Periodic Reporting for period 1 - OPTIUWS (System-wide Control of Iron-salts Addition in Urban Wastewater Systems to Achieve Optimal Management)
Période du rapport: 2021-02-01 au 2022-07-31
• What is the problem/issue being addressed?
In the last decades, increased awareness about the negative impact of eutrophication on the quality of water bodies and advances in environmental technology have given rise to more stringent wastewater treatment regulations and cost control in line with the EU policy. For instance, more stringent wastewater plant effluent quality and lower operational costs will compel wastewater communities to enhance the treatment efficiency. However, WWTPs are typically manipulated unefficiently due to difficult-to-measure variables, equipment and process faults as well as the resulted inaccurate control strategeis around the entire wastewater system. (i) Due to the extreme working conditions, the highly complex processes of microbial growth and large measurement delays, the measurements of effluent quality, such as BOD5 (biochemical oxygen demand for 5 days), COD (chemical oxygen demand), and TN (total nitrogen) as well as N2O, are usually difficult; (ii) Due to a large number of sensors and automation systems being equipped to collect process data as well as operate WWTPs, fault management is indispensible and able to better safe operations, which is viewed as a set of activities: fault detection, diagnosis, prognosis and maintenance; (iii) Depending on the difficult-to-measure variables and accurate fault diagnosis, accurate design of control strategies could further enhance the wastewater treatment efficiency with lower costs. Recognising and solving aforementioned problems would deliver system optimisation with tremendous economic and environmental benefits.
• Why is it important for society?
(1) Fristly, this project is able to reduce operational costs due to multiple-beneficial and optimal use of chemcals, then further to lower risk of contamination and reduce greenhouse gas emissions.
(2) Secondly, this project helps wastewater industries to meet the stringent effluent qualities and their sustainability targets.
(3) Thirdly, this project expands the ER’s research capacity in four focus areas. (i) WWTP process modelling: ; (ii) Interdisciplinary skills; (iii) Potential industrial applications; (iv) Research collaboration of China and EU, i.e. NSFC-European Commission.
(4) Fouthly, this project can disseminate wastewater management knowledge to society and then to enhance the understanding and awareness of the negative impact of eutrophication.
• What are the overall objectives?
Overall, the essence of this project is to develop and demonstrate a decision-support system to achieve multiple-beneficial and optimal management of a wastewater treatment system. In this light, soft-sensors are developed to sense difficult-to-measure variables. By resorting to the soft-sensors, hidden information can be refined and delivered for sequential fault detection and diagnosis. Then, fault detection and diagnosis are able to ensure safe operations of WWTPs. With the help of soft-sensors amd fault daignosis methodologies, various (Local/global) control strategies as well as coordination of them are developed to optimise operations, such as iron salts dosage, oxidation and inter-recycling. The integrated decision supporting system will thus substantially reduce the use of chemicals and oxidation, delivering large economical and environmental benefits to urban water utilities in the EU. The project will set an excellent example for integrated urban water management in the EU.
In the last decades, increased awareness about the negative impact of eutrophication on the quality of water bodies and advances in environmental technology have given rise to more stringent wastewater treatment regulations and cost control in line with the EU policy. For instance, more stringent wastewater plant effluent quality and lower operational costs will compel wastewater communities to enhance the treatment efficiency. However, WWTPs are typically manipulated unefficiently due to difficult-to-measure variables, equipment and process faults as well as the resulted inaccurate control strategeis around the entire wastewater system. (i) Due to the extreme working conditions, the highly complex processes of microbial growth and large measurement delays, the measurements of effluent quality, such as BOD5 (biochemical oxygen demand for 5 days), COD (chemical oxygen demand), and TN (total nitrogen) as well as N2O, are usually difficult; (ii) Due to a large number of sensors and automation systems being equipped to collect process data as well as operate WWTPs, fault management is indispensible and able to better safe operations, which is viewed as a set of activities: fault detection, diagnosis, prognosis and maintenance; (iii) Depending on the difficult-to-measure variables and accurate fault diagnosis, accurate design of control strategies could further enhance the wastewater treatment efficiency with lower costs. Recognising and solving aforementioned problems would deliver system optimisation with tremendous economic and environmental benefits.
• Why is it important for society?
(1) Fristly, this project is able to reduce operational costs due to multiple-beneficial and optimal use of chemcals, then further to lower risk of contamination and reduce greenhouse gas emissions.
(2) Secondly, this project helps wastewater industries to meet the stringent effluent qualities and their sustainability targets.
(3) Thirdly, this project expands the ER’s research capacity in four focus areas. (i) WWTP process modelling: ; (ii) Interdisciplinary skills; (iii) Potential industrial applications; (iv) Research collaboration of China and EU, i.e. NSFC-European Commission.
(4) Fouthly, this project can disseminate wastewater management knowledge to society and then to enhance the understanding and awareness of the negative impact of eutrophication.
• What are the overall objectives?
