BOARD
NOTICE
> Board > Notice| [축:남기전학생-Water AI,Editor's choice선정] 하수처리장 AI 자율제어 | ||
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축하합니다. 박사과정 남기전/허성구 학생 Water AI 논문이 WST Editor's choice Paper선정되어 Water Science and Technology (Volume 81, Issue 8) 표지논문으로 나올 예정입니다. 주제 : 딥 강화학습을 이용한 하수처리장(MBR) 최적설정치 AI 자율제어 시스템 개발 An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning
KiJeon Nam†, SungKu Heo†, Jorge Loy-Benitez, Pouya Ifaei, and ChangKyoo Yoo*, An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning, Water Science and Technology(ISSN:0273-1223, SCIE), -, International Water Association(IWA) (2020.01) (Ack:2017R1E1A1A03070713 & Climate Change Graduate School) ------------------------------------------------------------ Dear ChangKyoo Yoo,
Congratulations! Your paper ‘An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning’ has been selected as the next in our series of ‘Editor Choice’ papers for Water Science and Technology (WST). This new initiative serves to highlight papers of particular note in each issue by making them Open Access (at no charge to the authors). Your paper was selected to be in this series by the editorial board of WST and will thus be published OA under a CC BY-NC-ND license in Volume 81, Issue 8. In order to make your paper OA (free of charge), we simple require that you sign the attached OA license agreement. We will also be promoting your paper broadly on our social media channels.
The Editor who selected your paper made the following remarks:
Traditional approaches to modelling of treatment processes have been premised on mathematical approximations of the bio-chemical processes. This paper introduces an alternative approach based on deep-learning concepts using agents. The need for data generation through modelling is a desire to decrease the energy consumption at membrane bioreactor (MBR) plants. It was found using this approach that the MBR’s aeration energy consumption could be reduced by 34%.
Best wishes,
Michelle
Michelle Herbert Managing Editor
IWA Publishing Company registered in England No. 3690822
Follow @IWAPublishing and @IWAP_OA on Twitter!
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