KR101107301B1 - Method for predicting density of reaction product through recursive analysis model during capture process of carbon dioxide - Google Patents

Method for predicting density of reaction product through recursive analysis model during capture process of carbon dioxide Download PDF

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KR101107301B1
KR101107301B1 KR1020090134893A KR20090134893A KR101107301B1 KR 101107301 B1 KR101107301 B1 KR 101107301B1 KR 1020090134893 A KR1020090134893 A KR 1020090134893A KR 20090134893 A KR20090134893 A KR 20090134893A KR 101107301 B1 KR101107301 B1 KR 101107301B1
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carbon dioxide
reaction product
variable
regression analysis
analysis model
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KR20110078156A (en
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한건우
김제영
장용수
이민우
이해우
안치규
전희동
이민영
이창훈
박종문
박흥수
박주형
류성욱
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재단법인 포항산업과학연구원
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/30—Controlling by gas-analysis apparatus
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/46—Removing components of defined structure
    • B01D53/62—Carbon oxides
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
    • C01B32/00—Carbon; Compounds thereof
    • C01B32/50—Carbon dioxide
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
    • Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P20/00—Technologies relating to chemical industry
    • Y02P20/151—Reduction of greenhouse gas [GHG] emissions, e.g. CO2

Abstract

회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법이 제공된다.A method for predicting the concentration of reaction products in a carbon dioxide capture process through a regression analysis model is provided.

상기 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법은, 온라인상에서 이산화탄소 포집공정을 통해 측정되는 상태변수를 변동변수로 설정하는 단계; 온라인상에서 이산화탄소 포집공정을 통해 상기 변동변수에 의해 얻어지는 반응생성물의 농도를 목표변수로 설정하는 단계; 상기 변동변수와 상기 목표변수를 이용하여 회귀분석모델을 모델링하는 단계; 및 상기 회귀분석모델을 통하여 상기 변동변수의 변화에 따른 반응생성물의 농도를 예측하는 단계;를 포함하여 구성된다.Reaction product concentration prediction method of the carbon dioxide capture process through the regression analysis model, comprising: setting a state variable measured through a carbon dioxide capture process online as a variable variable; Setting a concentration of the reaction product obtained by the change variable as a target variable through a carbon dioxide capture process online; Modeling a regression analysis model using the variable and the target variable; And predicting the concentration of the reaction product according to the change of the variable through the regression analysis model.

회귀분석모델, 이산화탄소, 반응생성물, 변동변수, 목표변수 Regression model, carbon dioxide, reaction products, variable variables, target variables

Description

회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법{Method for predicting density of reaction product through recursive analysis model during capture process of carbon dioxide}Method for predicting density of reaction product through recursive analysis model during capture process of carbon dioxide}

본 발명의 일 측면은 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법에 관한 것으로, 특히 온라인상에서 이산화탄소 포집공정을 통해 얻어지는 반응생성물의 농도를 예측하는 방법에 관한 것이다.One aspect of the present invention relates to a method for predicting the concentration of a reaction product in a carbon dioxide capture process through a regression analysis model, and more particularly, to a method for predicting the concentration of a reaction product obtained through a carbon dioxide capture process online.

대기중 이산화탄소의 농도는 산업화에 따른 화석연료의 사용으로 급격히 증가하고 있으며, 이산화탄소의 발생량 감축을 위해 교토 의정서가 발효되었고 다양한 이산화탄소 포집 및 저장에 관한 기술을 개발하고 있다. The concentration of carbon dioxide in the atmosphere is rapidly increasing due to the use of fossil fuels due to industrialization, and the Kyoto Protocol has entered into force to reduce the amount of carbon dioxide produced, and various technologies for capturing and storing carbon dioxide have been developed.

이러한 이산화탄소의 포집 중 가장 상용화에 근접한 공정은 아민계열의 흡수제를 이용한 흡수법이 있다. 그러나, 아민을 이용한 흡수법은 대상 가스 중에 포함된 산성가스에 의해 아민이 분해되고, 흡수과정에서 사용된 아민을 재생하기 위해 높은 에너지가 소요되며, 약품의 단가가 비싼 단점이 있다.The closest to the commercialization of the capture of carbon dioxide is the absorption method using an amine-based absorbent. However, the absorption method using the amine has the disadvantage that the amine is decomposed by the acid gas contained in the target gas, high energy is required to regenerate the amine used in the absorption process, and the cost of the drug is expensive.

