Retrospective bench-scale reanalysis reveals variable phosphorus recovery across six phosphate concentrates, indicating feed grade alone does not ensure optimal wet-process acid performance.
Variability in phosphate-concentrate chemistry can affect sulfuric acid demand, phosphorus recovery, calcium sulfate formation, and solid–liquid separation during wet-process phosphoric acid (WPA) production. This study presents a retrospective, quality-controlled reanalysis of a historical bench-scale campaign comprising six coded phosphate concentrates (PQH-001 to PQH-006). Each concentrate was represented by one complete WPA bench test; accordingly, comparisons are descriptive rather than inferential. Feed P₂O₅ ranged from 27.59 to 30.55 wt%, whereas the minor-elements ratio (MER = 100 × (Al₂O₃ + Fe₂O₃ + MgO)/P₂O₅) ranged from 7.12 to 16.83%. The surviving records documented 98 wt% sulfuric acid, phosphoric acid, and gypsum streams, filtration calculations, and duplicate hydrochloric acid attack tests, but did not preserve several operating variables required for mechanistic attribution. Observed P₂O₅ recovery ranged from 94.68 to 97.99%. The highest-grade feed, PQH-004 (30.55 wt% P₂O₅), showed the lowest recovery (94.68%) and the highest residual P₂O₅ in dry gypsum (1.083 wt%), demonstrating that feed grade alone did not rank performance in this campaign. PQH-005 showed the most favorable observed combination of metrics: 97.99% recovery, 0.405 wt% P₂O₅ in dry gypsum, a filtration index of 185.3 kg h⁻¹ m⁻², and sulfuric-acid use of 2.603 t H₂SO₄/t feed P₂O₅, approximately 1% above the CaO-only stoichiometric benchmark. A stoichiometric QA/QC audit identified the reported PQH-003 acid addition as chemically inconsistent, and its filtration result was also non-defensible because a required dry-cake term was missing. The results support a staged, multi-parameter qualification approach based on feed chemistry, stoichiometric consistency, phosphorus deportment, and filtration behavior, followed by replicated modern bench and pilot validation before industrial scale-up.
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Antonio Clareti Pereira,, PhD (2026) studied this question.
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