Innovation Technology of Engineering Evaporation in the Accelerated Process into Old Brine with an Adaptive Fuzzy Logic Control
Bibliographic record
Abstract
Indonesia has significant potential for salt production but still faces challenges in meeting domestic demand.Although national salt production is projected to reach 2.04 million tons in 2024, Indonesia must continue importing around 2.8 million tons to meet the total national requirement of 4.8 million tons.This research aims to develop an adaptive engineering evaporation system capable of accelerating the transformation of young brine into mature (old) brine in a shorter period.The proposed innovation integrates an Artificial Heating System controlled by an adaptive Fuzzy Logic Control (FLC) to replace the dependency on solar heat and stabilize the evaporation process.The FLC algorithm simultaneously regulates two critical parameters: the artificial heating temperature to accelerate evaporation and the exhaust ventilation speed to maintain optimal room humidity.Experimental results demonstrate that the adaptive heating-ventilation system successfully increased brine concentration from 2-4 ° Be to 25-27 ° Be in only two days (approximately 48 hours), compared to conventional methods that require up to 30 days.The application of FLC effectively maintained thermal stability and adaptive environmental response, ensuring consistent evaporation performance and producing higher brine quality suitable for industrial salt production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".