이에, 최근에는 아민계 흡수제를 대체하기 위해 암모니아수를 이용한 이산화탄소 포집기술이 주목받고 있다. 암모니아는 화학적으로 안정하여 산성가스 등에 의해 분해되지 않으며, 높은 이산화탄소 흡수능을 갖는다.Therefore, in recent years, carbon dioxide capture technology using ammonia water to attract the amine-based absorbent has attracted attention. Ammonia is chemically stable and does not decompose by acidic gas and the like and has a high carbon dioxide absorption capacity.

또한, 약품비용이 비교적 낮고, 재생에 필요한 에너지가 아민계 흡수제에 비해 낮다는 등의 다양한 장점을 갖는다.In addition, the drug costs are relatively low, and the energy required for regeneration is lower than that of the amine absorbent, and so on.

암모니아수를 이용한 이산화탄소 포집공정은 크게 이산화탄소를 흡수하는 공정과 흡수된 이산화탄소 및 암모니아를 회수 및 재생하는 공정으로 분류된다. 이산화탄소 흡수과정에서 암모늄 염 및 암모늄 이온이 형성되며, 형성된 암모늄 염 및 암모늄 이온은 재생과정에서 이산화탄소와 암모니아로 각각 회수될 수 있다.The carbon dioxide capture process using ammonia water is classified into a process of absorbing carbon dioxide and a process of recovering and regenerating the absorbed carbon dioxide and ammonia. Ammonium salts and ammonium ions are formed during carbon dioxide absorption, and the formed ammonium salts and ammonium ions may be recovered as carbon dioxide and ammonia respectively during regeneration.

암모니아수를 이용한 이산화탄소 포집공정은 크게 이산화탄소를 흡수하는 공정과 흡수된 이산화탄소 및 암모니아를 회수 및 재생하는 공정으로 분류된다. 이산화탄소 흡수과정에서 암모늄 염 및 암모늄 이온이 형성되며, 형성된 암모늄 염 및 암모늄 이온은 재생과정에서 이산화탄소와 암모니아로 각각 회수될 수 있다.The carbon dioxide capture process using ammonia water is classified into a process of absorbing carbon dioxide and a process of recovering and regenerating the absorbed carbon dioxide and ammonia. Ammonium salts and ammonium ions are formed during carbon dioxide absorption, and the formed ammonium salts and ammonium ions may be recovered as carbon dioxide and ammonia respectively during regeneration.

그러나, 이와 같은 이산화탄소 포집공정에서 온라인상에서 실시간으로 얻어지는 반응생성물의 농도를 예측하는 방법에 관한 연구는 미흡한 실정이다.However, studies on how to predict the concentration of the reaction product obtained in real time online in such a carbon dioxide capture process is insufficient.

본 발명의 일 측면은 온라인상에서 실시간 모니터링을 통하여 이산화탄소 포집공정을 통해 측정되는 상태변수가 누락되는 일없이 반응생성물의 농도를 예측하는 방법을 제공하는 것을 목적으로 한다.One aspect of the present invention is to provide a method for predicting the concentration of the reaction product without missing the state variable measured through the carbon dioxide capture process through real-time monitoring on-line.

본 발명의 일 측면은, 온라인상에서 이산화탄소 포집공정을 통해 측정되는 상태변수를 변동변수로 설정하는 단계; 온라인상에서 이산화탄소 포집공정을 통해 상기 변동변수에 의해 얻어지는 반응생성물의 농도를 목표변수로 설정하는 단계; 상기 변동변수와 상기 목표변수를 이용하여 회귀분석모델을 모델링하는 단계; 및 상기 회귀분석모델을 통하여 상기 변동변수의 변화에 따른 반응생성물의 농도를 예측하는 단계;를 포함하여 구성된 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법을 제공한다.One aspect of the invention, the step of setting the state variable measured through the carbon dioxide capture process online as a variable variable; Setting a concentration of the reaction product obtained by the change variable as a target variable through a carbon dioxide capture process online; Modeling a regression analysis model using the variable and the target variable; And predicting the concentration of the reaction product according to the change of the variation variable through the regression analysis model.

