{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T06:50:05Z","timestamp":1791528605819,"version":"4.3.4"},"reference-count":44,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Water Environment Research"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Aeration is the largest energy consumer in activated\u2010sludge treatment and a principal control over nitrous oxide (N\n                    <jats:sub>2<\/jats:sub>\n                    O), a dominant contributor to a plant's carbon footprint, while effluent limits remain nonnegotiable. This paper develops a convex Koopman\u2010operator model predictive controller (MPC) for that three\u2010way trade\u2010off. Extended dynamic mode decomposition identifies a linear predictor of the nonlinear process, N\n                    <jats:sub>2<\/jats:sub>\n                    O included; successive linear programming reduces each control step to a strictly convex quadratic program with explicit ammonia and total\u2010nitrogen constraints, an offset\u2010free correction and an influent forecast, solved in about 9\u2009ms against a 15\u2010min interval. The predictor accounts for 96.7% of output variance one step ahead and 73.4% at the 90\u2010min horizon, against 71.9% and 39.8% for local linearization. On the single\u2010reactor reference model the controller eliminated effluent\u2010ammonia violation over 10 identification and noise realizations, against 7.5 and 15.4\u2009h for fixed\u2010dissolved\u2010oxygen and ammonia\u2010cascade baselines at indistinguishable aeration energy, and sweeping the emission weight traced a monotone frontier along which each per cent of N\n                    <jats:sub>2<\/jats:sub>\n                    O abatement costs about 2.4% of aeration energy. Transferred without modification to a five\u2010tank Activated Sludge Model No. 1 cascade manipulating aeration and internal recirculation, it removed ammonia violation from 69\u2009h to none and lowered the nitrogen\u2010load effluent index by 38% at N\n                    <jats:sub>2<\/jats:sub>\n                    O parity. A component\u2010wise ablation identifies the lifted predictor and the offset\u2010free correction, not the influent preview, as the sources of compliance. Run unchanged on an independently structured evaluation plant, the compliance and energy results transferred but the emission magnitudes and the ranking of the controllers on N\n                    <jats:sub>2<\/jats:sub>\n                    O did not, which bounds what the emission results license.\n                  <\/jats:p>","DOI":"10.1002\/wer.70597","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T06:07:05Z","timestamp":1791526025000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convex, Real\u2010Time Koopman Model Predictive Control of Activated\u2010Sludge Aeration: Constraint\u2010Aware Operation With an Explicit Energy\u2013Nitrous\u2010Oxide Operating Frontier"],"prefix":"10.1002","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0921-9418","authenticated-orcid":false,"given":"Bayram Arda","family":"Ku\u015f","sequence":"first","affiliation":[{"name":"Department of Electronics and Automation Gaziantep University  Gaziantep Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2011.2179349"},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","unstructured":"Arbabi H. M.Korda andI.Mezi\u0107.2018. \u201cA Data\u2010Driven Koopman Model Predictive Control Framework for Nonlinear Partial Differential Equations.\u201d 2018 IEEE Conference on Decision and Control (CDC) 6409 6414 IEEE.https:\/\/doi.org\/10.1109\/CDC.2018.8619720.","DOI":"10.1109\/CDC.2018.8619720"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40435\u2010024\u201001398\u20104"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.2166\/wst.2020.298"},{"key":"e_1_2_11_6_1","unstructured":"Boiocchi R.2016. \u201cModelling and Control of Nitrous Oxide Emissions From Wastewater Treatment Plants [Doctoral Dissertation].\u201d Technical University of Denmark."},{"key":"e_1_2_11_7_1","volume-title":"Computer Aided Chemical Engineering","author":"Boiocchi R.","year":"2015"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/21M1401243"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/10643389.2023.2183699"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2024.122179"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2025.108128"},{"key":"e_1_2_11_12_1","doi-asserted-by":"crossref","unstructured":"Dhouibi S. andS.Bouall\u00e8gue.2022. \u201cMulti\u2010Model Based Control of an Activated Sludge Process.\u201d In 2022 IEEE 21st International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE.https:\/\/doi.org\/10.1109\/STA56120.2022.10019005.","DOI":"10.1109\/STA56120.2022.10019005"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2025.0160339"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.11591\/ijeecs.v40.i2.pp629\u2010639"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1049\/cth2.70021"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.1c00840"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2013.07.046"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2011.04.040"},{"key":"e_1_2_11_19_1","doi-asserted-by":"crossref","unstructured":"Folkestad C. andJ. W.Burdick.2021. \u201cKoopman NMPC: Koopman\u2010Based Learning and Nonlinear Model Predictive Control of Control\u2010Affine Systems.\u201d 2021 IEEE International Conference on Robotics and Automation (ICRA) 7350 7356 IEEE.https:\/\/doi.org\/10.1109\/ICRA48506.2021.9562002.","DOI":"10.1109\/ICRA48506.2021.9562002"},{"key":"e_1_2_11_20_1","doi-asserted-by":"crossref","DOI":"10.2166\/9781780401171","volume-title":"Benchmarking of Control Strategies for Wastewater Treatment Plants","author":"Gernaey K. 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