Mechanism-Data-Driven Multiobjective Optimization for Wastewater Treatment Process

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Abstract
Set-point optimization of wastewater treatment process (WWTP) is critical for energy savings but is challenging due to complex nonlinear mechanisms and measurement noises. To address this optimization problem, a mechanism-data-driven multiobjective optimization method is developed to alleviate deficiencies in mechanisms and process data. First, a mechanism-data-driven model is established to describe the relationships between effluent quality, energy consumption, and key process variables. Then, the mechanisms and process data can be collaboratively leveraged to alleviate the inaccuracy of mechanism models and suppress measurement noises. Second, a weighted indicator-based multiobjective particle swarm optimization algorithm is proposed to suppress uncertainties introduced by measurement noises. Then, the set-points with noise robustness are obtained to improve optimization performance under real restricted conditions. Third, the proposed method is applied to the benchmark simulation model No. 1 to evaluate its capability. The results demonstrate that this method can improve the optimization performance of WWTP.
Funding Information
  • National Natural Science Foundation of China (62125301, 61890930-5, 62373011, 62021003, 62103012)
  • National Key Research and Development Project (2022YFB3305800-05)
  • Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020)
  • Beijing Natural Science Foundation (KZ202110005009)
  • Youth Beijing Scholar (037)

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