Data-driven Optimal Control Method of Low-carbon for Wastewater Treatment Aeration Process

For the existing wastewater treatment process, the carbon emission mechanism is unclear and difficult to assess, which hinders the implementation of effective control strategies to reduce overall carbon emissions. To solve this problem, a data-driven low-carbon optimization control method for the aeration process of wastewater treatment was designed. First, the influence factors of carbon emission and their relationship with water quality parameters were deeply analyzed, and the relationship between each water quality parameter and carbon emission in the aeration process was obtained. Second, a data-driven optimization model of energy consumption and carbon emission in the aeration process was designed to obtain the optimal control strategy of aeration process. Finally, the obtained low-carbon optimization control strategy was applied to the benchmark simulation model. Results demonstrate that the strategy can effectively track and control the aeration process and reduce the total energy consumption and carbon emissions.