Optimization of process parameters for the synthesis of class F fly ash-based geopolymer binders
Abstract
The Taguchi optimization approach was applied in this study to identify the optimal mix ratios for the production of geopolymer paste that uses fly ash as an aluminosilicate precursor. The method allows for probing the combined effect of selected process parameters on the output response with the least number of experiments possible, thereby reducing overall process time, cost, and effort. The compressive and splitting tensile strength values of the geopolymer paste specimens were explored in terms of alkali activator to binder ratio, NaOH concentration, Na2SiO3 to NaOH mass ratio, and standing time. A total of sixteen different proportions of mixtures were prepared, and the mechanical strengths of the specimens were evaluated using a universal testing machine. XRD and SEM analyses were used to examine the microstructures of the fly ash and the proposed optimized geopolymer paste. Results showed that a combination of alkali activator to binder ratio of 0.46, NaOH concentration of 14 M, Na2SiO3 to NaOH mass ratio of 1.5, and standing time of 72 h were found to be the best parameter settings yielding the highest strength of the paste specimens. The maximum compressive and splitting tensile strengths were 24.96 and 4.40 MPa, respectively, at the corresponding optimal values of the parameters. The apparent density of the specimens at these optimal conditions was determined to be 1835 kg/m3. From ANOVA results, it was observed that standing time and NaOH concentration had a significant influence on compressive and splitting tensile strength values, respectively. Based on the confirmation test, a variation of 4.1% and 2.2% in compressive and splitting tensile strengths, respectively, were observed as a result of extraneous variables.
- Publication:
-
Journal of Cleaner Production
- Pub Date:
- August 2023
- DOI:
- Bibcode:
- 2023JCPro.41537849A
- Keywords:
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- Compressive strength;
- Fly ash;
- Splitting tensile strength;
- Taguchi optimization approach