A realistic dynamic blower energy consumption model for wastewater applications.

At wastewater treatment plants (WWTPs) aeration is the largest energy consumer. This high energy consumption requires an accurate assessment in view of plant optimization. Despite the ever increasing detail in process models, models for energy production still lack detail to enable a global optimization of WWTPs. A new dynamic model for a more accurate prediction of aeration energy costs in activated sludge systems, equipped with submerged air distributing diffusers (producing coarse or fine bubbles) connected via piping to blowers, has been developed and demonstrated. This paper addresses the model structure, its calibration and application to the WWTP of Mekolalde (Spain). The new model proved to give an accurate prediction of the real energy consumption by the blowers and captures the trends better than the constant average power consumption models currently being used. This enhanced prediction of energy peak demand, which dominates the price setting of energy, illustrates that the dynamic model is preferably used in multi-criteria optimization exercises for minimizing the energy consumption.

Detailed off-gas measurements for improved modelling of the aeration performance at the WWTP of Eindhoven.

At wastewater treatment plants (WWTPs), the aerobic conversion processes in the bioreactor are driven by the presence of dissolved oxygen (DO). Within these conversion processes, the oxygen transfer is a rate limiting step as well as being the largest energy consumer. Despite this high importance, WWTP models often lack detail on the aeration part. An extensive measurement campaign with off-gas tests was performed at the WWTP of Eindhoven to provide more information on the performance and behaviour of the aeration system. A high spatial and temporal variability in the oxygen transfer efficiency was observed. Applying this gathered system knowledge in the aeration model resulted in an improved prediction of the DO concentrations. Moreover, an important consequence of this was that ammonium predictions could be improved by resetting the ammonium half-saturation index for autotrophs to its default value. This again proves the importance of balancing sub-models with respect to the need for model calibration as well as model predictive power.

Towards a benchmarking tool for minimizing wastewater utility greenhouse gas footprints.

A benchmark simulation model, which includes a wastewater treatment plant (WWTP)-wide model and a rising main sewer model, is proposed for testing mitigation strategies to reduce the system's greenhouse gas (GHG) emissions. The sewer model was run to predict methane emissions, and its output was used as the WWTP model input. An activated sludge model for GHG (ASMG) was used to describe nitrous oxide (N(2)O) generation and release in activated sludge process. N(2)O production through both heterotrophic and autotrophic pathways was included. Other GHG emissions were estimated using empirical relationships. Different scenarios were evaluated comparing GHG emissions, effluent quality and energy consumption. Aeration control played a clear role in N(2)O emissions, through concentrations and distributions of dissolved oxygen (DO) along the length of the bioreactor. The average value of N(2)O emission under dynamic influent cannot be simulated by a steady-state model subjected to a similar influent quality, stressing the importance of dynamic simulation and control. As the GHG models have yet to be validated, these results carry a degree of uncertainty; however, they fulfilled the objective of this study, i.e. to demonstrate the potential of a dynamic system-wide modelling and benchmarking approach for balancing water quality, operational costs and GHG emissions.

Wastewater treatment modelling: dealing with uncertainties.

This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation of a wastewater treatment system. It briefly references the methods currently used to evaluate prediction accuracy and uncertainty and discusses the relevance of uncertainty evaluations in model applications. The paper aims to raise awareness and initiate a comprehensive discussion among professionals on model prediction accuracy and uncertainty issues. It also aims to identify future research needs. Ultimately the goal of such a discussion would be to generate transparent and objective methods of explicitly evaluating the reliability of model results, before they are implemented in an engineering decision-making context.