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Optimization for fire performance of ultra-low density fiberboards using response surface methodology
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
Department of Material Science and Engineering, Fujian Agriculture and Forestry University, Fuzhou.
Department of Material Science and Engineering, Fujian Agriculture and Forestry University, Fuzhou.
Department of Material Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian.
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2017 (English)In: BioResources, ISSN 1930-2126, E-ISSN 1930-2126, Vol. 12, no 2, 3790-3800 p.Article in journal (Refereed) Published
Abstract [en]

The optimization of the process conditions for fire retardant ultra-low density fiberboards (ULDFs) was investigated using response surface methodology (RSM). Three parameters, namely those of Borax-Zinc-Silicate-Aluminum (B-Zn-Si-Al), chlorinated paraffin (CP), and chloride-vinyl chloride emulsions (PVDC) were chosen as variables. The considerably high R2 value (99.98%) indicated the statistical significance of the model. The optimal process conditions for the limiting oxygen index (LOI) were determined by analyzing the response surface's three-dimensional surface plot and contour plot, and by solving the regression model equation with Design Expert software. The Box-Behnken design (BBD) was used to optimize the process conditions, which showed that the most favorable dosages of B-Zn-Si-Al, CP, and PVDC were 800 mL, 46.47 mL, and 35.64 g, respectively. Under the optimized conditions, the maximum LOI was 48.4.

Place, publisher, year, edition, pages
North Carolina State University , 2017. Vol. 12, no 2, 3790-3800 p.
National Category
Other Mechanical Engineering
Research subject
Wood Science and Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-63593DOI: 10.15376/biores.12.2.3790-3800ISI: 000402883700114Scopus ID: 2-s2.0-85018937821OAI: oai:DiVA.org:ltu-63593DiVA: diva2:1102656
Note

Validerad;2017;Nivå 2;2017-05-30 (andbra)

Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2017-11-24Bibliographically approved

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Wu, ZhenzengChen, TingjieWang, Alice

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