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The future of WRRF modelling - outlook and challenges
Pusker Regmi1, Heather Stewart2, Youri Amerlinck3
1Brown and Caldwell, Walnut Creek, CA, USA E-mail: pregmi@brwncald.com; Contributed equally to this paper.
The wastewater industry is transitioning to sustainable resource recovery, prompting a reevaluation of existing International Water Association (IWA) models. This shift necessitates exploring new modeling approaches and data management strategies to handle increasing process complexity.
Area of Science:
- Environmental Engineering
- Wastewater Treatment Technologies
- Resource Recovery
Background:
- The wastewater industry is shifting from energy-intensive treatment to sustainable, low-energy operations focused on resource recovery.
- This paradigm shift raises questions about the suitability of current modeling approaches, particularly the International Water Association (IWA) models, which have been used for nearly 30 years.
Purpose of the Study:
- To discuss the challenges and future directions of wastewater modeling in the context of resource recovery and increasing process complexity.
- To gather insights and opinions from various sectors of the wastewater industry regarding the evolution of modeling techniques.
Main Methods:
- The study summarizes discussions and opinions from the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018.
- It synthesizes collective insights on the adaptability of current models and the potential of new data sources and modeling techniques.
Main Results:
- Debates exist on calibrating complex models and the introduction of new unknowns.
- The community is considering alternatives to traditional IWA models, such as 'black box' models and computational fluid dynamics.
- The capacity of new data sources and advanced data management techniques to cope with complex wastewater processes is under scrutiny.
Conclusions:
- The future of wastewater modeling requires adapting to resource recovery goals and managing complex, data-rich processes.
- There is a need to evaluate and potentially adopt new modeling structures and data handling methods to keep pace with industry advancements.
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