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Related Experiment Video

Updated: Jun 13, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Wastewater treatment process optimization in stochastic game based on multiagent deep reinforcement learning.

Guanghua Wang1, Xingyu Wang2, Fan Luo1

  • 1Research Center, Guangzhou Municipal Engineering Design& Research Institute, Guangzhou 510060, PR China.

Water Research
|June 11, 2026
PubMed
Summary

This study introduces a novel multi-objective optimization approach for papermaking wastewater treatment, integrating advanced modeling and multi-agent deep reinforcement learning (MADRL) to reduce costs and emissions.

Keywords:
Deep reinforcement learningEnergy consumptionGreenhouse gasesMulti-objectiveOperational costOptimizationWastewater

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Last Updated: Jun 13, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Environmental Engineering
  • Artificial Intelligence
  • Process Optimization

Background:

  • Papermaking wastewater treatment faces challenges with nonlinear dynamics, pollution control, and carbon reduction targets.
  • Achieving compliance while minimizing operational costs, energy use, and greenhouse gas (GHG) emissions is a critical industrial challenge.

Purpose of the Study:

  • To develop a multi-objective optimization strategy for the papermaking wastewater treatment process (PWTP).
  • To simultaneously address pollution control and carbon reduction goals in industrial wastewater treatment.

Main Methods:

  • Integration of Kriging and High-Dimensional Model Representation (HDMR) for real-time GHG emission estimation.
  • Development of a multi-agent deep reinforcement learning (MADRL) framework for dynamic optimization.
  • Modeling of biochemical and sedimentation processes using the Benchmark Simulation Model No. 1 (BSM1).

Main Results:

  • The proposed MADRL policy achieved a 3.52% reduction in operational costs and a 26.38% decrease in energy consumption.
  • A 7.9% reduction in greenhouse gas (GHG) emissions was observed while maintaining effluent quality standards.
  • The system demonstrated robust performance and compliance compared to the BSM1 benchmark control.

Conclusions:

  • The integrated Kriging-HDMR and MADRL approach offers an effective solution for optimizing papermaking wastewater treatment.
  • This method successfully balances pollution control, operational efficiency, and carbon mitigation.
  • The study highlights the potential of advanced AI techniques in addressing complex environmental engineering challenges.