z-logo
open-access-imgOpen Access
No-regret selection of effective control handles for integrated urban wastewater systems management under parameter and input uncertainty
Author(s) -
Julia M. Ledergerber,
Thibaud Maruéjouls,
Peter A. Vanrolleghem
Publication year - 2020
Publication title -
water science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.406
H-Index - 137
eISSN - 1996-9732
pISSN - 0273-1223
DOI - 10.2166/wst.2020.144
Subject(s) - regret , control (management) , reuse , wastewater , selection (genetic algorithm) , resource (disambiguation) , sensitivity (control systems) , uncertainty analysis , combined sewer , computer science , quality (philosophy) , risk analysis (engineering) , operations research , engineering , environmental engineering , waste management , simulation , machine learning , artificial intelligence , business , computer network , ecology , stormwater , electronic engineering , surface runoff , biology , philosophy , epistemology
Regulatory water quality limits are extended from the wastewater resource recovery facility (WRRF) to the sewer system. It is thus necessary to properly integrate those systems for the evaluation of the overall emissions to the receiving water. The integration of the sewer system and the WRRF, however, leaves us with multiple potential options to reduce these emissions. The proposed approach builds on previous research using global sensitivity analysis (GSA) as a screening method for available control handles. It considers parameter and input uncertainty to select control handles that generate large benefits even if the model differs from reality. Results from a real-life case study indicate that the three top-rated handles are comparably effective for all considered uncertainty and variability scenarios. But the results also showed that this does not apply to lower-rated handles.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom