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

Moon, Young-Sil (FACS Lab., Dept. Chemical Engineering)
Lim, Youngil (FACS Lab., Dept. Chemical Engineering)
Kim, Tae-Wan (Faculty of Plant Life and Environmental Sciences)
Publication Information
Korean Chemical Engineering Research / v.45, no.2, 2007 , pp. 133-142 More about this Journal
Abstract
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.
Keywords
Livestock Manure; Ammonia Emission Rate; Artificial Neural Network; Michaelis-menten Equation; Weight Partitioning Method;
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