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Design of a sequencing batch reactor sequence with an input load partition in a simulation-based experimental
1Department of Computer Automation and Control, J. Stefan Institute in Ljubljana, Slovenia.
Summary
Optimizing sequencing batch reactor (SBR) operations using a process model and pilot plant improved nitrogen removal efficiency. This approach reduced the need for external carbon, demonstrating the utility of models in wastewater treatment.
Area of Science:
- Environmental Engineering
- Wastewater Treatment Technologies
- Process Optimization
Background:
- Sequencing Batch Reactors (SBRs) are widely used for wastewater treatment.
- Optimizing SBR operational sequences is crucial for efficient nitrogen removal and reduced operational costs.
- Traditional experimental optimization on pilot plants can be resource-intensive.
Purpose of the Study:
- To optimize the operational sequence of a sequencing batch reactor (SBR).
- To achieve desired effluent nitrogen concentrations while minimizing or eliminating the need for external carbon addition.
- To evaluate the effectiveness of a process model in conjunction with pilot plant experiments.
Main Methods:
- Utilized the Activated Sludge Model No.1 (ASM1) with minor modifications for process modeling.
- Employed a laboratory pilot plant for experimental verification.
- Investigated a split-feed operating mode with optimized aerobic-anoxic phases, batch durations, and feeding times.
Main Results:
- The optimized SBR sequence, determined by the model, led to improved process performance when verified on the pilot plant.
- A reduction or elimination of external carbon addition was achieved.
- Observed some phenomena during pilot plant verification that were not predicted by the model, highlighting model limitations.
Conclusions:
- Process models, despite being simplifications, are valuable tools for optimizing SBR operations.
- The study successfully demonstrated the application of modeling and pilot-scale experiments for enhancing wastewater treatment efficiency.
- Further refinement of models may be needed to capture all real-world process complexities.