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

Abstract

ABSTRACT. 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 using 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), NH 4 + -N, NO 3 --N and NO 2 --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 2 k 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.

<i>Water Expert</i>: a conceptualized framework for development of a rule-based decision support system for distribution system decontamination

“…AI has been used extensively in the industrial sector (Fonseca et al, 2003;Delen and Pratt, 2006;Moynihan, 2004;Moynihan et al, 2006) and business community (Ahn et al, 2000;Nemati et al, 2002;Bahrammirzaee, 2010;Shen et al, 2011) to turn data into knowledge. AI application has also migrated into additional disciplines, including the water industry, where it has been demonstrated as an aid to modeling water quality (Panda et al, 2004;Purkait et al, 2008;Kisi et al, 2013) estimating water quantity (Nourani et al, 2012), predicting wastewater treatment performance (Rene et al, 2008), and optimizing distribution system design (Suribabu and Neelakantan, 2006). Thus, AI demonstrates tremendous, crossdisciplinary functionality in decision support.…”