Behera, Chitta RanjanChitta RanjanBeheraSrinivasan, BabjiBabjiSrinivasanChandran, KartikKartikChandranVenkatasubramanian, VenkatVenkatVenkatasubramanian2025-08-302025-08-302015-05-0510.1016/j.cej.2015.01.0442-s2.0-84922440391http://repository.iitgn.ac.in/handle/IITG2025/21462Recent studies reveal that Ammonium Oxidizing Bacteria (AOB) in the Biological Nitrification Removal (BNR) process is one of the main contributors for Nitrous Oxide (N<inf>2</inf>O) emissions, a powerful greenhouse gas having a potential of 300times that of Carbon Dioxide (CO<inf>2</inf>) (IPCC, 2011; Ravishankara et al., 2009 [1,2]). Though a few models have been proposed to understand the behaviour of N<inf>2</inf>O production by AOB under various conditions, there exists hardly any work that aim towards development of a control strategy that utilizes these kind of models to mitigate N<inf>2</inf>O production. In this work, a model is developed based on the experimental studies (Ni et al., 2013 [3]) that capture the dynamics of N<inf>2</inf>O during recovery to aerobic conditions, after a period of anoxia, a common practice in nitrogen removal process. Subsequently, this model is employed in soft sensing using Extended Kalman Filter (EKF) to estimate N<inf>2</inf>O concentration and develop an advanced model based control strategy for energy efficient BNR process with minimal N<inf>2</inf>O production. This control strategy provides an aeration profile that minimizes N<inf>2</inf>O production and energy consumption (less pumping cost) apart from meeting the desired ammonium level at the output. The key features of the proposed model based control strategy are: (i) only continuous measurements of DO is required and, (ii) fairly insensitive to fluctuations in the influent ammonium loading and changes in the model parameters.falseBiological nitrogen removal | Extended kalman filter | Nitrous oxide emission | Nonlinear model predictive controlArticle300-310May 05, 2015119WOS:000352247100034