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Record W1482601826 · doi:10.5539/mas.v9n7p140

Feed Plate and Feed Adsorbent Temperature Optimisation of Distillation – Adsorption Process to Produce Absolute Ethanol

2015· article· en· W1482601826 on OpenAlexvenueno aff
Fadlilatul Taufany, Nonot Soewarno, Koko Yuwono, Dimas Ardiyanta, Melvina Eliana, Indi Raisa Girsang

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
FundersDirektorat Jenderal Pendidikan TinggiInstitut Teknologi Sepuluh Nopember
KeywordsDistillationAdsorptionChromatographyFractional distillationEthanolChemistryTraySieve (category theory)ZeoliteBatch distillationFractionating columnMolecular sievePlate columnMaterials scienceMathematicsOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.012
GPT teacher head0.243
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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