Development of an intelligent sintering ore blending system for integration before ironmaking
In the context of the sintering blending process in steel production,challenges arise due to significant fluctuations in iron ore powder prices,the complexity of sintering raw material information,and the impact of various factors on sintering ore blending.Traditional genetic algorithm(GA)can easily fall into local optima.To address this issue,this study proposed a mathematical model based on an improved GA aimed at optimizing the sintering ore blending process to tackle the challenges posed by these influences on the cost of sintering materials.The model automatically adjusts the size of op-erators during the operational process based on the specific problem environment,effectively avoiding the premature convergence issue encountered by traditional GA.This ensures that the algorithm ulti-mately outputs a globally optimal solution when optimizing the sintering modeling.Starting with iron ore powder,the system utilizes technologies such as Python,MySQL and PyQt5 to construct an inte-grated sintering ore blending model.Through analysis and processing of backend data,the system ul-timately generates optimized sintering ore blending solutions.
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