Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making.

This study proposed an efficient framework for optimizing the design and operation of combined systems of wastewater treatment plants (WWTP) and constructed wetlands (CW). The framework coupled a WWTP model with a CW model and used a multi-objective evolutionary algorithm to identify trade-offs between energy consumption, effluent quality, and construction cost. Compared to traditional design and management approaches, the framework achieved a 27 % reduction in WWTP energy consumption or a 44 % reduction in CW cost while meeting strict effluent discharge limits for Chinese WWTP.

An improved ASM-GDA approach to evaluate the production kinetics of loosely bound and tightly bound extracellular polymeric substances in biological phosphorus removal process.

This study established an extended activated sludge model no. 2 (ASM2) for providing a new recognition of the contributions of both loosely-bound EPS (LB-EPS) and tightly-bound EPS (TB-EPS) into phosphorus (P) removal by incorporating their formation and degradation processes during the anaerobic-aerobic cycle. For determining the best-fit values for the new model parameters ( , , , and ) in this extended ASM2, a novel and convenient gradient descent algorithm (GDA) based ASM (ASM-GDA) method was developed.