Model based predictive control for energy efficient biological nitrification process with minimal nitrous oxide production

Recent 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.