본 발명의 일 실시예에서, 상기 상태변수는 이산화탄소가 흡수된 암모니아의 pH, 온도, 전기전도도 또는 이산화탄소 농도 중에서 적어도 하나인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법을 제공한다.In one embodiment of the present invention, the state variable is a method for predicting the reaction product concentration of the carbon dioxide capture process through the regression analysis model, characterized in that at least one of pH, temperature, electrical conductivity or carbon dioxide concentration of the carbon dioxide is absorbed. to provide.

본 발명의 다른 실시예에서, 상기 반응생성물은 수산화이온, 중탄산염이온, 탄산염이온, 카바메이트이온, 황산염이온 또는 질산염이온 중에서 어느 하나인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예 측방법을 제공한다.In another embodiment of the present invention, the reaction product concentration of the reaction product of the carbon dioxide capture process through a regression analysis model, characterized in that any one of hydroxide ions, bicarbonate ions, carbonate ions, carbamate ions, sulfate ions or nitrate ions It provides a forecasting method.

본 발명의 또 다른 실시예에서, 상기 회귀분석모델은 수리적 모델, 통계적 모델, 수리적 모델과 통계적 모델을 조합한 모델 중에서 어느 하나인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법을 제공한다.In another embodiment of the present invention, the regression analysis model predicts the reaction product concentration of the carbon dioxide capture process through the regression analysis model, characterized in that any one of a combination of a mathematical model, statistical model, mathematical model and statistical model Provide a method.

본 발명의 또 다른 실시예에서, 상기 회귀분석모델은 다중회귀분석법, 주성분회귀분석법, 부분최소자승법, 신경회로망-부분최소자승법, 커널-부분최소자승법 또는 LS-SVM(Least Square Support Vector Machine) 중에서 적어도 하나를 이용한 모델인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법을 제공한다.In another embodiment of the present invention, the regression analysis model is selected from among multiple regression, principal component regression, partial least squares, neural network-partial least squares, kernel-part least squares, or least square support vector machine (LS-SVM). It provides a method for predicting the reaction product concentration of the carbon dioxide capture process through a regression model, characterized in that the model using at least one.

본 발명의 일 측면에 따르면, 실험자나 관련업무 종사자들이 경험적인 방법이나 이론적인 방법에 의존하여 어려움을 겪었던 이산화탄소 포집공정에서 얻어지는 목표변수인 반응생성물의 참값 또는 정확한 예측치의 신속한 습득이 가능하다.According to an aspect of the present invention, it is possible to quickly acquire the true value or the accurate prediction of the reaction product, which is a target variable obtained in the carbon dioxide capture process, in which the experimenter or related workers suffered depending on empirical or theoretical methods.

본 발명의 다른 측면에 따르면, 온라인상에서 다양한 회귀분석모델을 통하여 최적의 회귀분석모델을 찾아내어 이산화탄소 포집공정의 최적화에 적용할 수 있다.According to another aspect of the present invention, it is possible to find an optimal regression analysis model through various regression analysis models online and apply it to the optimization of the carbon dioxide capture process.

이하, 첨부된 도면을 참조하여 본 발명의 실시형태를 설명한다. 그러나, 본 발명의 실시형태는 여러 가지의 다른 형태로 변형될 수 있으며, 본 발명의 범위가 이하 설명하는 실시형태로만 한정되는 것은 아니다. 도면에서의 요소들의 형상 및 크기 등은 보다 명확한 설명을 위해 과장될 수 있으며, 도면상의 동일한 부호로 표시되는 요소는 동일한 요소이다.Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the embodiments of the present invention may be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. Shapes and sizes of the elements in the drawings may be exaggerated for clarity, elements denoted by the same reference numerals in the drawings are the same elements.

