Modeling of recycling oxic and anoxic treatment system for swine wastewater using neural networks |
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Authors: | Jung-Hye Choi Jun-Il Sohn Hyun-Sook Yang Young-Ryun Chung Minho Lee Sung-Cheol Koh |
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Institution: | (1) Division of Civil and Environmental Engineering, Korea Maritime University, 606-791 Pusan, Korea;(2) Department Sensor Engineering, Kyungpook National University, 702-701 Taegu, Korea;(3) Department of Microbiology, Gyeongsang National University, 660-701 Chinju, Korea |
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Abstract: | A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has
been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled
to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others,
and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order,Alcaligenes faecalis, Brevundimonas diminuta andStreptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling
piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process
for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon
the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis (PCA) was first applied
to identify a relationship between input and output. The input would be microbial densities and the treatment parameters,
such as population densities of heterotrophic and lactic acid bacteria, suspended solids (SS), COD, NH4
+-N, or-tho-phosphorus (o-P), and total-phosphorus (T-P). Then multi-layer neural networks were employed to model the treatment process for each tank.
PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring
even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent
combined data analysis allowed a successful prediction of the treatment system for at least two days. |
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Keywords: | piggery slurry neural network principal component analysis (PCA) heterotrophs lactic acid bacteria (LAB) Alcaligenes faecalis |
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