Factors Affecting Carbonation Depth in Foamed Concrete Bricks for Accelerate CO2 Sequestration

Sustainability

Husnul Azan Tajarudin¹  Abdullah Faisal Alshalif²  Saddam Abo Sabah²  J. M. Irwan²  A. A. Al-Gheethi³  Wahid Ali Hamood Altowayti³  N. Othman³  S. Shamsudin⁴ 

[1] Division of Bioprocess, School of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;Jamilus Research Centre for Sustainable Construction (JRC-SC), Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia;Micro-Pollutant Research Centre (MPRC), Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia;Sustainable Manufacturing and Recycling Technology, Advanced Manufacturing and Materials Center (SMART-AMMC), Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia;

关键词: carbonation depth;    chamber;    temperature;    statistical analysis;    2^k factorial;    RSM;   

DOI  :  10.3390/su131910999

来源: DOAJ

【 摘 要 】

Foamed concrete bricks (FCB) have high levels of porosity to sequestrate atmospheric CO₂ in the form of calcium carbonate CaCO₃ via acceleration of carbonation depth. The effect of density and curing conditions on CO₂ sequestration in FCB was investigated in this research to optimize carbonation depth. Statistical analysis using 2^k factorial and response surface methodology (RSM) comprising 11 runs and eight additional runs was used to optimize the carbonation depth of FCB for 28 days (d). The main factors selected for the carbonation studies include density, temperature and CO₂ concentration. The curing of the FCB was performed in the chamber. The results indicated that all factors significantly affected the carbonation depth of FCB. The optimum carbonation depth was 9.7 mm, which was determined at conditions; 1300 kg/m³, 40 °C, and 20% of CO₂ concentration after 28 d. Analysis of variance (ANOVA) and residual plots demonstrated the accuracy of the regression equation with a predicted R² of 89.43%, which confirms the reliability of the predicted model.

【 授权许可】

Unknown