도 1은 본 발명의 이산화탄소 흡수 및 재생을 위한 이산화탄소 포집반응기의 구성도이다. 도 1을 참조하면, 암모니아가 채워져 있는 교반조(10)에 질소(N2)와 산소(O2)가 유입되면 교반조(10) 하부의 자석교반기(900)에 의해 교반되고, 질소(N2)와 산소(O2)가 흡수된 암모니아 수용액은 pH 센서(400) 및 온도센서(500)에 의해 각각 pH 및 온도가 측정된다. 자석교반기(900)는 흡수공정에서 흡수효율을 높이기 위한 것인데, 재생공정에서는 재생효율을 높이기 위해 히팅 멘틀(heating mentle)에 의해 열을 공급한다.1 is a block diagram of a carbon dioxide capture reactor for carbon dioxide absorption and regeneration of the present invention. Referring to FIG. 1, when nitrogen (N 2 ) and oxygen (O 2 ) are introduced into the agitating tank 10 filled with ammonia, the mixture is stirred by a magnetic stirrer 900 under the stirring tank 10, and nitrogen (N). 2 ) and the ammonia aqueous solution in which oxygen (O 2 ) is absorbed, pH and temperature are respectively measured by the pH sensor 400 and the temperature sensor 500. Magnetic stirrer 900 is to increase the absorption efficiency in the absorption process, in the regeneration process to supply heat by a heating mentle (heating mentle) to increase the regeneration efficiency.

또한, 교반조(10)의 질소(N2)와 산소(O2)가 흡수된 암모니아는 모터에 의해 펌프(600)가 구동하여 흡수탑(300) 상부로 보내어지고, 이 과정에서 전기전도도 센서(700)에 의해 전기전도도가 측정된다.In addition, the ammonia in which nitrogen (N 2 ) and oxygen (O 2 ) of the stirring vessel 10 are absorbed is sent to the upper portion of the absorption tower 300 by driving the pump 600 by a motor, and in this process, an electrical conductivity sensor Electrical conductivity is measured by 700.

또한, 흡수탑(300) 상부에서 암모니아는 응축기(200)에서 응축되어 이산화탄소 농도 검출기(100)로 보내어져 이산화탄소의 농도가 분석되고, 응축기(200)에서 응축된 암모니아는 순환기(800)에 의해 응축기(800) 하부로 보내어진다. In addition, ammonia in the upper portion of the absorption tower 300 is condensed in the condenser 200 and sent to the carbon dioxide concentration detector 100 to analyze the concentration of carbon dioxide, and the ammonia condensed in the condenser 200 is condensed by the circulator 800. (800) is sent to the bottom.

연산수단(1000)은 온라인상에서 이산화탄소 포집공정을 통해 pH 센서(400), 온도센서(500), 전기전도도 센서(700), 이산화탄소 농도 검출기(100)로부터 각각 검출된 pH, 온도, 전기전도도, 이산화탄소 농도에 관한 데이터를 수신한다. 그리 고, 연산수단(1000)은 상태변수인 pH, 온도, 전기전도도, 이산화탄소 농도를 변동변수로 설정하고, 변동변수에 의해 얻어지는 반응생성물의 농도를 목표변수로 설정한다. 반응생성물은 수산화이온, 중탄산염이온, 탄산염이온, 카바메이트이온, 황산염이온, 질산염이온일 수 있다.Computing means 1000 is the pH, temperature, electrical conductivity, carbon dioxide respectively detected from the pH sensor 400, temperature sensor 500, electrical conductivity sensor 700, carbon dioxide concentration detector 100 through a carbon dioxide capture process online Receive data regarding concentration. In addition, the calculation means 1000 sets the state variables pH, temperature, electrical conductivity, and carbon dioxide concentration as variable variables, and sets the concentration of the reaction product obtained by the variable as the target variable. The reaction product may be hydroxide ions, bicarbonate ions, carbonate ions, carbamate ions, sulfate ions, nitrate ions.

또한, 연산수단(1000)은 목표변수를 예측하기 위해 회귀분석모델을 모델링하고, 회귀분석모델을 통하여 변동변수의 변화에 따른 반응생성물의 농도를 예측한다.In addition, the calculation means 1000 models a regression analysis model to predict the target variable, and predicts the concentration of the reaction product according to the change of the variable through the regression analysis model.

도 2는 본 발명의 다양한 회귀분석모델을 통하여 흡수공정 및 재생공정에서 얻어지는 수산화이온, 중탄산염이온, 탄산염이온의 농도 보정결과 및 예측결과이다. 실험방법을 설명하면, 이산화탄소 포집공정에 기반한 암모니아로부터 샘플들을 추출하였다. 추출된 샘플들은 총 73개인데, 39개는 흡수공정에서 추출되었고, 34개는 재생공정에서 추출되었다.Figure 2 is a concentration correction and prediction results of the hydroxide ions, bicarbonate ions, carbonate ions obtained in the absorption and regeneration process through various regression analysis model of the present invention. In describing the test method, samples were extracted from ammonia based on a carbon dioxide capture process. A total of 73 samples were extracted, 39 were extracted by the absorption process and 34 were extracted by the regeneration process.

