Simultaneous Manganese Removal and Remineralization of Soft Waters Via Calcite Contactor
Bibliographic record
Abstract
phase, the negative impact of newly-formed Mn-layer on calcite dissolution rate using continuous desorption-dissolution experiments in smaller columns was investigated.For an elevated Mn concentration (i.e.5.0 mg Mn L -1 ) in the feed water, the coated layer was mainly composed of Mn which inhibits the mass transfer from the calcite core to the liquid phase.The superficial layer was identified as 5.2% Mn oxides (MnOx) by X-ray photoelectron spectroscopy (XPS).Therefore, it is postulated that Mn removal starts with an ion exchange sorption reaction between soluble Mn 2+ from aqueous phase and Ca 2+ from the CaCO3 matrix which is followed by a slow recrystallization of MnCO3 into MnO2.On the other hand, when the Mn content in the feed water was lower (i.e.0.5 mg Mn L -1 ), a considerably lower amount of MnOx was detected on the coated media.For all the examined conditions, the formation of this coating improved Mn removal due to the autocatalytic nature of adsorption/oxidation of dissolved manganese by MnOx.As for the third phase, the long-term efficiency of a calcite contactor was modeled using a mechanistic model based on calcite dissolution and progressive formation of a MnO2 layer which was implemented in PHREEQC software using a MATLAB interface via IPHREEQC modules to predict the reduction in hardness release expected in long-term operation.The model was calibrated with experimental data and resulted in realistic breakthrough curves.In order to accurately predict the pH of the effluent stream, a slow-rate recrystallization of MnCO3 into MnO2 was implemented (compared to fast precipitation of MnO2 or absence of MnO2 formation).Finally, given that after long-term operation of the calcite contactor in elevated Mn concentrations, the remineralization objective was not fully met, a possible solution was tested using a blend of calcite and Corosex TM (MgO) as the filtration media.For this purpose, first optimum ratio of 80% / 20% for Calcite/ Corosex TM was obtained, then the column was operated under elevated Mn concentration (5 mg L -1 Mn 2+ ) with the chosen ratio to investigate the efficiency of the contactor in remineralization of soft water with simultaneous Mn removal.The column demonstrated high efficiency: in the long-term operation, the contactor was able to remove over 99% of dissolved Mn from the synthetic feed water (SFW) and added above 40 mg CaCO3/L of hardness to the soft feed.The stable condition was reached much sooner than the previous phase when only calcite was used (10 h of operation versus above 100 h of operation).Accumulation of newly formed Mn residue increased the head loss overtime and caused filter clogging.It is important to note that using a blend of calcite and Corosex TM does not seem like a promising option for long-term operation if the Mn concentration in the feed is unrealistically high (i.e. 5 mg L -1 Mn 2+ ).However, for more realistic Mn concentrations (i.e.0.2 mg/L and less), adding a small portion of MgO to the filter would help to improve the hardness addition.
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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".