Intelligent optimization of raw material blending for alumina production with information uncertainty
一种解决蕴含不确定性信息的氧化铝配料问题的智能优化方法

Considering the uncertainty of raw material quality and time-lagging in composition measurement, a twostage intelligent optimization method is proposed to realize the optimal control of slurry quality for the raw material blending process in alumina production. By introducing an intermediate optimization objective, the blending optimization problem is decomposed into two stages, i.e. the optimization of the mixture ratio and the optimization of slurry combination, to reduce the effect of uncertainty step by step. In the mixture-ratio optimization, an expert hierarchical reasoning strategy based on the quality prediction model is proposed to optimize the mixture ratio with multi-index constraints. Then, an optimal combination model with uncertainty is built by incorporating the uncertainty of raw slurry quality into constraints, and an improved genetic algorithm is used to solve it. The proposed approach has been applied to the blending process of an alumina factory of China, and the optimal control of slurry quality is realized and the blending process is simplified.