Effects of minerals in ferric bauxite on sodium carbonate decomposition and volatilization

Direct reduction is an emerging technology for ferric bauxite utilization. However, because of sodium volatilization, its sodium carbonate consumption is considerably higher than that in ordinary bauxite processing technology. TG-DSC and XRD were applied to detecting phase transformation and mass loss in direct reduction to reveal the mechanism on sodium volatilization. The results show that the most significant influence factor of ferric bauxite on sodium volatilization in direct reduction system is its iron content. Sodium volatilization is probably ascribed to the instability of amorphous substances structure. Amorphous substances are the intermediate-products of the reaction, and the volatilization rate of sodium increases with its generating rate. These amorphous substances are volatile, thus, more sodium is volatilized with its generation. A small amount of amorphous substances are generated in the reaction between Na2CO3 and Al2O3; thus, only 3.15% of sodium is volatilized. Similarly, the volatilization rate is 1.87% in the reaction between Na2CO3 and SiO2. However, the volatilization rate reaches 7.64% in the reaction between Na2CO3 and Fe2O3 because of the generation of a large amount of amorphous substances.

Efficient Inverse Method for Structural Identification Considering Modeling and Response Uncertainties

The inverse problem analysis method provides an effective way for the structural parameter identification.However,uncertainties wildly exist in the practical engineering inverse problems.Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters,the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost.In this paper,an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters.Firstly,the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters.Afterwards,a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty.The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating.In order to solve the interval inverse problems considering response uncertainty,an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed.Through the coupling of the above two strategies,the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure,and then effectively realizes the uncertainty identification of unknown structural parameters.Finally,two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.