Feed Plate and Feed Adsorbent Temperature Optimisation of Distillation – Adsorption Process to Produce Absolute Ethanol
Bibliographic record
Abstract
Two subsequent separation processes are required to produce absolute ethanol (at least purity of 99.5% v/v),namely the distillation and adsorption processes. Thus, it is important to find the optimum operation conditionfor those following processes. The aims of the present study are to optimize the feed plate of distillation and thetemperature of feed adsorbent. This study is conducted using a continuous sieve tray distillation system with thenumber of 16 trays, the length-diameter ratio of 80.64, the reflux ratio of 3.5, and the feed with ethanol contentof 10 % v/v ethanol, which is produced via the fermentation process of molasses. To conduct the first aim of thisstudy, the feed enters the distillation column with several of variable feed plate, i.e. 12, 13, 14, and 15. This feedplate location is calculated from the top of the column. On the other hand, the second aim of this study, isconducted using the subsequent combination of distillation and adsorbent columns, where the distillate (purity ofethanol around 95% v/v) from the distillation column is then flowed into the adsorbent column with various feedadsorbent temperature, i.e. 80 °C, 90 °C, 100 °C, and 110 °C, to be purified as an absolute ethanol. Here theadsorbent column is designed as a fix bed adsorption column with a molecular sieve of 3A (zeolite) is used as anadsorbent for that purification process. Our results showed that the optimum feed plate is 14, because at thisplate the ethanol distillate has the highest content among those various variables. Meanwhile, the optimumtemperature of feed adsorbent is 90° C, which requires the least energy for the distillation - adsorption process,i.e. at 18691 kJ/kg absolute ethanol. This primary study is expected to be an alternative way to optimize theoperating condition of the sieve tray distillation-molecular sieve adsorption system by means of acquiring aminimum energy involving in the process to achieve the highest purity of ethanol.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".