Enhancing characterization of water use practices in cement manufacturing and related construction sectors
Notice bibliographique
Résumé
This work presents an investigation into water use patterns for cement manufacturing, ready-mixed concrete production, and buildings under construction and after occupation. Cement is the main component in making concrete, which is the most widely used structural building material in the world, and therefore plays an important role in global water use in the construction sector. The data collection methodology included review of refereed journals, analysis of published Corporate Social Responsibility Reports from worldwide cement companies, as well as case studies conducted in two cement plants (one in Brazil and one in Canada), thus incorporating real-world operating conditions. Analysis of water usage at ready-mixed concrete plants and buildings under construction and after occupation was also undertaken in Brazil. Water use at the two cement plants ranged from 250 to 2,000 litres per tonne of cement (compared to reported 147 to 3,500 L/tonne), indicating a wide range in water use patterns. Eleven stages of water use were identified for cement manufacturing, but accurate water use data could not be obtained for all these stages. Identifying and implementing water saving opportunities in cement manufacturing was hampered by a lack of reliable water use data. To address this, an approach was developed for categorizing levels of data reliability according to methods of data acquisition, and this approach was used to characterize the reliability of data compiled during this research. Reliability for the collected data in this study was then characterized to be between A+ to C-. The proposed data reliability approach can help improve data collection, reporting and decision-making around water conservation, both locally within manufacturing facilities and on jobsites, and at the level of governmental policy. This work therefore contributes to the field of water management by (a) shedding light on the lack of water usage data availability and reliability in the globally important sectors of cement manufacturing, concrete production, and buildings under construction and after occupation; (b) proposing an approach for improving the reliability of water usage data; (c) suggesting steps to improve knowledge of water usage in various sectors of construction industry; and (d) promoting best water management practices in this field.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».