This study evaluates and optimizes the comprehensive property of desulfurization gypsum-based composites(DGCs).The water-gypsum ratio(A),ratio of dihydrate to hemihydrate desulfurization gypsum(B),and dosage of silica...This study evaluates and optimizes the comprehensive property of desulfurization gypsum-based composites(DGCs).The water-gypsum ratio(A),ratio of dihydrate to hemihydrate desulfurization gypsum(B),and dosage of silica fume(C)were selected as multifactorial factors to design the three-level response surface methodology(RSM)experiments.Additionally,X-ray powder diffraction and scanning electron microscope(SEM)were used.The results indicate that the interactions of factor AC,BC and AB have the most significant effect towards the mechanical performances,thermal insulation as well as water resistance of DGCs,respectively.The water-gypsum ratio has the greatest influence on the overall performance of DGCs.In addition,the relative errors between the RSM test values and the model predictions do not exceed 5%,indicating that the RSM optimization models are highly accurate and well-fitted.展开更多
基金Funded by the National Natural Science Foundation of China(Nos.52168027 and 51968009)the Guizhou Provincal Science and Technology Project(Nos.[2020]1Y244 and[2022]027)。
文摘This study evaluates and optimizes the comprehensive property of desulfurization gypsum-based composites(DGCs).The water-gypsum ratio(A),ratio of dihydrate to hemihydrate desulfurization gypsum(B),and dosage of silica fume(C)were selected as multifactorial factors to design the three-level response surface methodology(RSM)experiments.Additionally,X-ray powder diffraction and scanning electron microscope(SEM)were used.The results indicate that the interactions of factor AC,BC and AB have the most significant effect towards the mechanical performances,thermal insulation as well as water resistance of DGCs,respectively.The water-gypsum ratio has the greatest influence on the overall performance of DGCs.In addition,the relative errors between the RSM test values and the model predictions do not exceed 5%,indicating that the RSM optimization models are highly accurate and well-fitted.
