Regional optimal allocation for reducing waste loads via artificial neural network and particle swarm optimization: a case study of ammonia nitrogen in Harbin, northeast China

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Courtesy of IWA Publishing

Cutting external waste loads can improve water quality. Allocation for reducing waste loads should consider changing variables, such as river flows and pollutant emissions. A particle swarm optimization (PSO) method and coupling artificial neural network (ANN) models have been applied to optimize reduction rates of ammonia nitrogen (NH3-N) loads from sewage outlets in Harbin, northeast China. For the planned water quality functional section (WQFS), the NH3-N concentration is related to emitted pollutant loads and can be well predicted by ANN linkage models. Further, NH3-N load reduction rates of all outlets are optimized by PSO with the water quality standard target. The highest NH3-N concentrations occur in January and February, a typical low-flow period in Harbin. The results delivered optimum NH3-N reduction rates for the five outlets, for January and February 2011. All predicted NH3-N concentrations after the reduction meet the water quality standard. The results indicate that the outlet with the highest NH3-N load has the biggest reduction rate in each WQFS, and outlets in the WQFS with higher background NH3-N concentrations need to cut more NH3-N loads. Decision-makers should not only focus on the outlet with the highest NH3-N emission load, but also ensure that the NH3-N concentration of upper WQFS meets the water quality goal.

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