Neural network modeling for nutrient dynamics in a recycling piggery slurry treatment system

A recycling reactor system operated under sequential anoxic and oxic conditions was evaluated, in which the nutrients of piggery slurry were anaerobically and aerobically treated and then a portion of the effluent was recycled to the pigsty. The most dominant aerobic heterotrophs from the reactor were Alcaligenes faecalis (TSA-3), Brevundimonas diminuta (TSA-1) and Abiotrophia defectiva (TSA-2) in decreasing order, whereas lactic acid bacteria, LAB (MRS-1, etc.) were most dominantly observed in the anoxic tank. Here we have tried to model the nutrient removal process for each tank in the system based on population densities of heterotrophic and LAB. Principal component analysis (PCA) was first applied to delineate a relationship between input (microbial densities and treatment parameters such as population densities of heterotrophic and LAB, suspended solids (SS), COD, NH₄ ⁺–N, ortho-phosphorus, and total phosphorus) and output. Multi-layer neural networks using an error back-propagation learning algorithm were then employed to model the nutrient removal process for each tank. PCA filtration of microbial densities as input data was able to enhance generalization performance of the neural network, and this has led to a better prediction of the measured data. Neural networks independently trained for each treatment tank and the combined analysis of the subsequent tank data allowed a successful prediction of the treatment system for at least 2 days.