RMSEC(Root Mean Square Error of Calibration)는 이산화탄소가 흡수된 암모니아의 pH, 온도, 전기전도도, 이산화탄소 농도를 변동변수로 하여 수산화이온, 중탄산염이온, 탄산염이온의 농도를 보정하여 얻은 결과이고, RMSEP(Root Mean Square Error of Prediction)는 RMSEC의 보정결과를 이용하여 수산화이온, 중탄산염이온, 탄산염이온의 농도를 예측하여 얻은 결과이다.Root Mean Square Error of Calibration (RMSEC) is a result obtained by calibrating the concentrations of hydroxide ions, bicarbonate ions, and carbonate ions using the pH, temperature, electrical conductivity, and carbon dioxide concentration of ammonia absorbed by carbon dioxide as the variable variables. Mean Square Error of Prediction) is a result obtained by estimating the concentrations of hydroxide ions, bicarbonate ions and carbonate ions using the RMSEC correction results.

이때, 습식분석방법을 사용하였으며, 임의의 농도의 산용액으로 pH를 8.2와 4.5까지 낮추기까지 소요된 산용액의 소모량을 바탕으로 수산화이온(OH-), 중탄산염이온(HCO3 -), 탄산염이온(CO3 2-)의 농도를 결정하였다.In this case, it was used for the wet analysis method, hydroxide ions on the basis of consumption of the acid solution required to reduce to 8.2 and 4.5 to a pH of the acid solution of any concentration (OH -), bicarbonate ions (HCO 3 -), carbonate ions The concentration of (CO 3 2- ) was determined.

도 2를 살펴보면, RMSEC(Root Mean Square Error of Calibration)와 RMSEP(Root Mean Square Error of Prediction)의 각각의 경우에 흡수공정과 재생공정에 대하여 각각 수산화이온(OH-), 중탄산염이온(HCO3 -), 탄산염이온(CO3 2-)의 농도가 표기되어 있다. 이때, 사용된 회귀분석모델은 다중회귀분석법 (Multiple Linear Regression, MLR), 주성분회귀분석법(Principle Component Regression, PCR), 신경회로망-부분최소자승법(Neural Network Partial Least Squares, NNPLS), 커널-부분최소자승법(Kernel Partial Least Squares, KPLS), LS-SVM(Least Square Support Vector Machine)이 이용되었다.Referring to FIG. 2, in the case of root mean square error of calibration (RMSC) and root mean square error of prediction (RMSEP), hydroxide ions (OH − ) and bicarbonate ions (HCO 3 − ) for absorption and regeneration processes respectively. ), The concentration of carbonate ion (CO 3 2- ) is indicated. At this time, the regression model used is Multiple Linear Regression (MLR), Principle Component Regression (PCR), Neural Network Partial Least Squares (NNPLS), Kernel-Partial Minimum The square method (Kernel Partial Least Squares, KPLS) and Least Square Support Vector Machine (LS-SVM) were used.

각 회귀분석모델을 통해 예측된 농도를 비교해보면, LS-SVM가 가장 오차가 적은 결과를 보이고 있으므로, LS-SVM이 최적의 회귀분석모델이라고 할 수 있으며, 이산화탄소 포집공정의 최적화에 적용할 수 있다.When comparing the concentrations predicted by each regression model, LS-SVM shows the least error result, so LS-SVM is the optimal regression model and can be applied to the optimization of CO2 capture process. .

도 3은 본 발명의 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법의 흐름도이다.Figure 3 is a flow chart of the reaction product concentration prediction method of the carbon dioxide capture process through the regression analysis model of the present invention.

먼저, 연산수단(1000)을 이용하여 온라인상에서 이산화탄소 포집공정을 통해 측정되는 상태변수를 변동변수로 설정한다(S100). 이때, 연산수단(1000)을 이용하 여 측정되는 상태변수는 데이터 마이닝(data mining)을 이용하여 측정된다.First, a state variable measured through a carbon dioxide capture process online is set as a variation variable using the calculation means 1000 (S100). At this time, the state variable measured using the calculation means 1000 is measured using data mining.

