ANR - Next generation sludge treatment: optimization of treatment performance through online monitoring of holistic sludge properties

污泥处理和管理是世界各地城市面临的日益严峻的挑战。生物固体处理的成本约占废水处理成本的一半，并且随着新的废水处理设施的建设和现有设施的升级以跟上不断增长的人口和更严格的法规，数量继续增加。污泥处理和管理将是未来十年的主要挑战。本研究的目标是调查在线和实时使用新的整体污泥描述符，通过污泥处理过程监测，确定，建模和预测污泥行为，并利用这些知识优化处理工艺的设计和运行。基于这项研究所产生的知识，我们的最终目标是在我们的工业合作伙伴的支持下，为污泥处理过程开发在线和实时优化和自动化系统。该研究将有助于优化污泥处理工艺，提高其性能，同时降低运营成本和环境影响。这将为污水处理厂节省大量资金。污泥流变学、远紫外（FUV）、近红外（NIR）和电阻抗（EIS）光谱以及水活性将被用作整体描述符，以捕捉污泥特性、流动和行为的变化。这些描述符可以在线和实时测量，并且它们对污泥基质无破坏性。因此，它们可以成功地用于处理过程的优化和自动化。本研究将进行实验室规模和全尺寸测试。该小组还将完成一项评估， 处理工艺变更的环境和经济影响。

Sludge treatment and management is a growing challenge for municipalities around the world. The cost of biosolids treatment constitutes to approximately half of the cost of wastewater treatment, and the quantities continue to increase as new wastewater treatment facilities are built and the existing ones are upgraded to keep up with the growing population and stricter regulations. Sludge treatment and management is going to be a major challenge in the next decade. The goal of this research is to investigate the in-line and real-time use of novel holistic sludge descriptors to monitor, determine, model and predict sludge behavior through sludge treatment processes and use this knowledge for the optimization of design and operation of treatment processes. Based on the knowledge generated from this research, our ultimate goal is to develop in-line and real-time optimization and automation systems for sludge treatment processes with support from our industrial partners. The research will help to optimize sludge treatment processes, improve their performance while reducing the operational costs and the environmental impacts. This will result in important savings for treatment plants. Sludge rheology, far UV (FUV), near infrared (NIR) and electrical impedance (EIS) spectroscopies, and water activity will be used as holistic descriptors to capture the changes in sludge characteristics, flow, and behavior. These descriptors can be measured in-line and real-time, and they are non-destructive to sludge matrix. Therefore, they can be successfully used for optimization and automation of treatment processes. Both lab-scale and full-scale tests will be carried out in this study. The team will also complete an evaluation of environmental and economic impacts of the changes to the treatment processes.