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Gas–liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas–liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid–base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas–liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas–liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas–liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas–liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners.

Abstract:Complete mixing is hard to achieve in large bioreactors in wastewater treatment plants.This often leads to a non-uniform distribution of components such as e.g. dissolved oxygen and the process rates depending on them. Furthermore, when these components are used as input for a controller, the location of the sensor can potentially affect the control action. In this contribution, the effect of sensor location and the choice of setpoint on the controller performance were examined for a non-homogeneous pilot bioreactor described by a compartmental model. The impact on effluent quality and aeration cost were evaluated. It was shown that a dissolved oxygen controller with a fixed setpoint performs differently as function of the location of the sensor. When placed in a poorly mixed location, the controller increases the aeration intensity to its maximum capacity leading to higher aeration costs. When placed just above the aerated zone, the controller decreases the aeration rate resulting in lower dissolved oxygen concentrations in the whole system, compromising effluent quality. In addition to the location of the sensor, the selection of an appropriate setpoint also impacts controller behavior. This suggests that mixing behavior of bioreactors should be better quantified for proper sensor location and controller design. INTRODUCTIONControl strategies have become state of the art in wastewater treatment plants (WWTP) to achieve cost-effective and optimized operational system behavior (e.g. Olsson et al., 2005;Olsson, 2012). Hereby, on-line sensors are used to gather process information and action is undertaken depending on the system's state (feedback control). The location of sensors is discussed in previous studies (e.g. Waldraff et al., 1998), but it often concerns the development of observers that are used as an input to controllers. Controllers are typically designed based on process models that are mostly approximated by a tanks-in-series approach. Hence, at most the effect of sensor location along the advective flow direction (1D) can be simulated. However, the number of tanks is usually chosen small (for computational reasons) and all tanks are considered completely mixed. In this way they average out local variations occurring in the other 2 dimensions. In reality perfect mixing never occurs: inefficiently mixed reactors possess less well mixed regions or even dead zones resulting in a non-uniform environment.