그 이후에, 연산수단(1000)을 이용하여 온라인상에서 이산화탄소 포집공정을 통해 변동변수에 의해 얻어지는 반응생성물의 농도를 목표변수로 설정한다(S200).Thereafter, using the calculation means 1000, the concentration of the reaction product obtained by the variation variable through the carbon dioxide capture process online is set as the target variable (S200).

그 이후에, 연산수단(1000)을 이용하여 목표변수를 예측하기 위해 수학식 1과 같은 회귀분석모델을 모델링한다(S300). 즉, 연산수단(1000)을 이용하여S100 단계의 설정된 변동변수와 S200 단계에서 설정된 목표변수를 이용하여 수학식 1과 같은 회귀분석모델을 모델링하는 것이다.Thereafter, a regression analysis model such as Equation 1 is modeled in order to predict the target variable using the calculation means 1000 (S300). That is, the regression analysis model shown in Equation 1 is modeled by using the calculation means 1000 using the set variable in step S100 and the target variable set in step S200.

y = ax1 + bx2 + cx3 + dx4 y = ax 1 + bx 2 + cx 3 + dx 4

여기서, x1, x2, x3, x4는 변동변수이고, y는 목표변수이며, a, b, c, d는 상수이다. 즉, x1, x2, x3, x4는 이산화탄소가 흡수된 암모니아의 pH, 온도, 전기전도도, 이산화탄소 농도에 해당하고, y는 수산화이온, 중탄산염이온, 탄산염이온, 카바메이트이온, 황산염이온, 질산염이온과 같은 반응생성물의 농도에 해당한다.Here, x 1 , x 2 , x 3 and x 4 are variable variables, y is a target variable, and a, b, c and d are constants. That is, x 1 , x 2 , x 3 and x 4 correspond to pH, temperature, electrical conductivity and carbon dioxide concentration of ammonia absorbed by carbon dioxide, y is hydroxide ions, bicarbonate ions, carbonate ions, carbamate ions, sulfate ions This corresponds to the concentration of the reaction product, such as nitrate ion.

S300 단계의 회귀분석모델은 수리적 모델, 통계적 모델, 수리적 모델과 통계적 모델을 조합한 모델일 수 있으며, 다중회귀분석법, 주성분회귀분석법, 부분최소자승법, 신경회로망-부분최소자승법, 커널-부분최소자승법, LS-SVM (Least Square Support Vector Machine)이 사용될 수 있다.The regression analysis model of step S300 may be a mathematical model, a statistical model, a combination of a mathematical model and a statistical model, multiple regression analysis, principal component regression, partial least squares, neural network-partial least squares, kernel-partial least squares LS-SVM (Least Square Support Vector Machine) may be used.

그 이후에, 연산수단(1000)을 이용하여 회귀분석모델을 통하여 변동변수의 변화에 따른 반응생성물의 농도를 예측한다(S400). 즉, 수학식 1에서 변동변수 x1, x2, x3, x4가 정해지면 연산수단(1000)이 회귀분석모델을 통하여 목표변수인 반응생성물의 농도 y를 예측할 수 있는 것이다.Thereafter, the calculation means 1000 is used to predict the concentration of the reaction product according to the change of the variable through the regression analysis model (S400). That is, when the variation variables x 1 , x 2 , x 3 , and x 4 are determined in Equation 1, the calculation means 1000 may predict the concentration y of the reaction product as a target variable through the regression analysis model.

본 발명은 상술한 실시형태 및 첨부된 도면에 의해 한정되지 아니한다. 첨부된 청구범위에 의해 권리범위를 한정하고자 하며, 청구범위에 기재된 본 발명의 기술적 사상을 벗어나지 않는 범위 내에서 다양한 형태의 치환, 변형 및 변경이 가능하다는 것은 당 기술분야의 통상의 지식을 가진 자에게 자명할 것이다.The present invention is not limited by the above-described embodiment and the accompanying drawings. 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 as defined by the appended claims, .

도 1은 본 발명의 이산화탄소 흡수 및 재생을 위한 이산화탄소 포집반응기의 구성도이다.1 is a block diagram of a carbon dioxide capture reactor for carbon dioxide absorption and regeneration of the present invention.

