Abstract
The synthesis of vaterite was investigated from a statistical point of view to identify sets of optimal experimental conditions to obtain pure anhydrous calcium carbonate polymorph. Relevant research papers in the field of the precipitation of calcium carbonate were compiled in a secondary dataset
using a statistical mixed method described in another of our publications. This statistical mixed method consisted of three distinctive stages: a systematic literature review (Stage 1), followed by a meta-analysis of the acquired secondary data (Stage 2) and the validation in the laboratory (Stage 3).
In this work we present the results of Stages 2 and 3 of the mentioned method. A decision tree was built with the vaterite dataset and obtained good classification performance. A number of if-then decision rules were created covering the occurrence and absence of vaterite. The oven drying temperature, the pH and the concentration of the salt were used to control polymorphism. The best result corresponded to a vaterite polymorphic abundance of 93.6 ± 0.3%. It was possible to carry out a different investigation and arrive at new insights as a result of the unique size and characteristics of the mined data from Web of Science scientific articles
using a statistical mixed method described in another of our publications. This statistical mixed method consisted of three distinctive stages: a systematic literature review (Stage 1), followed by a meta-analysis of the acquired secondary data (Stage 2) and the validation in the laboratory (Stage 3).
In this work we present the results of Stages 2 and 3 of the mentioned method. A decision tree was built with the vaterite dataset and obtained good classification performance. A number of if-then decision rules were created covering the occurrence and absence of vaterite. The oven drying temperature, the pH and the concentration of the salt were used to control polymorphism. The best result corresponded to a vaterite polymorphic abundance of 93.6 ± 0.3%. It was possible to carry out a different investigation and arrive at new insights as a result of the unique size and characteristics of the mined data from Web of Science scientific articles
| Original language | English |
|---|---|
| Pages (from-to) | 668-680 |
| Number of pages | 13 |
| Journal | Chemical Engineering Research & Design |
| Volume | 188 |
| Early online date | 10 Oct 2022 |
| DOIs | |
| Publication status | Published - Dec 2022 |
Bibliographical note
AcknowledgementsThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Keywords
- Supervised Learning
- Decision tree
- Vaterite
- Meta-Analysis
- Reactive Crystallization