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Record W4396701096 · doi:10.11159/iceptp24.146

Desulfurization Wastewater Evaporation Technology: Field Test, Product Analysis and Potential Risk Assessment

2024· article· en· W4396701096 on OpenAlexvenueno aff
Heng Chen, Linjun Yang

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterFlue-gas desulfurizationEnvironmental scienceProduct (mathematics)EvaporationWaste managementEnvironmental engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Desulfurization wastewater evaporation technology is an effective approach to achieve zero liquid discharge.However, limited information on the practical engineering performance and the evaporation product properties was a lack of awareness.In this study, we conducted a field test of desulfurization wastewater evaporation technology in a demonstration project.The results indicated that the technology was highly feasible and applicable to operating conditions with varying desulfurization wastewater flow rates.Maintaining the low moisture content of the evaporation product was crucial for successfully implementing wastewater evaporation, and exceeding a certain threshold may lead to issues such as wall sticking and blockage of the conveying pipeline.The moisture content of the evaporation product for operation conditions Ⅰ, ⅠⅠ, and ⅠⅠⅠ were 1.2%, 0.9%, and 0.7%, respectively, which could meet the engineering standards.The migration characteristics of chloride were investigated, revealing that less than 3% of Cl was released into gaseous HCl.The concentrations of other typical pollutants, such as NOx and SO2, remained unchanged after treatment, indicating that the wastewater evaporation technology did not significantly affect the distribution of gaseous pollutants.For the influence of wastewater evaporation on comprehensive fly ash utilization, the chloride mass fraction in the final product was lower than 0.06%, indicating that it could meet strict regulations and would not affect comprehensive fly ash utilization.Moreover, the study found that the evaporation process promoted the agglomeration of particulate matter, as confirmed by larger particle sizes and XRD analysis.It may slightly promote the dust removal efficiency of the electrostatic precipitator due to the aggregation effect of fine particles and the enhanced specific resistance.In this study, the single droplet drying method and spray drying system revealed the evaporation characteristics and product properties.The wastewater droplet evaporation process could be divided into two periods: constant rate and falling rate in the evaporation process.Besides, a shell would form when the critical concentration was achieved on the droplet surface.Then, the inflation, rupture, and re-inflation stages would be involved in the history of shell evolution.The components of the evaporation product mainly consisted of MgCl2, CaCl2, NaCl, and CaSO4.The total operating cost of wastewater evaporation ranged from 25 to 30 yuan per ton of wastewater, primarily consisting of coal consumption for extracting flue gas.The data obtained in this work complements and verifies existing research on wastewater evaporation technology.However, some potential risks and existing challenges of wastewater evaporation technology must be assessed.When the chloride content of the fly ash had more stringent requirements, installing a small bypass dust collector after the evaporation drying tower could be considered.By this means, all evaporation products were collected separately, avoiding any impact on the downstream equipment.Besides, the collected evaporation product could be mixed with the boiler slag, used in bricks and other industries that did not limit the chloride content or require low requirements to avoid the negative impact of wastewater evaporation products on the comprehensive fly ash utilization.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.185
Teacher spread0.183 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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