Using Aspen Plus to Simulate Pharmaceutical Processes – An Aspirin Case Study
Bibliographic record
Abstract
The use of chemical process simulators such as Aspen Plus for pharmaceutical applications has received relatively little attention. This is due, in part, to the specialized raw materials, products and unit operations that are involved in the processes. This paper illustrates that the Aspen Plus simulation system has many of the functional capabilities that make it an attractive tool for simulating pharmaceutical processes. These features include solids handling capabilities, batch processing, drying, process optimization including data reconciliation and parameter estimation, and finally cost estimation including capital and operating costs. These features are demonstrated using a process to manufacture the common over-the-counter pharmaceutical commonly referred to by its trade name, aspirin. Aspirin also known as ASA or acetylsalicylic acid is one of the oldest pharmaceuticals in wide spread use today and it is relatively simple to manufacture. However, the simulation of the process using traditional chemical process simulators is not straightforward and exhibit several difficulties involved in solids handling and batch processing. The synthesis of ASA is based on the acetylation of salicylic acid with acetic anhydride: C7H6O3 + C4H6O3 → C9H8O4 + CH3COOH salicylic acid + acetic anhydride → acetylsalicylic acid + acetic acid The reactants (salicylic acid and acetic anhydride) are mixed together with a small amount of sulphuric acid (H2SO4) acting as a catalyst. The mixture is then heated to about 50 °C to increase the reaction kinetics. The liquid mixture of unreacted salicylic acid and acetic anhydride along with acetylsalicylic acid and acetic acid products are then fed into a crystallizer operated about 3 °C. The objective of the crystallizer is to precipitate dissolved ASA into solid ASA by cooling the solution below the solubility limit. A hydro-cyclone is then employed to separate the solid ASA from the solid-liquid stream by spinning the mixture at high velocity. The cyclone separates the solid ASA from the solid-liquid stream while allowing about 15 % of the supernatant liquid to remain with the solid stream. The solid ASA plus supernatant stream are finally dried to ~ 17 wt.% moisture (dry basis) which is typical in ASA dryers. Aspen Plus was used to simulate a process designed to produce 145 kg/h of ASA. Based on this case study it was found that this process was able to convert about 78 wt.% of the reactants to ASA product at a production cost of approximately $3/kg. Based on this preliminary feasible process, process improvements in the form of optimization and process control were undertaken including studying the effect of the reactor temperature and recycle split fraction on the process operation and economics. Heat exchanger network synthesis and the production rate were also investigated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".