Dynamic multi-objective optimization control for wastewater treatment process based on modal decomposition and hybrid neural network

Liu, Qing; Jiang, Xiangyuan

doi:10.1016/j.jwpe.2024.105274

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…24 Several review papers indicated that the links of DRL study to minimize carbon emission 15 and balance the conflicting objectives of WWTP performances are imperative and could be promising future directions. 25 Although researchers have explored WWTP operation optimization with multiple objectives, such as effluent quality and energy consumption, using reinforcement learning 26 or other machine learning tools, 27 they neither ignored the environmental effect of GHG directly emitted from the process nor dug into the gambling relations or synergy effects of multiple objectives. 28 Chen et al considered GHG emissions in their multiobjective optimal control study of WWTPs based on life cycle assessment and referenced emission factors from related works.…”

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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