LAPSE:2024.0953
Published Article
LAPSE:2024.0953
Optimization of Ternary Activator for Enhancing Mechanical Properties of Carbonized Cementitious Material Based on Circulating Fluidized Bed Fly Ash
June 7, 2024
Abstract
In this study, circulating fluidized bed fly ash (CFBFA) non-sintered ceramsite was innovatively developed. The CFBFA was addressed by adding ternary activator (including cement, hydrated lime, and gypsum) to prepare ceramsite. In the curing process, the use of power plant flue gas for curing not only captured greenhouse gas CO2, but also enhanced the compressive strength of the ceramsite. The compressive strength of the composite gravels prepared by the CFBFA was modeled using a novel approach that employed the response surface methodology (RSM) and artificial neural network (ANN) coupled with genetic algorithm (GA). Box−Behnken design (BBD)-RSM method was used for the independent variables of cement content, hydrated lime content, and gypsum content. The resulting quadratic polynomial model had an R2 value of 0.9820 and RMSE of 0.21. The BP-ANN with a structure of 3-10-1 performed the best and showed better prediction of the response than the BBD-RSM model, with an R2 value of 0.9932 and RMSE of 0.19. The process parameters were optimized using RSM optimization tools and GA. Validation experiments showed that the GA-ANN prediction results were more accurate than the BBD-RSM results.
In this study, circulating fluidized bed fly ash (CFBFA) non-sintered ceramsite was innovatively developed. The CFBFA was addressed by adding ternary activator (including cement, hydrated lime, and gypsum) to prepare ceramsite. In the curing process, the use of power plant flue gas for curing not only captured greenhouse gas CO2, but also enhanced the compressive strength of the ceramsite. The compressive strength of the composite gravels prepared by the CFBFA was modeled using a novel approach that employed the response surface methodology (RSM) and artificial neural network (ANN) coupled with genetic algorithm (GA). Box−Behnken design (BBD)-RSM method was used for the independent variables of cement content, hydrated lime content, and gypsum content. The resulting quadratic polynomial model had an R2 value of 0.9820 and RMSE of 0.21. The BP-ANN with a structure of 3-10-1 performed the best and showed better prediction of the response than the BBD-RSM model, with an R2 value of 0.9932 and RMSE of 0.19. The process parameters were optimized using RSM optimization tools and GA. Validation experiments showed that the GA-ANN prediction results were more accurate than the BBD-RSM results.
Record ID
Keywords
artificial neural network, Box–Behnken design, Genetic Algorithm, response surface methodology, ternary activator
Subject
Suggested Citation
Xu N, Ma S, Wang N, Feng Y, Liu Y, Ren K, Bai S. Optimization of Ternary Activator for Enhancing Mechanical Properties of Carbonized Cementitious Material Based on Circulating Fluidized Bed Fly Ash. (2024). LAPSE:2024.0953
Author Affiliations
Xu N: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Ma S: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Wang N: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Feng Y: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Liu Y: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Ren K: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Bai S: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Ma S: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Wang N: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Feng Y: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Liu Y: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Ren K: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Bai S: Shanxi Province Key Laboratory of Clean & High Efficient Combustion and Utilization of Circulating Fluidized Bed, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan 030024, China
Journal Name
Processes
Volume
12
Issue
2
First Page
289
Year
2024
Publication Date
2024-01-29
ISSN
2227-9717
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Original Submission
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PII: pr12020289, Publication Type: Journal Article
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LAPSE:2024.0953
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https://doi.org/10.3390/pr12020289
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[v1] (Original Submission)
Jun 7, 2024
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