数据驱动的污水处理曝气过程低碳优化控制方法

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

针对现有的污水处理过程存在碳排放机理不清且难以评估,无法通过有效的调控方式降低碳排放总量的问题,设计了一种数据驱动的污水处理曝气过程低碳优化控制方法。首先,通过深入分析碳排放影响因素及其与水质指标的相互关系,获得了曝气过程各水质指标和碳排放之间的关联关系;其次,采用数据驱动的方法,设计了曝气过程能耗与碳排放的优化模型,以获取曝气过程最优化的控制策略;最后,将获取的曝气过程低碳优化控制方法应用于基准仿真模型。测试结果说明该方法能够有效地跟踪控制曝气过程,降低能耗与碳排放量总量。

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.