Optimization of wastewater treatment plant operation using artificial intelligence and machine learning

现代污水处理厂配备了SCADA系统，可持续收集大量数据。该项目旨在使用这些数据并开发基于人工智能/机器学习（AI/ML）的模型，然后可用于预测和自动改善污水处理厂（WWTP）的生物处理操作。该项目将建立在现有研究的基础上，并使用卡尔加里特定污水处理厂的数据开发AI/ML模型。工厂数据将被用作案例研究，这项研究的成功将导致AI/ML驱动的动态控制工具的开发，这将使污水处理厂的运行参数适应不断变化的进水特性，优化污水处理厂的性能。这些机器驱动的动态控制工具将为污水处理厂节省大量能源，降低运营成本，并通过改善污水质量和减少温室气体排放来最大限度地减少城市污水对环境的影响。

Modern wastewater treatment plants are fitted with SCADA systems that continually collect a significant amount of data. This proposed project is intended to use that data and develop artificial intelligence/machine learning (AI/ML) based models which can then be used to predict and automatically improve the operation of biological treatment at wastewater treatment plants (WWTP). The project will build on existing research and develop AI/ML models using data from a specific wastewater treatment plant in Calgary. The plant data will be used as a case study and the success of this research will lead to the development of AI/ML driven dynamic control tools which will make WWTP operating parameters adapt to the changing influent water characteristics and optimize WWTP performance. These machine-driven dynamic control tools will lead to substantial energy savings for WWTP, reduce operational cost and minimize environmental impacts of municipal wastewater by improving effluent quality and lowing greenhouse gas emissions.