Optimization of sludge conditioning and dewatering using sludge network strength and residual polymer concentration

利用污泥网络强度和残留聚合物浓度优化污泥调理和脱水

• 批准号: RGPIN-2016-06246
• 负责人: Ormeci, Banu
• 金额: $ 3.06万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688528
• 关键词: Optimization; sludge; conditioning; dewatering; using

Dewatering plays a key role in reducing the cost of sludge treatment and final disposal. However, optimization of sludge conditioning and dewatering continues to be a challenge due to continuous changes in sludge characteristics and solids concentration during operation. Ideally, polymer dose should be checked and optimized on a real-time basis but the majority of treatment plants still use operators to manually adjust the polymer dose by checking cake solids. This usually leads to overdosing of polymer typically by 30-40%, which not only increases polymer cost but also decreases cake dryness, and thus disposal costs. In addition, excess polymers discharged to environment have toxic effects on aquatic ecosystems. Therefore, there is a major need for reliable systems that can achieve real-time optimization of polymer dose at wastewater treatment plants. Current technologies typically rely on solids concentration of incoming sludge or turbidity of the effluent, which are not ideal in capturing important sludge characteristics and predicting dewatering performance. An innovative approach to conditioning and dewatering optimization is needed.***This proposal presents a completely new and novel approach to dewatering optimization through in-line and real-time monitoring of network strength and residual polymer concentration. Network strength is a holistic parameter that captures the changes in the physical, chemical, and flow characteristics of sludge and is superior to monitoring individual parameters such as sludge solids concentration which current systems rely on. Measurement of residual polymer is a direct way of optimizing the polymer dose based on excess polymer remaining in sludge. The short-term goal of the research is to complete the fundamental research tasks required for the development of a dewatering optimization system that uses network strength for feed-forward control and residual polymer concentration for feed-back control to achieve dewatering optimization. The long-term goal of the research program is to complete the development of a fully integrated dewatering automation system. ***The research program is expected to lead to important savings for treatment plants, reduce the cost of sludge dewatering and final disposal, decrease the carbon footprint of treatment, and minimize the adverse impact of discharged polymers on aquatic ecosystems. The research will help to train highly qualified personnel in sludge treatment and optimization and automation of treatment systems, which are all high-demand areas. The research has a strong potential to generate new products and systems, and will benefit the Canadian economy. ********

脱水在降低污泥处理和最终处置成本方面起着关键作用。 然而，由于运行期间污泥特性和固体浓度的不断变化，污泥调理和脱水的优化仍然是一个挑战。理想情况下，应实时检查和优化聚合物剂量，但大多数处理厂仍然使用操作员通过检查滤饼固体手动调整聚合物剂量。这通常导致聚合物过量，通常为30- 40%，这不仅增加了聚合物成本，而且降低了滤饼干燥度，从而降低了处理成本。此外，过量的聚合物排放到环境中会对水生态系统产生毒性影响。因此，主要需要能够在废水处理厂实现聚合物剂量的实时优化的可靠系统。目前的技术通常依赖于进入的污泥的固体浓度或流出物的浊度，这在捕获重要的污泥特性和预测脱水性能方面并不理想。需要一种创新的方法来优化调节和脱水。*该提案通过在线和实时监测网络强度和残留聚合物浓度，提出了一种全新的脱水优化方法。网络强度是一个整体参数，它反映了污泥的物理、化学和流动特性的变化，比当前系统所依赖的单个参数（如污泥固体浓度）的监测具有上级优势。残留聚合物的测量是根据污泥中剩余的聚合物来优化聚合物剂量的直接方法。本研究的近期目标是完成开发以网络强度为前馈控制、残余聚合物浓度为反馈控制的脱水优化系统所需的基础研究任务。该研究计划的长期目标是完成完全集成的脱水自动化系统的开发。 * 该研究计划预计将为处理厂节省大量资金，降低污泥脱水和最终处置的成本，减少处理的碳足迹，并最大限度地减少排放的聚合物对水生生态系统的不利影响。这项研究将有助于培养高素质的人才，在污泥处理和处理系统的优化和自动化，这都是高需求的领域。该研究具有产生新产品和系统的强大潜力，并将使加拿大经济受益。********