Techno-economic analysis of laccase production, immobilization and use of the immobilized biocatalyst in hospital wastewater bioremediation
Bibliographic record
Abstract
• Copper sulfate showed a dual effect of inducing and repressing laccase activity. • Cost analyses show that laccase-based bioprocess is promising. • Cost of wastewater treatment by laccase is competitive and can be further reduced. • Reduction of labor and raw material cost can promote laccase-based bioprocess. In this study, cost analyses were performed to assess the economic feasibility and the competitiveness of a combined bioprocess for the production of a T. hirsuta laccase under submerged fermentation, immobilization of the crude enzyme and use of the formulated biocatalyst for hospital wastewater treatment. The laccase was immobilized on an amino-functionalized silica microsphere attached to a polyethylene scaffolding. Capital (CapEx) and operational (OpEx) expenses were calculated for each step of the process, considering the cost of equipment, raw materials, workload and utilities. For laccase production, labor cost represented the highest contributor to the total cost (51.6 %), followed by the equipment cost (42.2 %), while the costs of raw materials and energy were significantly low at 5.0 % and 1.1 %, respectively. The cost of the crude laccase was estimated to be 3.90 CAD (2024) kU −1 . In the immobilization step, the cost of raw materials represents the major cost contributor with 89.9 % due to the important cost of the immobilization support, while labor accounted for 6.4 %, equipment 3.6 % and energy 0.1 %. Sensitivity analyses were performed to investigate the cost dynamics with respect to the number of laccase production batches, the cost of raw materials, electricity, and labor. The cost of pilot-scale wastewater treatment using the formulated biocatalyst, taking into account a less expensive but similar immobilisation support, was estimated to be 0.87 CAD (2024) m −3 , which is a reasonable cost but can be further reduced after the process optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".