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[허성구 통합, JWPE/JCR10%]유입성상별 최적처리 제어:SBR AI제어-스케쥴링 (From Influent to AI Optimal Control and Scheduling​)
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  • 2023-03-28

연구실 논문 소식입니다.

EMSEL 연구실 통합과정 허성구/JuinYau박사, 통합과정 Hai학생, 우태용 통합/김상윤 석사과정, Paulina교수/Usman박사 그리고 부강테크 오태석 팀장 논문이 Journal of Water Process Engineering (IF=7.34, JCR TOP 10%: Water Resources)저널에 게재 승인되었습니다.  

-대전 하수처리장내 부강테크 아나목스 SBR공정 Water AI 기술 개발 및 실공정 적용 논문 

-아나목스 공정/PN-SBR ASM type 모델링및 보정, 유입설상별 Dynamic Programming기반 최적운전전략 기법, 배치내 NO2/NH4 ratio 설정치

-유입성상별 처리장 운전조건 최적화:유입성상 조건에 따른 SBR AI 제어-스케쥴링 (From Influent to AI Optimal Control and Scheduling​)

-저자: 통합과정 허성구/ (졸업생)JuinYau 박사, 통합과정 Hai학생, 우태용 통합/김상윤 석사과정, (졸업생) Paulina교수/Usman박사, 부강테크 오태석 팀장

-실제 full-scale 고도처리SBR공정에 Water AI기술을 적용한 연구로서 From Influent Augmentation to AI-Aided Optimal Control and Scheduling​ 최신 연구 결과

실제 공정에 적용되었고 현재 BKT부강테크와 Water AI 현장 최종 데이타를 가지고 좋은 저널에 같이 응용 논준비하는 중

 

Hi EMSEL,

I am pleased to inform you that SungKu (a combined student) and Dr. Juin Yau Lim​ (with Hai​, Dr. Safder, TaeYong, SangYun and TaeSuk Oh@BKT)'s paper has been published for publication in the Journal of Water Process Engineering (ISSN: 2214-7144, IF=7.34, JCR TOP10% journal).

Please give your big hands to SungKu and Dr. Lim. A patent will be applied soon.

Congratulations for the paper acceptance indeed. 


Topic: End-to-End Autonomous Control and Scheduling of Partial Nitriration SBR

​Keywords: Autonomous resilient operation; NO2/NH4 ratio; Full-scale PN-SBR; Rank based model calibration; Optimal aeration strategy; Artificial intelligenc

 

[308] SungKu Heo+, Juin Yau Lim+, Hai-Tra Nguyen, Paulina Vilela, Usman Safder, TaeYong Woo, SangYoun Kim, TaeSeok Oh, ChangKyoo Yoo*, End-to-End Autonomous and Resilient Operability Strategy of Industrial-Scale PN-SBR System: From Influent Augmentation to AI-Aided Optimal Control and Scheduling, Journal of Water Process Engineering (ISSN: 2214-7144, SCIE, JCR TOP 10%: Water Resources), 53(7), pp.103694 (2023.7) (Ack: NRF: NRF중견2021R1A2C2007838 & BKT(2020003160009)

 

 Mathematical model resembling a current operating full-scale PN-SBR process. 

 Rank-based global sensitivity analysis assisted calibration protocol was developed. 

 Influent data augmentation method considering varying C/N ratio was developed. 

 AI-OpAS maintained the NO2/NH4 of 1.1 with resilient autonomous operation. 

 AI-OpAS reduced aeration energy by 31.38% under varying influent conditions. 

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