ORIGINAL PAPER
Estimation of prognostic neural network application in gaseous emissions modeling
 
 
 
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Journal of Research and Applications in Agricultural Engineering 2007;52(2):71-74
 
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ABSTRACT
Predictive abilities of artificial neural networks are one of the main topics of their application. The aim of this paper was to use their suitability for modeling of ammonia emission during farmyard manure composting. The best results were obtained while using the MLP neural networks.
REFERENCES (17)
1.
Amon B., Amon T., Boxberger J., Pollinger A., 1999. Emission of NH3, N2O and CH4 from composed and anaerobically stored farmyard manure. Proceeding at the 9th International Conference on Management Strategies for Organic Waste Use in Agriculture, Rennes, France, p. 209-216.
 
2.
Chadwick D.R., 2005 Emissions of ammonia, nitrous oxide and methane from cattle manure heaps: effect of compaction and covering. Atmospheric Environment 39, p. 787–799.
 
3.
Dach J., Sęk T., 1998 Aerodynamiczne metody pomiaru strat amoniaku z obornika i gnojowicy. Poznań.
 
4.
Dach, J., Pietrowski M., 2003. The Research on Ammonia Emissions from the Manure During Storage, Composting Process and Spreading on the Field, w: Myczko, A.(red.), Elim. Agric. Risks Health Envir. 2003, r.1 Basick Problems in Agriculture. Wyd. Center of Excellence Tragen Poznań, 123-132.
 
5.
Dach J., Kowalik I., Zbytek Z, Pietrowski M. 2004 Badanie emisji gazowych w procesie kompostowania obornika, Wybrane zagadnienia ekologiczne we współczesnym rolnictwie. PIMR, Monografia, str. 292-297.
 
6.
Dewes T., 1999. Ammonia emissions during the initial phase of microbial degradation of solid and liquid cattle manure. Bioresource Technology 70, p. 245-248.
 
7.
Michel F., Kelner H., Rigot J., Wilkinson T., Pecchia J., 2004 Effects of Straw, Sawdust and Sand Bedding on Dairy Manure Composting. ASAE/CSAE Annual International Meeting. Ontario, Canada.
 
8.
Mustin, M. (1987.). "Le compost, gestion de la matiere organique." Edition Francois Dubuse-Paris 947.
 
9.
Niżewski P., Dach J., Boniecki P., Zastosowanie sztucznych sieci neuronowych do modelowania procesu emisji amoniaku z pól nawożonych gnojowicą. Inżynieria Rolnicza. (przyjęty do druku).
 
10.
Paillat J. M., Robin P., Hassouna M., Leterme P., 2005 Predicting ammonia and carbon dioxide emissions from carbon and nitrogen biodegradability during animal waste composting. Atmospheric Environment 39, p. 6833–6842.
 
11.
Sommer S. G., Dahl P., 1999 Nutrient and Carbon Balance during Composting of Deep Litter. J. Agric. Engng. Res. 74, p. 145-153.
 
12.
Tadeusiewicz R., Lula P., 2001. Statistica Neural Networks PL: wprowadzenie do sieci neuronowych. Stat-Soft Polska, Kraków.
 
13.
Tiquia S. M., Tam N. F. Y., 1997 Composting of spent pig litter in turned and forced-aerated piles. Environmental Pollution 99, p. 329-337.
 
14.
Tiquia S. M., Tam N. F. Y., 2001 Characterization and composting of poultry litter in forced-aeration piles. Process Biochemistry 37, p. 869-880.
 
15.
Tiquia, S. M., Richard T.L., Honeyman M.S., 2002. Carbon, nutrient, and mass loss during composting. Nutr. Cycling Agroecosyst. 62, p. 15–24.
 
16.
Zvomuya F., Larney F. J., Nichol C. K., Olson A. F., Miller J. J., DeMaere P. R., 2005 Chemical and Physical Changes Following Co-Composting of Beef Cattle Feedlot Manure with Phosphogypsum. Journal of Environmental Quality, p. 2318-2327.
 
17.
Żurada J., Barski M., Jędruch W., 1996. Sztuczne sieci neuronowe. Wydawnictwo Naukowe PWN, Warszawa.
 
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