An Optimization Method Based on Integrated Predictive Models and Expert Reasoning Strategies for Mix Proportions in Lead-zinc Sinter

To deal with the problem of high cost and low accuracy existed in traditional methods of lead-zinc sinter mix proportions, a methodology based on integrated prediction models of agglomerate composition and expert reasoning strategies is proposed in this paper. First, based on the expert experience mechanism model and neural network model, an intelligent integrated model is presented to assure the composition prediction precision of Pb-Zn agglomerate and to meet the requirements of the data completeness by blending computation. Then, the sinter proportion optimization model is established with the objective of minimizing the costs. Finally, the proportions are optimized through expert reasoning optimization strategies and an integrated synthesis methodology. The simulation results demonstrate the validity of this methodology.