Deep learning based simulators for the phosphorus removal process control in wastewater treatment via deep reinforcement learning algorithms

Mohammadi, Esmaeel; Stokholm-Bjerregaard, Mikkel; Hansen, Aviaja Anna; Nielsen, Per Halkjær; Ortiz-Arroyo, Daniel; Durdevic, Petar

doi:10.1016/j.engappai.2024.107992

Cited by 2 publications

(1 citation statement)

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“…Wang et al developed an actor–critic algorithm reinforcement learning structure with an adaptive critic with weight allocation to address the optimal control problem in the WWTP, pointing out the challenges of system complexity derived from strong coupling of the control operations. Mohammadi et al used DRL to adapt to the process’s complex dynamics, optimized the WWTP for phosphorus removal control, and suggested a comparative investigation of different DRLs in an imperfect environment. Croll et al have evaluated four common DRL algorithms systematically to minimize treatment energy in wastewater treatment control .…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Papermaking Wastewater Treatment Processes under Economic, Energy, and Environmental Goals

He,

Lu,

Wang

et al. 2024

Environ. Sci. Technol.

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Due to the heterogeneity of recycled paper materials and the production conditions, pollutants in papermaking wastewater fluctuate sharply over time. Quality control of the papermaking wastewater treatment process (PWTP) is challenging and costly. As regulations are also growing about the environmental effects of the PWTP on greenhouse gas (GHG) emission, energy consumption, etc., the PWTP formulates a complex multiobjective optimization problem. This research established a multiagent deep reinforcement learning framework to simultaneously optimize process cost, energy consumption, and GHG emission in the PWTP, subjected to the effluent quality, to realize economic, energy, and environmental (3E) goals. The biological treatment process of wastewater in paper mills was simulated using benchmark simulation model no. 1 (BSM1). The data generated based on the BSM manual was utilized for model training, and real data acquired from a local papermaking factory was used to estimate the model performance. The results show that the proposed method outperforms conventional techniques in identifying the best control strategies for multiple targets.

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Section: Introductionmentioning

confidence: 99%