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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710229
• 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，由于运行过程中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，应该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来手动调整聚合物剂量。这通常会导致聚合物过量30-40%，这不仅会增加聚合物成本，还会降低饼的干燥度，从而降低处置成本。此外，排放到环境中的过量聚合物对水生生态系统有毒性影响。因此，迫切需要可靠的系统来实现污水处理厂聚合物剂量的实时优化。目前的技术通常依赖于进入污泥的固体浓度或流出污泥的浊度，这在捕捉污泥的重要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调节和脱水优化。