Bioprocess design and economic analysis for the commercial production of environmentally friendly bioinsecticides from <i>Bacillus thuringiensis</i> HD‐1 <i>kurstaki</i>
Bibliographic record
Abstract
A production process for B. thuringiensis (Bt) bioinsecticides was designed in detail, including alternative batch, low-density fed-batch (LDFB), and high-density fed-batch (HDFB) fermentation configurations. Capital and operating costs, as well as profitability based on simple rate of return, were performed using a purpose-written FORTRAN program, explicitly analyzing production of a water-based flowable product used in forestry applications. The total capital cost was 18 million dollars (Canadian dollars) for a stand-alone plant with base-scale capacity of 3 x 10(7) billion international units (BIU)/year. Raw material costs amounted to 1.5 million dollars yearly, of which approximately half was for formulation ingredients. Per-unit production cost rose sharply for scales of less than 1 x 10(7) BIU/year, but was little affected by scale above 3 x 10(7) BIU/year. Product cost was much lower at all scales for a LDFB as opposed to batch fermentation process, but HDFB gave relatively little additional cost benefit. Profitability analysis performed by co-varying scale and selling price showed that break-even occurred at a price of 0.45 dollars/BIU for a batch process at base scale, while with LDFB fermentation the same production volume sold at 0.35 dollars/BIU gave a 12% rate of return. Since the assumed base scale would represent 8-15% of current world Bt bioinsecticide production, based on value or volume, it was concluded that profitability would require some or all of the following elements: targeting higher-value markets such as disease vector control, in addition to forestry; a potentially lower plant capacity (although at least 1 x 10(7) BIU/year;) and coproduction of other large-volume microbial products to absorb capacity and match bioinsecticide output to market demand.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
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".