Influence of Ambient Air Temperature on the Cooling/Heating Load of a Single Cell Protein Jacketed Fermenter Operating on Cheese Whey under Continuous Conditions
Bibliographic record
Abstract
The heat generated by mixing and lactose metabolism, during the continuous production of single cell protein from cheese whey lactose using a jacketed fermenter with running cooling water, was calculated using a heat balance equation. The technique quantified the heat produced in and lost from the fermentation unit. Most of the heat generated by mixing in the cell-free system (97.47%) was lost with exhaust gas, while a very small amount (2.53%) was lost through the fermenter lid, wall, and bottom. The heat generated by mixing was significant (26.31% of the total heat generated in the fermentation system with an active yeast population present) and, therefore, cannot be ignored in heat balance calculations. About 19.71% of the total heat generated in the reactor was lost through the coolant at an ambient temperature of 22 +/- 0.5 degrees C, showing the need for a cooling system. A yeast population size of 986 million cells/mL and a lactose removal efficiency of 95.6% were observed. About 72.5% and 27.5% of the lactose consumed were used for growth and respiration, respectively. A yield of 0.66 g of cells/g of lactose was achieved. The heat released by unit biomass was 7.05 kJ/g of cells. The results showed the significant impact of ambient air temperature on the cooling load. The heat to be removed from the medium by the cooling system varied from 3.46 to 281.56 kJ/h when the temperature increased from 16 to 30 degrees C. A heating system is needed to maintain the medium temperature at 34 degrees C when the ambient air temperature is below 16 degrees C.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".