{"686514":{"#nid":"686514","#data":{"type":"event","title":"PhD Defense by Zhaoyang Xiong","body":[{"value":"\u003Cp\u003ESchool of Civil and Environmental Engineering\u003C\/p\u003E\u003Cp\u003EPh.D. Thesis Defense Announcement\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELayered ARTIFICIAL ITELLIGENCE Predictive-Control Framework for Next-Generation Wastewater Treatment Plants\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EBy\u003Cstrong\u003E\u0026nbsp;Zhaoyang Xiong\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAdvisor:\u003C\/p\u003E\u003Cp\u003EDr. Yongsheng Chen\u003C\/p\u003E\u003Cp\u003ECommittee Members:\u0026nbsp; Dr. Xing Xie (CEE), DR. Joe Bozeman (CEE), DR. Ameet Pinto (CEE), DR. Enlu Zhou (ISYE)\u003C\/p\u003E\u003Cp\u003EDate and Time:\u003Cstrong\u003E\u0026nbsp;December, 3, 2025.\u0026nbsp;3pm EST\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ELocation:\u0026nbsp;Daniel Lab 303\u003C\/p\u003E\u003Cp\u003EVirtual Link:\u0026nbsp;\u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_MWZjMTVkMDctMjc4OS00YzlhLTlmYTctMjViMmI3ZmQwNjU2%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22b9323431-e40e-4649-b88b-d82191e5763b%22%7d\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_MWZjMTVkMDctMjc4OS00YzlhLTlmYTctMjViMmI3ZmQwNjU2%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22b9323431-e40e-4649-b88b-d82191e5763b%22%7d\u0022\u003E\u003Cstrong\u003EJoin the meeting now\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003EMeeting ID: 281 877 299 879 6\u003C\/p\u003E\u003Cp\u003EPasscode: jX6DT3Aq\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EABSTRACT\u003Cbr\u003EMunicipal wastewater treatment plants (WWTPs) operate under changing conditions that do not always match the pace of their control systems. Flow and water quality can shift quickly, while biological processes and mechanical equipment respond more gradually. Most WWTPs rely on fixed setpoints and operator experience to bridge these gaps. This approach keeps effluent quality reliable, but it also limits WWTP\u2019s ability to anticipate disturbances and adjust aeration and recirculation efficiently.\u003Cbr\u003EThis dissertation introduces a three-layered artificial intelligence (AI) predictive control framework that works with the Supervisory Control and Data Acquisition (SCADA) and programmable logic controller (PLC) systems already in place at most WWTPs. The framework is organized into a Prediction Layer, a Local Control Layer, and a Plant-Wide Optimization Layer. Each layer is demonstrated through a full-scale field study at a municipal WWTP.\u003Cbr\u003EThe first layer uses long-term operating records to forecast short-term effluent behavior. These forecasts give operators earlier awareness of shifts in loading conditions and help them adjust operation before large changes occur. The second layer focuses on dissolved oxygen (DO) control. A short-term DO predictor is paired with a rule-based decision module, allowing the controller to act in a steady and interpretable way. This design aligns more closely with how operators make decisions and avoids the abrupt actions that sometimes arise from fully data-driven controllers. The third layer coordinates multiple biological zones by combining predictive modeling with supervisory control embedded in SCADA. It provides a structured way to balance aeration, internal recirculation, external recirculation, and carbon dosing under changing influent conditions.\u003Cbr\u003ETogether, these layers form a practical path toward more anticipatory and energy-conscious operation in municipal wastewater treatment. The framework improves decision-making without requiring major changes to existing infrastructure and supports a gradual transition toward smarter WWTP control.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ELayered ARTIFICIAL ITELLIGENCE Predictive-Control Framework for Next-Generation Wastewater Treatment Plants\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Layered ARTIFICIAL ITELLIGENCE Predictive-Control Framework for Next-Generation Wastewater Treatment Plants"}],"uid":"27707","created_gmt":"2025-11-18 17:52:26","changed_gmt":"2025-11-18 17:53:09","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-12-03T15:00:00-05:00","event_time_end":"2025-12-03T17:00:00-05:00","event_time_end_last":"2025-12-03T17:00:00-05:00","gmt_time_start":"2025-12-03 20:00:00","gmt_time_end":"2025-12-03 22:00:00","gmt_time_end_last":"2025-12-03 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Daniel Lab 303","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}