The Adiabatic Mononitrobenzene Process from the Bench Scale in 1974 to a Total World Capacity Approaching 10 Million MTPY in 2012
Bibliographic record
Abstract
The age of adiabatic mononitrobenzene (MNB) production began with a meeting held in July 1974 at the Canadian Industries Ltd. (CIL) Explosives Research Laboratory in McMasterville, Quebec, Canada. Two senior scientists of the American Cyanamid Company disclosed the adiabatic MNB concept, and invited CIL to contribute its sulfuric acid concentration technology, and lead the piloting of the adiabatic process. Three simple questions had to be answered at that time: What is the rate of by-products formation? Can the spent acid be recycled indefinitely? What scale-up rules should be applied to size industrial-scale stirred tank nitrators? The first adiabatic MNB plant was brought on line in 1979, in Louisiana, USA. At that time, the world’s MNB production was less than 1 million metric tonnes per year (MTPY), all coming from plants based on the incumbent isothermal technology. The world capacity in 2012 for MNB is now approaching 10 million MTPY, predominantly from adiabatic plants. This paper is a review of challenges which had to be overcome to bring the now dominant adiabatic MNB process to its current state of high reliability, high yield and energy efficiency, and excellent safety record. MNB capacity estimates quoted in this paper should be viewed as “best guesses” only. Producers keep production records confidential.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".