Rutile TiO₂ Production: Optimization of Microwave Calcination of Metatitanic Acid Using Response Surface Methodology

Abstract

Rutile TiO2 was prepared by microwave calcination of metatitanic acid. A quadratic regression model for the conversion to rutile TiO2 was developed by response surface methodology (RSM), to investigate the effects of the calcination temperature, the calcination time, the sample mass, and their interactions on the conversion rates. Within the scope of the experiments, only the calcination temperature and time had significant effects on the rutile TiO2 conversion. Under the optimized conditions (866 °C, 61 min, 58 g), the predicted rutile TiO2 conversion was 99.91 %, approaching the actual conversion (99.44 %). Thus, the optimization by RSM of microwave calcination of metatitanic acid for the preparation of rutile TiO2 is a feasible approach to achieving a more efficient TiO2 production process.

Phase transformation analysis and process optimisation of low-grade lead–zinc oxysulphide ore carbothermal reduction

“…The higher the F value, the greater the impact on the response value. 32,38–40 For the reduction time A , reduction temperature B , and carbon mass ratio C , the interactions AB , AC , and BC P -values (prob > F -values) of the square terms A 2 and B 2 were less than 0.05. This indicates that the impacts of the three single factors ( A , B , and C ), interactions AB , AC , and BC , and square terms A 2 and B 2 on the volatilisation rate of zinc are significant.…”

“…This indicates that the quadratic regression model has high reliability and reflects the relationship between the experimental factors and zinc volatilisation rate well. Thus, the response surface method (BBD) scheme can accurately reflect the impact of experimental factors on the zinc volatilisation rate, and the selected model can effectively predict the actual experimental changes 32,40 .…”

“…Response surface methodology (RSM) is a statistical test method for optimizing random processes; it analyses the interaction between experimental factors and optimising process conditions by establishing the estimated functional relationship between experimental factors and response values. [30][31][32][33] The experimental conditions are optimized by the response surface method in different fields, and the carbothermic reduction in related fields is optimized by the response surface method. For example, the response surface method is used to prepare LiFePO4/C by carbothermic and carbon thermal reduction of low-grade phosphate rock.…”