硅锰合金埋弧熔炼过程中炉渣成分软测量

Soft sensor modeling for slag composition of silicon-manganese smelting process

针对硅锰合金埋弧熔炼过程中炉渣成分检测难的问题,提出一种基于自适应差分进化(ADE)优化的约减最小二乘支持向量机(RLSSVM)软测量模型。该模型以硅锰合金熔炼过程的工况参数为实测数据集,首先通过斯密特正交变换获取高维特征空间核矩阵的基,然后利用Direct Kernel PLS回归计算得到约减最小二乘支持向量机软测量模型,并以最小化训练样本的均方差为目标函数,用自适应差分进化算法优化最小二乘支持向量机的核参数和正则化参数,将此模型应用于30 MW硅锰合金埋弧冶炼过程炉渣成分测量。结果表明:ADE-RLSSVM模型测量值与实际值的最大相对误差为7.3%,运行时间为21 min。

To overcome the difficulty that the slag composition cannot be effectively measured in silicon-manganese smelting process,a soft sensor model based on reduced least squares support vector machine（RLSSVM） was proposed,which was optimized by adaptive differential evolution（ADE） algorithm.Firstly,based on the measured data,the base vectors of kernel matrix can be gotten by Schmidt orthogonalization in the high dimensional feature space.Then,the direct kernel partial least squares regression（PLS） calculation was conducted to obtain the RLSSVM soft sensor model.Taking the minimum standard deviation of the training sample as the objective function,the adaptive differential evolution algorithm was used to optimize the kernel function parameters and regularization parameter of LSSVM.At last,applying this method to estimate the slag composition in a 30 MW submerged arc furnace,the results show that the maximum relative error of ADE-RLSSVM model is 7.3% and the computation time is 21 min.