The Impact of Inject Chemical Neutralization toward the PH Change in the Reject Water Management on the Raw Water Treatment Facilities in Petrochemical Industries
Bibliographic record
Abstract
<p><strong>ABSTRACT. </strong>The water waste management in Petrochemical Industry becomes the significant challenge in maintain environmental quality—particularly in regulating pH levels in accordance with the standards set by Indonesia’s Ministry of Environment and Forestry Regulation No. 5 of 2014—chemical injection is widely employed. This method involves the addition of acidic or alkaline agents to neutralize the pH of reject water. This study evaluates the effect of varying chemical injection dosages to determine the optimal dose required to achieve a pH range of 6 to 9. The findings demonstrate a direct relationship between the increase in chemical injection dosage and changes in pH levels, where higher dosages consistently raised the pH, stabilizing at an average value of 8.2. Over a one-month monitoring period, the optimal dosage was identified as 0.085 m³, resulting in an average pH of 6.47. Excessive dosing is not only less effective but also led to increase operational costs, reaching up to IDR 872,235. Thus, optimizing chemical injection dosage is critical—not only for ensuring compliance with environmental pH standards but also for minimizing chemical consumption and reducing operational expenditures.</p><p><strong>Keywords:</strong></p><p>Inject chemicals, Wastewater treatment, pH, Reject water</p>
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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 | 0.001 |
| 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 | 0.001 | 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".