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Regulatory Implications of Integrated Real-Time Control Technology under Environmental Uncertainty
Author(s) -
Fanlin Meng,
Guangtao Fu,
David Butler
Publication year - 2020
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.9b05106
Subject(s) - upstream (networking) , greenhouse gas , downstream (manufacturing) , environmental science , work (physics) , control (management) , baseline (sea) , set (abstract data type) , computer science , environmental engineering , environmental economics , environmental resource management , engineering , operations management , mechanical engineering , computer network , ecology , oceanography , artificial intelligence , economics , biology , programming language , geology
Integrated real-time control (RTC) of urban wastewater systems, which can automatically adjust system operation to environmental changes, has been found in previous studies to be a cost-effective strategy to strike a balance between good surface water quality and low greenhouse gas emissions. However, its regulatory implications have not been examined. To investigate the effective regulation of wastewater systems with this technology, two permitting approaches are developed and assessed in this work: upstream-based permitting (i.e., environmental outcomes as a function of upstream conditions) and means-based permitting (i.e., prescription of an optimal RTC strategy). An analytical framework is proposed for permit development and assessment using a diverse set of high performing integrated RTC strategies and environmental scenarios (rainfall, river flow rate, and water quality). Results from a case study show that by applying means-based permitting, the best achievable, locally suitable environmental outcomes (subject to 10% deviation) are obtained in over 80% of testing scenarios (or all testing scenarios if 19% of performance deviation is allowed) regardless of the uncertain upstream conditions. Upstream-based permitting is less effective as it is difficult to set reasonable performance targets for a highly complex and stochastic environment.

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