A dual approach for modelling and optimisation of industrial urea reactor: Smart technique and grey box model
Bibliographic record
Abstract
Abstract Urea has the highest demand among all solid nitrogenous fertilisers within the agriculture industry. In this paper, a mathematical model and an Artificial Neural Network (ANN) technique are proposed for the simulation and optimisation of the urea plant in an industrial petrochemical company. The developed mathematical model consists of complex vapour–liquid equilibria for the NH3–CO2–H2O–(NH2)2CO system in thermodynamic and reaction frameworks. The smart technique (e.g. ANN) considers the CO2 conversion in terms of temperature and the molar ratios of NH3/CO2 and H2O/CO2 in the liquid phase. The ANN predictions were compared with the real data and results obtained from the mathematical model. An acceptable agreement was attained between deterministic methods. Through implementation of a systematic sensitivity analysis, it was found that a temperature of 191°C, a pressure of 132 atm and a NH3/CO2 ratio of 2.7 are the optimum process conditions for the urea production. It is concluded that the developed ANN (or connectionist) technique is an efficient tool for modelling complex phase equilibria with reaction in the industrial urea plant.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".