Experimental Results and Neural Prediction of Sequencing Batch Reactor Performance under Different Operational Conditions

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Three lab scale sequencing batch reactors (SBR) were simultaneously operated at different process conditions to understand the dynamics of organic and nitrogen removal from a synthetic wastewater source. The SBRs were operated continuously for 255 days at different C/N ratio (3 - 6), aeration time (4 - 10 hr) and salt concentrations (0.5 - 2%). The COD removal efficiencies under steady state operation were consistently greater than 80%, while nitrogen removal efficiencies (10 - 98%) were inhibited by high salt concentrations. Back propagation neural network was applied to model this experimental data us ing influent COD, influent nitrogen, salt concentration, aeration time, MLSS concentration and C/N ratio as the input parameters to predict the performance parameters, viz., COD removal efficiency (COD-RE), total nitrogen removal efficiency (T-RE), NH4+–N, NO3-–N and NO2-–N formed. The data points were randomized and divided into training (190 × 3) and testing set (65 × 3). The internal network parameters were selected using the 2k full factorial design of experiments. The appropriate network topology for this system (6-12-5) was selected by estimating the best correlation coefficient (R) value (0.8482) achieved during prediction of the testing set. The result from this study showed that a neural network based model can be used as an efficient data driven model to predict the performance of a SBR unit.