Marine and fresh water growth and harvesting in a single photobioreactor : advantages and challenges
Bibliographic record
Abstract
Growth and harvesting are the two main steps contributing to high production costs of microalgae. These two steps were combined using a single vessel to grow marine (Pavlova pinguis) and fresh water (Chlorella vulgaris) microalgae. Combining growth and harvesting steps in a single vessel reduces capital as well as operation and maintenance (O&M) costs. Following the algae growth stage, biomass harvesting was obtained by electroflotation (EF) – electrocoagulation (EC) using a series of flat aluminum electrodes. These electrodes were inserted into the reactor between aligned grooves drilled on opposing sides. Electrical energy consumption during the harvesting stage was optimized as a function of the size and number of electrodes, and current density. Concentrated algal biomass was collected from the tip surface using a scoop. After harvesting, the spent water was assayed for residual nutrients and adjusted for a second growth cycle. Characteristics of the second and first growth cycles were compared. Energy, capital and operations cost savings were estimated. Challenges and solutions of the proposed photobioreactor (PBR) design are presented. For example, to reduce heat load transparent conductive oxide (TCO) coated glass materials were used to construct the PBR. Our recently acquired data show that TCO coated glass improved algae growth by almost 50%, compared to a non-coated control. TCO-coated glass blocks infrared radiation and eliminates the need for water spray cooling. More recently we have developed novel flexible TCO-coated plastic materials that could replace float glass for the construction of high efficiency and cost effective PBR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".