Mass transfer and flow characterization of novel algae-based nutrient removal system
Bibliographic record
Abstract
BACKGROUND: Recirculating aquaculture systems (RAS) are an essential component of sustainable inland seafood production. Still, nutrient removal from these systems can result in substantial environmental problems, or present a major cost factor with few added value options. In this study, an innovative and energy-efficient algae based nutrient removal system (NRS) was developed that has the potential to generate revenue through algal commercialization. We optimized mass transfer in our NRS design using novel aeration and mixing technology, using air lift pumps and developed an original membrane cartridge for the continuous operation of nutrient removal and algae production. Specifically, we designed, manufactured and tested a 60-L NRS prototype. Based on specific airlift mixing conditions as well as concentration gradients, we assessed NRS nutrient removal capacity. We then examined the effects of different internal bioreactor geometries and radial orientations on the mixing efficiency. RESULTS: Using the start-up dynamic method, the overall mass transfer coefficient was found to be in the range of 0.00164-0.0074 [Formula: see text], depending on flow parameters and we confirmed a scaling relationship of mass transfer across concentration gradients. We found the optimal Reynolds number to be 500 for optimal mass transfer, as higher Reynolds numbers resulted in a relatively reduced increase of mass transfer. This relationship between mass transfer and Reynolds number is critical to assess scalability of our system. Our results demonstrate an even distribution of dissolved oxygen levels across the reactor core, demonstrating adequate mixing by the airlift pump, a critical consideration for optimal algal growth. Distribution of dissolved gases in the reactor was further assessed using flow visualization in order to relate the bubble distribution to the mass transfer capabilities of the reactor. We run a successful proof of principle trial using the green alga Dunaliella tertiolecta to assess mass transfer of nutrients across the membrane and biomass production. CONCLUSIONS: Manipulation of the concentration gradient across the membrane demonstrates a more prominent role of airlift mixing at higher concentration gradients. Specifically, the mass transfer rate increased threefold when the concentration gradient was increased 2.5-fold. We found that we can grow algae in the reactor chamber at rates comparable to those of other production systems and that the membrane scaffolds effectively remove nutrients form the wastewater. Our findings provide support for scalability of the design and support the use of this novel NRS for nutrient removal in aquaculture and potentially other applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".