Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology

The use of agro-waste in geopolymers, to serve as alternative material to Ordinary Portland Cement (OPC) presents a great potential to the sustainability of the construction sector. Accordingly, this study aimed to optimize the production parameters of geopolymer cement using response surface method...

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Main Authors: Kasumba A. Buyondo, Peter W. Olupot, John B. Kirabira, Abdulfatah A. Yusuf
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Case Studies in Construction Materials
Subjects:
RHA
RSM
Online Access:http://www.sciencedirect.com/science/article/pii/S2214509520301339
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spelling doaj-cb2a64564fd64c06bcb487efa82cb5732020-12-25T05:09:46ZengElsevierCase Studies in Construction Materials2214-50952020-12-0113e00461Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodologyKasumba A. Buyondo0Peter W. Olupot1John B. Kirabira2Abdulfatah A. Yusuf3Department of Mechanical Engineering, Makerere University Kampala, P. O. Box 7062, Kampala Uganda; Department of Mechanical Engineering, Kampala International University, P. O. Box 20000, Kampala, Uganda; Corresponding author at: Department of Mechanical Engineering, Makerere University Kampala, P. O. Box 7062, Kampala Uganda.Department of Mechanical Engineering, Makerere University Kampala, P. O. Box 7062, Kampala UgandaDepartment of Mechanical Engineering, Makerere University Kampala, P. O. Box 7062, Kampala UgandaDepartment of Mechanical Engineering, Makerere University Kampala, P. O. Box 7062, Kampala UgandaThe use of agro-waste in geopolymers, to serve as alternative material to Ordinary Portland Cement (OPC) presents a great potential to the sustainability of the construction sector. Accordingly, this study aimed to optimize the production parameters of geopolymer cement using response surface methodology (RSM) with rice husk ask (RHA), metakaolin (MK) and an alkaline activator as reactants. During the experiment, two-factorial design was run using the central composite design (CCD) to generate 13 combinations of cement (reactant) mixtures with standard sand in order to cast mortar prisms whose compressive strength was evaluated according to BS EN standard. Data analysis on the compressive strength was carried out using MATLAB software. Models for compressive strengths with RHA and MK as predictors were generated for samples cured at 7 and 14 days. Result showed that the overall R2 of these models were 0.8951 and 0.63, respectively. The β values for the multiple linear model for the strength at 7 days of curing was <0.05, which makes the model a reliable predictor for the geopolymer strength. The performance tests carried out on the optimal cement sample showed the consistency of 52.9 %, initial and final setting time of 7 and 13.5 h respectively. The optimal geopolymer cement also had a bulk density of 1546.4 kg/m3, and water absorption of 19.19 % with the optimum compressive strength of 0.80 MPa at 7 days.http://www.sciencedirect.com/science/article/pii/S2214509520301339RHAMetakaolinRSMStrengthOptimizationGeopolymers
collection DOAJ
language English
format Article
sources DOAJ
author Kasumba A. Buyondo
Peter W. Olupot
John B. Kirabira
Abdulfatah A. Yusuf
spellingShingle Kasumba A. Buyondo
Peter W. Olupot
John B. Kirabira
Abdulfatah A. Yusuf
Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
Case Studies in Construction Materials
RHA
Metakaolin
RSM
Strength
Optimization
Geopolymers
author_facet Kasumba A. Buyondo
Peter W. Olupot
John B. Kirabira
Abdulfatah A. Yusuf
author_sort Kasumba A. Buyondo
title Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
title_short Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
title_full Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
title_fullStr Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
title_full_unstemmed Optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
title_sort optimization of production parameters for rice husk ash-based geopolymer cement using response surface methodology
publisher Elsevier
series Case Studies in Construction Materials
issn 2214-5095
publishDate 2020-12-01
description The use of agro-waste in geopolymers, to serve as alternative material to Ordinary Portland Cement (OPC) presents a great potential to the sustainability of the construction sector. Accordingly, this study aimed to optimize the production parameters of geopolymer cement using response surface methodology (RSM) with rice husk ask (RHA), metakaolin (MK) and an alkaline activator as reactants. During the experiment, two-factorial design was run using the central composite design (CCD) to generate 13 combinations of cement (reactant) mixtures with standard sand in order to cast mortar prisms whose compressive strength was evaluated according to BS EN standard. Data analysis on the compressive strength was carried out using MATLAB software. Models for compressive strengths with RHA and MK as predictors were generated for samples cured at 7 and 14 days. Result showed that the overall R2 of these models were 0.8951 and 0.63, respectively. The β values for the multiple linear model for the strength at 7 days of curing was <0.05, which makes the model a reliable predictor for the geopolymer strength. The performance tests carried out on the optimal cement sample showed the consistency of 52.9 %, initial and final setting time of 7 and 13.5 h respectively. The optimal geopolymer cement also had a bulk density of 1546.4 kg/m3, and water absorption of 19.19 % with the optimum compressive strength of 0.80 MPa at 7 days.
topic RHA
Metakaolin
RSM
Strength
Optimization
Geopolymers
url http://www.sciencedirect.com/science/article/pii/S2214509520301339
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