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Enregistrement W6986947957

Rening av kalifältspat laklösning – Utvinning av kalium

2024· article· en· W6986947957 sur OpenAlexaboutno aff

Notice bibliographique

RevueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2024
Typearticle
Langueen
DomaineMaterials Science
ThématiqueClay minerals and soil interactions
Établissements canadiensnon disponible
Organismes subventionnairesVINNOVA
Mots-clésPotashPotassiumAgricultureExtraction (chemistry)Fertilizer
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Potassium is an essential element for the growth of plants. Soluble potassium salts, known as potash, are used as fertilizers to fulfil plant nutritional needs and to boost crop production. It is projected that the demand for potash in the agriculture industry would increase at a compound annual growth rate of 3.2% between 2024 and 2029. Currently, potash is commercially mined from sea brines and buried ancient seas. This makes potash extraction geographically confined, with substantial commercial reserves concentrated in a few countries, including Canada, Russia, Belarus, China, and Germany. Due to the limited number of suppliers, the global potash market is highly sensitive to international relations, trade policies, and even conflicts. Potassium can be found abundantly in certain clays and rock-forming minerals. K-bearing feldspars are estimated to account for approximately 12 wt. % of the earth’s crust and they can contain up to 14 wt.% of potassium. This abundance makes K-feldspar an attractive potential potash source. The widespread availability of K-feldspar across various regions of the world suggests that, if economically viable extraction methods can be developed, it could contribute to diversifying the potash supply chain and to meet the growing demand for this mineral. However, still, economic extraction of potash from these minerals is not feasible. This work is part of the ERA-MIN project POTASSIAL, which aims to develop a zero-waste process for the treatment of K-feldspar in order to provide an alternative source of potash and alumina. The main processing steps suggested to produce potash and alumina involve intensive grinding, HCl leaching, separation and purification processes, crystallization, and roasting. This doctoral project is part of the work package aimed at developing a suitable separation and purification approach for treating the leaching solution, with an emphasis on potassium recovery. Two different recovering/purification methods, namely anti-solvent crystallization and solvent extraction have been included in this work for the recovery of potassium. The selection of these methods was based on the following requirements set for the formulation of the purification processes: (i) minimal neutralization of the leaching solution, (ii) maximum separation of impurities, and (iii) feasible concentration of potassium. Anti-solvent crystallization was selected for direct and selective recovery of potassium in the form of muriate of potash. The aim for using this process is to combine both purification and crystallization steps for potassium recovery. Initially, screening experiments were carried out with the aim of analysing the crystallization behaviour of key components. Screening experiments were performed using five anti-solvents, namely methanol, ethanol, acetone, 2-propanol, and ethylene glycol. Acetone and 2-propanol were viable options for crystallization of potassium chloride. Using acetone and 2-propanol, the effects of anti-solvent ratio, time, and anti-solvent addition rate on potassium-chloride crystallization were then further investigated. A recovery of 83% of potassium was achieved when using acetone at the O/A of 5 with the addition rate of 10 ml/min, at room temperature with a hold time of 180 minutes. The optimum conditions for 2-propanol were determined to be similar, except for using a 5 ml/min addition rate for 79% recovery. The final muriate of potash products had a purity of over 99.9% using either of the anti-solvent. However, differences in morphology and crystal size of the products were observed. Experiments were followed by determining the efficiency of solvent extraction using crown ethers for possible purification and concentration of potassium from the leaching solution. This approach was chosen for situations in which the potassium content in the leaching solution is inadequate for direct crystallization and crystallization methods other than anti-solvent are applied. Crown ethers were chosen as the extractant due to their ability to function in highly acidic environments. Accordingly, the effects of HCl concentration, extractant type, diluent, extractant concentration, and organic-to-aqueous phase ratio on potassium extraction efficiency was examined. Dibenzo-18-crown-6 diluted in m-cresol showed to preferentially extract potassium (85% recovery) from highly acidic HCl solutions (2 to 6 M), with minimal co-extraction of impurities, such as aluminium and sodium. Finally, two scenarios were considered for the purification of K-feldspar leaching solution to recover potassium: 1- Anti-solvent crystallization followed by distillation (for the recovery of the anti-solvent) and 2- Solvent extraction with crown ethers followed by evaporative crystallization (to recover potash). Economic factors associated with these scenarios were examined to establish the conditions under which each treatment approach is preferred for recovering potash from a K-feldspar leaching solution.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,061
Score d'incertitude au seuil0,204

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0610,052

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,301
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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