1384289

11353

One of the stumbling blocks in the operation of alternatingly aerated activated sludge processes (ASPs) for nitrogen removal is the limited knowledge of both the varying influent composition and the complex dynamics of the biological process. This paper presents a simple physical N-removal model for alternatingly aerated, continuously mixed ASPs. The simplicity is achieved by capturing the slower process dynamics in recursively estimated time-varying model parameters. Both seasonal and diurnal parameter variations are tracked. Also the influent ammonium concentration is treated as a recursively estimated model parameter. The method performs excellently on real data collected from an alternatingly aerated pilot scale ASP fed with municipal wastewater. Simulation of the resulting time-varying model yields accurate and computationally cheap predictions of ammonium and nitrate concentrations in the specific plant under operation over the next hours. Simulation for different control input scenarios can be used to optimize process performance, either manually by operators or automatically by model based optimizing controllers. Another possible application is optimization of the sludge (biomass) concentration, as the estimated parameters contain information regarding process load and concentrations and activities of the N-removing biomass. From this information it can be computed whether there is an excess/shortage of sludge in the reactor.

87

nitrogen removal , Nonlinear , recursiveestimation , stability , Activated sludge process , Alternating aeration , control , Kalman filter , model , optimization

Studia Iranica

100

100