在烧结生产过程中，固体燃耗占据了生产能耗的70%左右，而与固体燃耗相关的工艺参数与固体燃耗之间呈现出非线性关系。为了实现优化生产和达到降低生产能耗的目的，本文采用改进后的BP神经网络挖掘两者之间存在的映射关系。本文提出了一种基于广义Curry原则非精确线搜索的共轭梯度算法，利用新的线搜索规则来确定算法的学习步长，在保证算法全局收敛的前提下，优化学习步长，提高了算法的收敛速度。利用改进的算法对烧结生产成本进行分析和预测，仿真结果说明改进算法具有很好的收敛性，预测的均方误差为0.009 8，准确率达到94.31%。

In the production process of sintering, the consumption of solid fuel is about 70% of the total energy consumption. There exists a nonlinear relationship between technological parameters and the solid fuel consumption in the sintering process. For the purpose of optimizing production and reducing energy consumption, this paper proposes an improved BP neural network to find the correlations between technological parameters and the solid fuel consumption. This paper proposes an improved conjugate gradient algorithm which combines the conjugate gradient algorithm with the inexact line search route based on the generalized Curry principle. The proposed algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed. The new algorithm is applied in the cost prediction of actual sintering production. Simulation results show that the algorithm has better convergence compared with the traditional conjugate gradient algorithms. The MSE of prediction is 0.0098 and the accuracy rate reaches 94.31%.