INTEGRATED MATHEMATICAL AND QUALITATIVE MODELLING

FOR SUSTAINABLE URBAN WATER MANAGEMENT

INDIVIDUAL RESEARCH PROJECT ER 1

#11 SANITAS SUSTAINABLE AND INTEGRATED URBAN WATER SYSTEM MANAGEMENTWWW.SANITAS-ITN.EU

THE CHALLENGE

There is a significant technological gap when it comes to practical tools in the water sector for assessing the poten-tial for nitrous oxide (N2O) emissions from the biological wastewater treatment processes in water resource recovery facilities (WRRFs). As N2O is a potent greenhouse gas (GHG), it has been seen to make up almost 80 percent of WRRF carbon footprints. N2O is also known to be a strong ozone depleting gas. Despite growing awareness and concern for reducing emissions from the water sector, little has been done due to:

• Limited in-depth understanding among practitioners on of the mechanisms for N2O emissions and how to mitigate them.

• With a lack of simple tools that can facilitate and streamline assessments of potential N2O emissions from WRRFs; focus is placed only on primary objec-tives like meeting permit conditions.

Although mathematical models have been developed for predicting N2O emissions from WRRFs, they either only represent single pathways, which may not be represent-ative of the actual WRRF pathways, or include multiple pathways, with an inherent increase in complexity and diffi-culty level for practitioners. Furthermore, the mathematical models have only been validated for a number of cases and have yet to reach consensus.

Fellow in charge: Jose PorroSupervisor: Joaquim Comas LEQUIA - Universitat de Girona

THE PROJECT

This research project aimed to develop a practical tool for qualitatively assessing the potential for N2O emissions from WRRFs. The idea was to build on previous work integrating mathematical models with qualitative models, which incorporate artificial intelligence (AI) techniques to provide a more informed decision making for selecting WRRF operating strategies. The tool is to leverage the vast body of knowledge, which has been built in recent years, on N2O pathways/mechanisms and operational factors influencing its production and emissions, and apply it in practice. The project also aimed to investigate the feasibility of this approach for assessing potential GHG emissions from sewers and rivers, and ultimately assessing potential GHG emissions from the integrated urban wastewater system (UWWS).

METHODOLOGY

Figure 1 - Integrated mathematical / knowledge-based N2O risk assessment modelling framework.

• Developed integrated mathematical / knowledge-based N2O risk assessment modelling framework and proof of concept using Benchmark Simulation Model No. 2 (BSM2) platform (Figure 1).

• Validated N2O risk model with full-scale data from three different WRRFs.

• Demonstrated application of the N2O risk assessment modelling framework for identifying mitigation control strategies in a real WRRF.

• Investigated feasibility of the integrated mathematical / qualitative modelling approach for integrated UWWS GHG risk assessment by measuring GHG emissions in sewer and river.

BENEFITS / APPLICABILITY

The key benefits of the N2O risk model and integrated/qualitative modelling approach include the following:

• N2O risk model is a simple tool that does not need calibration; just need to paste in either simulation output data, or SCADA data to quickly get risk results and assess N2O risk for large data sets.

• N2O risk model accounts for all rele-vant pathways of N2O production in activated sludge systems and can properly diagnose risk regardless of which pathways are actually present in a particular WRRF.

• Control strategies to mitigate N2O emissions can be identified and tested via simulation.

Applications for the N2O risk model include:

• Assessing and improving baseline process (nitrogen removal) efficiency versus N2O risk.

• Sustainable benchmarking of integrated UWWS control strategies.

• Gaining mechanistic insight on all relevant N2O production pathways.

• Online WRRF supervision and control: viewing N2O risk in real-time and using its rule-based system for controlling DO to minimize or eliminate N2O risk when feasible based on process conditions.

RESULTS

Proof of concept showed N2O risk model can be an effective decision support tool for:

• Assessing N2O production risk from plant-wide and reactor levels in addition to effluent water quality (EQI) and operational cost (OCI) (Table 1).

• Diagnosing specific risks for mitigation opportunities using simulation data or SCADA data.

• Hypothesizing N2O pathways and selecting appropriate mathematical N2O models.

N2O risk model corresponded well with full-scale N2O measurement data from three different plants with different configurations and performed better than single-pathway mathematical model (Figure 2).

Demonstrated on a real case (Eindhoven, NED), the N2O risk assessment framework verifies how it can be used to identify strategies that eliminate risk of N2O emissions; hence, peaks of actual N2O emissions, and maintain effluent water quality, and possibly save energy costs by reducing periods of over-aeration or High DO risk (Figures 3 and 4).

Measurements showed there are significant GHG emissions in sewers and rivers, varying with parameters such as flow and dissolved oxygen, which can be extracted from mathematical models and correlated with emissions. Therefore, integrated mathematical/knowledge-based modelling approach is feasible for integrated UWWS GHG risk assessment.

Table 1: Proof of Concept Results.

Figure 2: N2O risk model validation results. (A) individual risks representing two pathways, incomplete hydroxy-lamine oxidation (orange line) and nitrifier denitrification (red line). (B) overall risk (black line) incorporating both pathways corresponds to measured N2O (green) better than single-pathway mathematical model (blue line).

Figure 3: (A) N2O risk based on simulation of current (base case) dissolved oxygen (DO) control with various peaks of N2O risk versus (B) N2O risk (zero) based on simulation of DO control (1.6 mg/L) to eliminate N2O risk.

Figure 4: Simulation of effluent ammonia concentration based on current (base case) DO control (blue line) and risk-based DO control (green line).

#11

The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013, under REA agreement 289193. This publication reflects only the authors’ views and the European Union is not liable for any use that may be made of the information contained therein.

Coordinator: Joaquim Comas, LEQUIA - Universitat de Girona, joaquim.comas@udg.edu

SANITAS SUSTAINABLE AND INTEGRATED URBAN WATER SYSTEM MANAGEMENTWWW.SANITAS-ITN.EU