Development of a Real-Time Intelligent Platform for Wastewater Influent Forecasting

废水进水预测实时智能平台开发

• 批准号: 533918-2018
• 负责人: Li, Zoe
• 金额: $ 0.04万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 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=698493
• 关键词: Development; Real; Time; Intelligent; Platform

Municipal wastewater treatment plants (WWTPs) require reliable influent forecast for the design, operation, and control of wastewater treatment processes. Inaccurate estimates of influent flow and load could easily lead to poor treatment performance and high operating cost. The complex collection and transport processes of urban effluent, as well as uncertainties arising from aging infrastructure, make it a daunting challenge for WWTP managers to generate reliable wastewater influent forecasts. In this project, a real-time intelligent platform for wastewater influent forecasting will be developed. This entails four tasks: 1) develop a pattern mining layer to characterize the temporal fluctuations in wastewater inflow and pollution loading; 2) develop an intelligent forecasting layer to generate reliable influent forecasts; 3) develop a real-time visualization layer to provide visualization of wastewater data and forecasts; and 4) integrate and commercialize the developed forecasting platform. The proposed research represents a unique contribution to the development of influent forecasting methodologies for supporting WWTP management. It will provided specialized training for highly qualified personnel. The students will gain expertise in data processing, time series analysis, pattern mining, and machine learning techniques, which are highly valued in consulting and academia. This work will be completed collaboratively between Hydromantis Environmental Software Solutions, Inc. and the applicant's research group at McMaster University. Hydromantis is one of the Canada's leading companies in wastewater treatment simulation. This project will allow them to integrate the developed forecasting platform into their current software products, which will give them a significant market advantage. The impacts of this work will be economically significant, as it will provide valuable technical support for making the maximum use of the country's existing and future wastewater treatment facilities.

城市污水处理厂（WWTPs）需要可靠的进水预测用于污水处理工艺的设计、运行和控制。进水流量和负荷的不准确估计容易导致处理性能差和运行成本高。城市污水复杂的收集和运输过程，以及老化的基础设施带来的不确定性，使污水处理厂管理人员在生成可靠的污水进水预测方面面临严峻的挑战。本项目将开发一个实时智能污水进水预报平台。这涉及四项任务：1）开发一个模式挖掘层，以表征废水流入和污染负荷的时间波动; 2）开发一个智能预测层，以生成可靠的流入预测; 3）开发一个实时可视化层，以提供废水数据和预测的可视化;以及4）集成和商业化开发的预测平台。 拟议的研究是一个独特的贡献，为支持污水处理厂管理的进水预测方法的发展。它将为高素质的人员提供专门培训。学生将获得数据处理，时间序列分析，模式挖掘和机器学习技术方面的专业知识，这些技术在咨询和学术界具有很高的价值。 这项工作将由Hydromantis Environmental Software Solutions，Inc.以及申请人在麦克马斯特大学的研究小组Hydromantis是加拿大领先的污水处理模拟公司之一。该项目将使他们能够将开发的预测平台集成到他们当前的软件产品中，这将为他们带来显着的市场优势。这项工作将产生重大的经济影响，因为它将为最大限度地利用该国现有和未来的废水处理设施提供宝贵的技术支持。