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烧结工序能耗预测与优化研究是确保生产有序合理、节能环保和低成本的重要手段。在烧结工序能耗定义分析及烧结工序能耗主要影响因素分析的基础上,建立了基于径向基神经网络-遗传算法(RBF—GA)的烧结能耗预测与优化模型。在神经网络模型对能耗高精度预报的基础上结合遗传算法求解优化模型,计算出最佳的输入参数组合。通过案例研究,验证了所提方法的正确性和有效性。
Research on energy consumption prediction and optimization of sintering process is an important means to ensure that production is orderly, energy saving and environmental protection and low cost. Based on the definition of energy consumption in sintering process and the main influence factors of energy consumption in sintering process, a prediction and optimization model of sintering energy consumption based on radial basis function neural network-genetic algorithm (RBF-GA) is established. Based on the neural network model of high-precision prediction of energy consumption combined with genetic algorithm to solve the optimization model, calculate the best combination of input parameters. Through the case study, the correctness and validity of the proposed method are verified.