Korean Chemical Engineering Research

  • 제45권2호
  • /
  • Pages.133-142
  • /
  • 2007
  • /
  • 0304-128X(pISSN)
  • /
  • 2233-9558(eISSN)
한국화학공학회 (The Korean Institute of Chemical Engineers)
권호 표지

인공신경망을 이용한 시비된 분뇨로부터의 암모니아 방출량 예측

Prediction of Ammonia Emission Rate from Field-applied Animal Manure using the Artificial Neural Network

  • 문영실 (한경대학교 화학공학과 FACS 연구실) ;
  • 임영일 (한경대학교 화학공학과 FACS 연구실) ;
  • 김태완 (한경대학교 식물생명환경과학부)
  • 투고 : 2006.07.03
  • 심사 : 2006.12.08
  • 발행 : 2007.04.30
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초록

화학비료의 과다사용으로 환경오염의 문제가 심각해 지면서 친환경 농자재(목초재 또는 축산분뇨 등)를 사용하는 유기농업의 필요성이 대두되어 왔다. 이러한 친환경 농자재의 시용량은 작물 종류별, 토양 종류별, 계절별, 재배환경 등에 따라 결정되어져야 한다. 유기비료로서 축산분뇨량의 효율적 사용과 축산분뇨로부터의 암모니아 방출량 저감을 위해서는 먼저 축산분뇨의 경작지 시비 후 암모니아 방출모델이 제시되어야 한다. 그리고 암모니아 방출에 영향이 큰 인자들을 찾아내어 이 인자들을 변화시킴으로서 암모니아 방출량을 감소시킬 수 있을 것이다. 이 연구에서는 인공신경망(artificial neural network) 기법을 이용하여 시비된 돈분의 암모니아 휘산량을 예측한다. 유럽지역에서 얻은 암모니아 방출 실험데이터(ALFAM database)를 바탕으로, 암모니아 손실 영향인자에 따른 암모니아 방출량을 Michaelis-Menten 모델식을 이용하여 예측한다. 이 모델식의 모델인자(암모니아 최대 방출량과 암모니아 최대 방출량의 50%에 도달하는 시간)는 feedforward-backpropagation 인공신경망 기법으로 예측하였고, 가중치 분할법(weight partitioning method)으로 암모니아 손실에 미치는 총 15개의 영향인자의 상대적인 중요도를 분석하였다. 그 결과 암모니아 방출량은 기후에 따라 크게 좌우되고, 돈분의 상태도 상당한 영향을 주고 있다.

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

키워드

과제정보

연구 과제 주관 기관 : 국립한경대학교 GRRC(Gyeong-gi regional research center)

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