Overall, the essence of this project is to develop and demonstrate a decision-support system to achieve multiple-beneficial and optimal management of a wastewater treatment system. In this light, soft-sensors are developed to sense difficult-to-measure variables. By resorting to the soft-sensors, hidden information can be refined and delivered for sequential fault detection and diagnosis. Then, fault detection and diagnosis are able to ensure safe operations of WWTPs. With the help of soft-sensors amd fault daignosis methodologies, various (Local/global) control strategies as well as coordination of them are developed to optimise operations, such as iron salts dosage, oxidation and inter-recycling. The integrated decision supporting system will thus substantially reduce the use of chemicals and oxidation, delivering large economical and environmental benefits to urban water utilities in the EU. The project will set an excellent example for integrated urban water management in the EU.
(1) Soft-sensors: Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors are widely used to predict and monitor these variables and then to optimize plant operations. Machine learning based soft-sensors are proposed to monitor the effluent qualities, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN) as well as the highly dynamic N2O emissions in the plant-wide wastewater plants.
(2) Fault management: Due to complexities of systems, instrumentations and dataa review of this project goes beyond the state of the art by critically analyzing previous works on AI-based data-driven methodologies to system-wide fault detection, life cycle fault management and transformation of big and small data into analytics, particularly, from two different points of view: process faults (such as bulking sludge, sewer corrosion & technology specifics) and instrumentation faults (such as sensors and actuators), thereby offering more opportunities to distinguish complex patterns and dynamics. Our analysis reveals the relative strengths and weaknesses of the different approaches to design fault diagnosis tools and to apply these in the UWS. Finally, the opportunities and challenges are discussed.
(3) Several control strategies are proposed to optimize WWTPs.
In summary, by implementing the project, an overview results and their exploitation and dissemination can be seen as follows:
(i) 2 peer review papers have been pulished and 3 peer review papers are under review. Particularly, the paper “Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling” was published in the open access journal “Water”. This will facilitate the dissemination of this project. Also, 1 conference paper has been accepted by the “IWA World Water Congress 2022” as a poster. This will further enhance the project dissemination.
(ii) We have taken part in the “BioProScale 2022” conferece in Berlin and the course “Artificial Intelligence in Biochemical and Chemical Engineering”.
(iv) We have a secondment to City University of Hongkong during 21st-31st, July, 2022. In this period, we have taken part in the Hong Kong Tech Forum on Data Science and AI (DSAI) in City University of Hongkong. Also, we have a meeting and discussion on how to involve reliabilty analysis theory in the urban wastewater treatment system.
(v) A PhD student, Dong Li, from South China University of Technology joined the PROSYS group from Feb, 2021 to Feb, 2022. He was working on the soft-sensor modeling and assist the modeling works in this project. This further help the communication and worldwide applications of this project.
(2) Fault management: Due to complexities of systems, instrumentations and dataa review of this project goes beyond the state of the art by critically analyzing previous works on AI-based data-driven methodologies to system-wide fault detection, life cycle fault management and transformation of big and small data into analytics, particularly, from two different points of view: process faults (such as bulking sludge, sewer corrosion & technology specifics) and instrumentation faults (such as sensors and actuators), thereby offering more opportunities to distinguish complex patterns and dynamics. Our analysis reveals the relative strengths and weaknesses of the different approaches to design fault diagnosis tools and to apply these in the UWS. Finally, the opportunities and challenges are discussed.
(3) Several control strategies are proposed to optimize WWTPs.
In summary, by implementing the project, an overview results and their exploitation and dissemination can be seen as follows:
(i) 2 peer review papers have been pulished and 3 peer review papers are under review. Particularly, the paper “Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling” was published in the open access journal “Water”. This will facilitate the dissemination of this project. Also, 1 conference paper has been accepted by the “IWA World Water Congress 2022” as a poster. This will further enhance the project dissemination.
(ii) We have taken part in the “BioProScale 2022” conferece in Berlin and the course “Artificial Intelligence in Biochemical and Chemical Engineering”.
(iv) We have a secondment to City University of Hongkong during 21st-31st, July, 2022. In this period, we have taken part in the Hong Kong Tech Forum on Data Science and AI (DSAI) in City University of Hongkong. Also, we have a meeting and discussion on how to involve reliabilty analysis theory in the urban wastewater treatment system.
(v) A PhD student, Dong Li, from South China University of Technology joined the PROSYS group from Feb, 2021 to Feb, 2022. He was working on the soft-sensor modeling and assist the modeling works in this project. This further help the communication and worldwide applications of this project.
(i) Due to the costs of hardware sensors, for example COD and BOD, as well as their corresponding maintenance, soft-sensors can provide a cost-efficient way to monitor the critical quality variables in the WWTPs. Even though N2O sensors are not expensive as other hardware sensors, such as COD and BOD, soft-sensor can avoid the time-delay of hardware counterparts. It is envisioned that soft-sensors will offer a cost-saving way for WWTPs monitoring and management. Also, predicting N2O can better the GHG (Green Gas) emission control in the wastewater system.
(ii) Involvement of Fault management components in the decision-making system can ensure WWTPs being operated safely and betterly meet the increasing effluent standard of quality. This is only confounded as contries continue to experience heavily polluted waterways, affecting human life as well as aquatic and terrestrial life. This senarios continue to be worsen as countries continue to industialize and modernize.
(ii) Involvement of Fault management components in the decision-making system can ensure WWTPs being operated safely and betterly meet the increasing effluent standard of quality. This is only confounded as contries continue to experience heavily polluted waterways, affecting human life as well as aquatic and terrestrial life. This senarios continue to be worsen as countries continue to industialize and modernize.