Synthesis and Application of Granular Activated Carbon from Biomass Waste Materials for Water Treatment: A Review
Bibliographic record
Abstract
There is an increased global demand for activated carbon (AC) in application of water treatment and purification. Water pollutants that have exhibited a greater removal efficiency by AC included but not limited to heavy metals, pharmaceuticals, pesticides, natural organic matter, disinfection by-products, and microplastics. Granular activated carbon (GAC) is mostly used in aqueous solutions and adsorption columns for water treatment. Commercial AC is not only costly, but also obtained from non-renewable sources. This has prompted the search for alternative renewable materials for AC production. Biomass wastes present a great potential of such materials because of their availability and carbonaceous nature. This in turn can reduce on the adverse environmental effects caused by poor disposal of these wastes. The challenges associated with biomass waste based GAC are their low strength and attrition resistance which make them easily disintegrate under aqueous phase. This paper provides a comprehensive review on recent advances in production of biomass waste based GAC for water treatment and highlights future research directions. Production parameters such as granulation conditions, use of binders, carbonization, activation methods, and their effect on textural properties are discussed. Factors influencing the adsorption capacities of the derived GACs, adsorption models, adsorption mechanisms, and their regeneration potentials are reviewed. The literature reveals that biomass waste materials can produce GAC for use in water treatment with possibilities of being regenerated. Nonetheless, there is a need to explore 1) the effect of preparation pathways on the adsorptive properties of biomass derived GAC, 2) sustainable production of biomass derived GAC based on life cycle assessment and techno-economic analysis, and 3) adsorption mechanisms of GAC for removal of contaminants of emerging concerns such as microplastics and unregulated disinfection by-products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".