Optimisation of an Agitated Thin Film Evaporator for Concentrating Orange Juice Using Aspen Plus
Bibliographic record
Abstract
Abstract This paper presents an application for the optimisation of an existing agitated sdthin film evaporator (ATFE) for concentrating orange juice using Aspen PlusTM. A rigorous heat exchanger mode (Heatx) and a rigorous two‐phase flash model (Flash2) were used to simulate the dominant effects of the ATFE. The thermo‐physical properties of orange juice, not available in Aspen Plus, were determined experimentally and correlated as functions of temperature and solids content by Boonsriudomsuk [1]. Effective heat transfer coefficients were calculated from measured temperatures and flow rates. Experiments were performed on a laboratory‐scale and pilot plant system and compared with the simulation results. The Aspen Plus simulation model using experimentally determined heat transfer coefficients and thermo‐physical properties of orange juice compared well with the experimental data from the ATFE. When the mass and energy balance data were reconciled the errors between both experimental and simulation results were significantly decreased. The optimisation results indicated that by operating at the optimum operating conditions the operating costs could be reduced by about 10%. This translates into savings of more than $10.000/vear in the case of the laboratory‐scale evaporator and $33,000/year in the case of the pilot plant. If a commercial ATFE process was optimised then the potential savings could approximate to $330,000/year. Clearly, process optimisation is a valuable tool in the design and operation of these processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".