Grant: Collaborative Research and Development Grants
McMaster University
$500.00
Development of a Real-Time Intelligent Platform for Wastewater Influent Forecasting_x000d_ _x000d_ _x000d_
FY 2020-21
Summary
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.
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
533918-2018
Name nom
Li, Zoe
Department département
Civil Engineering
Organization id
27
Institution établissement
McMaster University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2018
Award amount
500
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1002
Area of application group en
Northern development
Area of application group fr
Développement du nord
Area of application en
Environment
Area of application fr
Environnement
Research subject code
1000
Research subject group en
Civil engineering
Research subject group fr
Génie civil
Research subject en
Civil engineering
Research subject fr
Génie civil
Installment
inst:2 - 2
Partie
1
Nb partie
2
Application title
Development of a Real-Time Intelligent Platform for Wastewater Influent Forecasting_x000d_
_x000d_
_x000d_
Keyword
NSERC
Application summary
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.
