Model predictive control of two-step nitrification and its validation via short-cut nitrification tests
1a Guangzhou Water Investment Group Co. Ltd , Guangzhou , People's Republic of China.
Environmental Technology
|February 23, 2016
Summary
Short-cut nitrification (SCN) offers cost savings but lacks predictive models. A new two-step kinetic model accurately simulates SCN, enabling optimized biological nitrogen removal in wastewater treatment.
Area of Science:
- Environmental Engineering
- Wastewater Treatment Technologies
- Biochemical Engineering
Background:
- Short-cut nitrification (SCN) is an efficient wastewater treatment process reducing aeration and carbon source costs.
- Existing Activated Sludge Models lack the necessary state variable for nitrite to enable predictive control of SCN.
- Optimizing SCN is crucial for enhancing biological nitrogen removal efficiency and sustainability.
Purpose of the Study:
- To develop and validate a two-step kinetic model for simulating short-cut nitrification.
- To investigate the influence of operational parameters like dissolved oxygen, sludge retention time, and aeration time on SCN.
- To enable model predictive control for biological nitrogen removal processes utilizing SCN.
Main Methods:
- Development of a two-step kinetic model incorporating pH and temperature as control parameters.
- Implementation and simulation of the model in a Sequencing Batch Reactor (SBR) environment.
- Experimental validation of the model's predictions using data from an SBR reactor.
Main Results:
- The developed two-step kinetic model accurately simulates ammonia removal and nitrite production kinetics.
- Increasing dissolved oxygen initially enhances ammonia oxidation and nitrite accumulation for SCN.
- Elevated dissolved oxygen levels can shift SCN to complete nitrification; increased sludge retention time and aeration time favor nitrite accumulation.
Conclusions:
- The two-step kinetic model provides a reliable tool for simulating and optimizing SCN processes.
- Operational parameters can be manipulated to control the SCN pathway or achieve complete nitrification.
- The model's accuracy in predicting SCN performance supports its use in model predictive control for enhanced biological nitrogen removal.
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