The IWA Greenhouse Gas Task Group Perspective on the Use of Water Quality and Process Models for Sustainable Wastewater Management
IWA has approved a new Task Group (TG) whose focus is on communicating ongoing efforts and guiding new research on greenhouse gas footprints of wastewater systems. These efforts comprise both research into the source of nitrous oxide (N2O) and methane (CH4) emissions
and model development for use in model-based optimization and instrumentation, control and automation (ICA). The wastewater systems considered by the TG cover collection systems, wastewater treatment plants, and rivers. The TG is unique in the sense that it was initiated from four Specialist
Groups: Sustainability, Nutrient Removal and Recovery, Instrumentation, Control and Automation, Systems Analysis and Integrated Assessment. This indicates that the problem can be approached from different angles. The strength of the TG is that it can integrate all these objectives. The TG
core group consists of all authors of this paper to which are added a number of distinguished water professionals fulfilling other TG roles. A balance between energy, operational costs, greenhouse gas emissions, and water quality is essential for a sustainable water sector, and so the aim
of the TG is to develop modeling tools that can help find this balance.
Recent research has shed some new light into some of the mechanisms responsible for greenhouse gas (GHG) emissions from both collection systems (Guisasola et al., 2008) and wastewater treatment plants (WWTPs) (Ahn et
al., 2009; Yu et al., 2010). Some of this knowledge has been or is currently being translated into mathematical models that can extend existing models to include GHG related state variables. This should allow for better predictions of GHG emissions from specific parts of these systems. When
integrating all these models, one can get insight into the total system contribution to GHG emissions as well as identifying which parts of the system are the main contributors. An additional benefit of incorporating these ideas into existing mathematical models is that this can be done in
a dynamic fashion, which captures diurnal variations and allows targeting operational conditions that promote or diminish emissions. Given that dynamic variability in standing ammonia concentrations correlates very well with emissions N2O and NO (Ahn et al., 2009; Kampschreur et
al., 2008; Yu et al., 2010), it is important to recognize and describe such phenomena in the developed process model equations. Extending this further, models can beused to minimize emissions through optimum system operation, for example, by testing hypothetical scenarios and applying ICA.
Lessons learned through methodology development on how to optimize WWTPs for certain objectives (e.g. effluent quality, decreasing energy usage) can be reused and adapted to speed up the implementation of GHG emission in the models (Jeppsson et al., 2007; Nopens et al., 2010). Moreover, minimizing
the GHG footprint is not a stand-alone goal and should be performed in conjunction with optimizing other needs, such as maintaining effluent quality, reducing energy usage, maintaining receiving water quality. Hence, the problem becomes a multi-criteria optimization which is challenging and
needs the development of new tools to accomplish all set goals.
Other key reasons for the TG's focus include the following:
Stricter discharge limits are forcing the upgrading of treatment configurations, many of which include cyclic
transitions from low to higher dissolved oxygen (DO) concentrations contributing to the whole-cell N2O generation by N. europaea cultures that has been recently documented by Yu et al. (2010). In general, rapidly changing process conditions characteristic for advanced wastewater
treatment can lead to increased N2O emission, however also adaptation of bacterial populations leading to reduced N2O emission has been observed (Kampschreur et al, 2009)
Energy conservation pressures are prompting control strategies,
such as reducing DO set points to reduce kWh, which can result in greater overall GHG emission by potentially increasing N2O emissions under certain conditions (Porro, 2010), as AOB can utilize nitrite as electron acceptor instead of oxygen under oxygen limited conditions and produce
N2O (Bock et al., 1995; Kester et al., 1997)
CH4 emissions in sewers and at the WWTP can make up a significant fraction of a utility's total GHG footprint and can impact plant influent COD (Guisasola et al., 2008), which can
also impact N2O emissions (Johnson and Hiatt, 2009).
Modeling and optimizing CH4 production through anaerobic digestion will be critical for minimizing both GHG footprints and energy consumption of WWTPs. For example, modified anaerobic
digestion models can be used to investigate the dynamics of methane generation from co-digestion with other waste products.
All of the above clearly illustrate the complexity of the problem as there are different optimization constraints, which will likely
be highly coupled and result in the need for tradeoff decisions that can be best facilitated through LCA and other benchmarking tools.
In an attempt to illustrate how modeling and control tools can be of help to reach these multiple objectives, a state-of-the-art integrated model comprising
of a collection system model and a plant-wide WWTP model is being developed for a virtual test case. The test case will account for a variety of issues including: population size, temperature variation, separate/combined sewer of certain length and diameter, annual average flow to WWT works,
diurnal flow and loads, effluent limits, primary, secondary and tertiary treatment, sludge treatment through thickening, anaerobic digestion and dewatering.
The dynamic GHG footprint is calculated and compared to currently used methods to prove its added value in terms of providing detail
of diurnal performance of different parts of the wastewater system which will lead ultimately to improved prediction accuracy. Different scenarios and treatment configurations will be simulated and documented to illustrate the difficulty in accounting for all constraints imposed on the system.
This model will serve as a basis to target challenges that will set the scene for determining directions of further developments within the TG.
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