Henrik Thomsen
Henrik is a highly experienced chemical engineer specializing in wastewater treatment and process optimization. With over 30 years of experience in real-time optimization and on-line measurement, he has developed significant expertise in activated sludge processes and nutrient removal. His research focuses on developing algorithms for real-time optimization of wastewater treatment plants, resulting in several patents for optimization methods. They have extensive experience in designing and implementing advanced process control systems, particularly the Hubgrade Wastewater Performance Plant (formerly STAR AQUAVISTA) system for wastewater treatment optimization. Key areas of research include:
1. Energy savings in aeration tanks through reduced mixing
2.Balancing sludge blankets and flow distribution in final settlers
3. Online estimation of nitrification rates
4. Application of instrumentation, control, and automation (ICA) in wastewater systems
His work has been published in respected journals such as Water Science and Technology and presented at international conferences. He has demonstrated the practical benefits of ICA application in over 50 wastewater systems over a 15-year period, showcasing both OPEX and CAPEX savings through optimized operations and advanced process control. Henrik was keynote speaker at a former ICA conference: 10th IWA Conference on Instrumentation, Control and Automation, Cairns, Australia, 2009. With a Bachelor’s degree in Chemical Engineering with Extended Process Control from the Technical University of Denmark, Henrik combines academic knowledge with extensive practical experience in wastewater treatment plant optimization and management.
onsdag
22 oktober
14:30 - 14:50
Optimizing Efficiency and Sustainability: Real-Time Control technologies in Wastewater Treatment at Skreia, Norway
The article "Sustainable real-time optimization of energy and chemical consumption in a COD & Phosphorous removing MBBR plant in Norway" details a case study at the Skreia Wastewater Treatment Plant (WWTP) in Østre Toten Municipality, Norway. The WWTP, designed for 18,350 population equivalents, employs a Moving Bed Biofilm Reactor (MBBR) system followed by high-rate clarification using Actiflo technology for the removal of COD (Chemical Oxygen Demand) and Phosphorus.
The primary goal of the project was to optimize operations for sustainability by implementing real-time control strategies. Traditionally, WWTPs operate with fixed high levels of aeration and chemical dosage to ensure compliance even during peak load periods, leading to high operational costs and greenhouse gas emissions. The Skreia WWTP aimed to move away from this standard practice by introducing a more dynamic and responsive control system.
The original instrumentation setup was expanded with additional sensors to monitor parameters like turbidity, nitrate, Total Organic Carbon (TOC), and Total Phosphorus (TP) in the effluent. This data was integrated into the Hubgrade Wastewater Plant Performance (HWP) platform, a digital tool that leverages live data and artificial intelligence for real-time optimization.
A key innovation was the implementation of a new aeration control strategy that moved away from continuous aeration at a fixed Dissolved Oxygen (DO) setpoint. Instead, the system calculated the Oxygen Uptake Rate (OUR) in real-time based on DO, water temperature, and airflow measurements. This allowed for a dynamic DO setpoint and the introduction of intermittent aeration during low load periods. The system also included compensation components based on TOC and nitrate levels to prevent insufficient aeration and nitrification, respectively, further optimizing energy use and reducing greenhouse gas emissions like nitrous oxide (N2O).
Similarly, the chemical dosage control strategy was upgraded from a fixed rate to a cascade PID controller system. This system used TP levels to calculate a turbidity target, which then controlled the dosage of coagulant and polymer in the Actiflo units. A pH compensation component was also added to prevent excessive chemical dosage and ensure the effectiveness of the chemicals.
The results were significant. Compared to the design expectations and the initial operation with standard controls, the new real-time control strategy led to a 51% reduction in energy consumption, a 26% reduction in coagulant dosage, and a 20% reduction in polymer dosage. These savings also translated to a 50-tonne reduction in CO2-eq emissions per year. The effluent quality remained compliant throughout the implementation, demonstrating the effectiveness of the new strategies.
The article highlights the importance of real-time data and advanced control systems in optimizing wastewater treatment operations. It also acknowledges the challenges related to sensor reliability, which will be further discussed in the full paper. Overall, the case study demonstrates a successful approach to making wastewater treatment plants smarter, safer, and more sustainable.
- Språk:
- Engelska
- Lokal:
- Innovationsscenen
The article "Sustainable real-time optimization of energy and chemical consumption in a COD & Phosphorous removing MBBR plant in Norway" details a case study at the Skreia Wastewater Treatment Plant (WWTP) in Østre Toten Municipality, Norway. The WWTP, designed for 18,350 population equivalents, employs a Moving Bed Biofilm Reactor (MBBR) system followed by high-rate clarification using Actiflo technology for the removal of COD (Chemical Oxygen Demand) and Phosphorus.
The primary goal of the project was to optimize operations for sustainability by implementing real-time control strategies. Traditionally, WWTPs operate with fixed high levels of aeration and chemical dosage to ensure compliance even during peak load periods, leading to high operational costs and greenhouse gas emissions. The Skreia WWTP aimed to move away from this standard practice by introducing a more dynamic and responsive control system.
The original instrumentation setup was expanded with additional sensors to monitor parameters like turbidity, nitrate, Total Organic Carbon (TOC), and Total Phosphorus (TP) in the effluent. This data was integrated into the Hubgrade Wastewater Plant Performance (HWP) platform, a digital tool that leverages live data and artificial intelligence for real-time optimization.
A key innovation was the implementation of a new aeration control strategy that moved away from continuous aeration at a fixed Dissolved Oxygen (DO) setpoint. Instead, the system calculated the Oxygen Uptake Rate (OUR) in real-time based on DO, water temperature, and airflow measurements. This allowed for a dynamic DO setpoint and the introduction of intermittent aeration during low load periods. The system also included compensation components based on TOC and nitrate levels to prevent insufficient aeration and nitrification, respectively, further optimizing energy use and reducing greenhouse gas emissions like nitrous oxide (N2O).
Similarly, the chemical dosage control strategy was upgraded from a fixed rate to a cascade PID controller system. This system used TP levels to calculate a turbidity target, which then controlled the dosage of coagulant and polymer in the Actiflo units. A pH compensation component was also added to prevent excessive chemical dosage and ensure the effectiveness of the chemicals.
The results were significant. Compared to the design expectations and the initial operation with standard controls, the new real-time control strategy led to a 51% reduction in energy consumption, a 26% reduction in coagulant dosage, and a 20% reduction in polymer dosage. These savings also translated to a 50-tonne reduction in CO2-eq emissions per year. The effluent quality remained compliant throughout the implementation, demonstrating the effectiveness of the new strategies.
The article highlights the importance of real-time data and advanced control systems in optimizing wastewater treatment operations. It also acknowledges the challenges related to sensor reliability, which will be further discussed in the full paper. Overall, the case study demonstrates a successful approach to making wastewater treatment plants smarter, safer, and more sustainable.