도 2는 본 발명의 다양한 회귀분석모델을 통하여 흡수공정 및 재생공정에서 얻어지는 수산화이온, 중탄산염이온, 탄산염이온의 농도 측정결과 및 예측결과이다. 2 is a result of measurement and prediction of the concentrations of hydroxide ions, bicarbonate ions and carbonate ions obtained in the absorption and regeneration processes through various regression analysis models of the present invention.

도 3은 본 발명의 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법의 흐름도이다.Figure 3 is a flow chart of the reaction product concentration prediction method of the carbon dioxide capture process through the regression analysis model of the present invention.

<도면의 주요 부분에 대한 부호의 설명>                 <Explanation of symbols for the main parts of the drawings>

10 : 교반조 100 : 농도 검출기10: stirring tank 100: concentration detector

200 : 응축기 300 : 흡수탑 200: condenser 300: absorption tower

400 : pH 센서 500 : 온도센서400: pH sensor 500: Temperature sensor

600 : 펌프 700 : 전기전도도 센서 600: pump 700: conductivity sensor

800 : 순환기 900 : 교반기 또는 히팅 멘틀 800: circulator 900: stirrer or heating mantle

1000 : 연산수단1000: calculation means

Claims (5)

온라인상에서 이산화탄소 포집공정을 통해 측정되는 상태변수를 변동변수로 설정하는 단계;Setting a state variable measured through a carbon dioxide capture process online as a variation variable; 온라인상에서 이산화탄소 포집공정을 통해 상기 변동변수에 의해 얻어지는 반응생성물의 농도를 목표변수로 설정하는 단계;Setting a concentration of the reaction product obtained by the change variable as a target variable through a carbon dioxide capture process online; 상기 변동변수와 상기 목표변수를 이용하여 회귀분석모델을 모델링하는 단계; 및Modeling a regression analysis model using the variable and the target variable; And 상기 회귀분석모델을 통하여 상기 변동변수의 변화에 따른 반응생성물의 농도를 예측하는 단계;Predicting a concentration of a reaction product according to the change of the variable by using the regression analysis model; 를 포함하여 구성된 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법.Reaction product concentration prediction method of the carbon dioxide capture process through a regression analysis model consisting of. 제1항에 있어서,The method of claim 1, 상기 상태변수는 이산화탄소가 흡수된 암모니아의 pH, 온도, 전기전도도 또는 이산화탄소 농도 중에서 적어도 하나인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법.The state variable is at least one of pH, temperature, electrical conductivity or carbon dioxide concentration of the ammonia absorbed carbon dioxide, the reaction product concentration prediction method of the carbon dioxide capture process through a regression analysis model. 제1항에 있어서,The method of claim 1, 상기 반응생성물은 수산화이온, 중탄산염이온, 탄산염이온, 카바메이트이온, 황산염이온 또는 질산염이온 중에서 어느 하나인 것을 특징으로 하는 회귀분석모델 을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법.The reaction product is a reaction product concentration prediction method of the carbon dioxide capture process through a regression analysis model, characterized in that any one of hydroxide ions, bicarbonate ions, carbonate ions, carbamate ions, sulfate ions or nitrate ions. 제1항에 있어서,The method of claim 1, 상기 회귀분석모델은 수리적 모델, 통계적 모델, 수리적 모델과 통계적 모델을 조합한 모델 중에서 어느 하나인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법.The regression analysis model is a mathematical model, statistical model, the model of the combination of the mathematical model and the statistical model, characterized in that the reaction product concentration prediction method of the carbon dioxide capture process through the regression analysis model. 제1항에 있어서,The method of claim 1, 상기 회귀분석모델은 다중회귀분석법, 주성분회귀분석법, 부분최소자승법, 신경회로망-부분최소자승법, 커널-부분최소자승법 또는 LS-SVM(Least Square Support Vector Machine) 중에서 적어도 하나를 이용한 모델인 것을 특징으로 하는 회귀분석모델을 통한 이산화탄소 포집공정의 반응생성물 농도 예측방법.The regression analysis model is a model using at least one of multiple regression analysis, principal component regression, partial least squares, neural network-partial least squares, kernel-partial least squares, or LS-SVM (Least Square Support Vector Machine). Prediction of reaction product concentration in carbon dioxide capture process using a regression analysis model.
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