심층신경망을 나타낸 도면이다.1 is a view showing a deep neural network for predicting low-charging spontaneous ignition 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. DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention.
이하에서는 본 발명의 일실시예에 따른 심층신경망을 이용한 저탄장 자연발화 예측방법에 대하여 설명한다.Hereinafter, a method for predicting low carbon spontaneous ignition using a deep neural network according to an embodiment of the present invention will be described.
도 1은 본 발명에 따른 저탄장 자연발화 예측을 위한 심층신경망을 나타낸 도면이다.1 is a view showing a deep neural network for predicting low-charging spontaneous ignition according to the present invention.
도 1을 참조하면, 본 발명에 따른 심층신경망을 이용한 저탄장 자연발화 예측방법은, 자연발화와 관련된 온도와 환경 정보를 심층신경망의 입력으로 선정한다. 여기서, 심층신경망 입력으로 풍속, 기온, 습도, 대기압력, 저탄장 내부온도를 사용할 수 있다. Referring to FIG. 1, in the method for predicting low-charging spontaneous ignition using the deep neural network according to the present invention, temperature and environmental information related to spontaneous ignition are selected as inputs of the deep neural network. Here, the deep neural network input may use wind speed, temperature, humidity, atmospheric pressure, low-charging internal temperature.
예를 들면, 상기 심층신경망은 80개의 입력층 노드(10), 20개의 은닉층1 노드(20), 10개의 은닉층2 노드(30), 1개의 출력층 노드(40)로 구성될 수 있다. 이때, 상기 은닉층1,2 노드에서 Sigmoid 활성함수를 사용하여 저탄장 자연발화를 예측할 수 있다. For example, the deep neural network may include 80 input layer nodes 10, 20 hidden layer 1 nodes 20, 10 hidden layer 2 nodes 30, and 1 output layer node 40. At this time, the low carbon spontaneous ignition can be predicted using the Sigmoid activity function in the hidden layer 1, 2 nodes.
각층의 노드들을 연결한 화살표는 각 신호들의 흐름을 나타내고 있으며, 이전 노드의 데이터는 가중치(Weight)를 제공하여 화살표로 연결된 다음 노드로 전달된다. 전달된 가중치가 곱해진 입력데이터들을 모두 더하면 가중합(weight sum)이 되어 활성함수를 통해 출력 값이 생성된다. 이때, 상기 가중치(weight)는 확률적 경사하강법을 이용한 오차역전파법으로 갱신할 수 있다. The arrows connecting the nodes of each layer indicate the flow of signals, and the data of the previous node is delivered to the next node connected by the arrow by providing the weight. Adding all the input data multiplied by the weighted weight results in a weight sum, which generates an output value through the active function. In this case, the weight may be updated by an error backpropagation method using a stochastic gradient descent method.
이상에서 본 발명의 실시예에 대하여 상세하게 설명하였지만 본 발명의 권리범위는 이에 한정되는 것은 아니고 다음의 청구범위에서 정의하고 있는 본 발명의 기본 개념을 이용한 당업자의 여러 변형 및 개량 형태 또한 본 발명의 권리범위에 속하는 것이다.Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

Claims (3)

  1. 저탄장 자연발화와 관련된 온도와 환경 정보를 심층신경망의 입력층 노드에 입력하는 단계; 및Inputting temperature and environmental information related to low carbon spontaneous combustion into an input layer node of a deep neural network; And
    상기 심층신경망의 은닉층1,2 노드에서 Sigmoid 활성함수를 사용하여 저탄장 자연발화를 예측하여 츨력층 노드로 출력하는 단계;를 포함하는 것을 특징으로 하는 심층신경망을 이용한 저탄장 자연발화 예측방법.Predicting low-charging spontaneous ignition using the Sigmoid activity function at the hidden layer 1, 2 nodes of the deep neural network, and outputting to the output layer node.
  2. 제1항에 있어서,The method of claim 1,
    상기 입력층 노드의 입력은 풍속, 기온, 습도, 대기압력, 및 저탄장 내부온도인 것을 특징으로 하는 심층신경망을 이용한 저탄장 자연발화 예측방법. The input of the input layer node is a wind speed, temperature, humidity, atmospheric pressure, low-charging natural ignition prediction method using a deep neural network, characterized in that the internal temperature.
  3. 제1항에 있어서,The method of claim 1,
    상기 심층신경망은 입력층, 은닉층1, 은닉층2, 출력층은 각각 80개, 20개, 10개, 1개 노드로 구성하는 것을 특징으로 하는 심층신경망을 이용한 저탄장 자연발화 예측방법.The deep neural network is a low-charging spontaneous speech prediction method using a deep neural network, characterized in that the input layer, hidden layer 1, hidden layer 2, the output layer is composed of 80, 20, 10, 1 node, respectively.
PCT/KR2018/009768 2018-08-24 2018-08-24 Method for predicting spontaneous ignition of coal stockpile using deep neural network WO2020040332A1 (en)

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