Discover the Editor's Choice: From each issue of the leading journal Water Science & Technology the core team of editors select one outstanding paper to share across IWA Publishing platforms. 

IWA Publishing is pleased to announce that the 32nd Editor's Choice Paper, chosen by James Ball is now Open Access: 

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

Why was the paper selected for Editor's Choice? 

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%.

James Ball, Editor.


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