硅锰铁合金埋弧炉供电系统建模分析与工艺指标预测
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摘要
硅锰铁合金埋弧炉将高压电能通过供电系统转换为熔池电弧热能和炉料电阻热能,以满足铁合金熔炼需要的热量。供电系统由电炉变压器、短网、三相电极、熔池电弧和炉料电阻等组成。由于电弧与炉料电阻呈现非线性和时变性,三相电极位置调整、电极消耗和负载耦合引起电弧和炉料电阻变化,使供电系统模型具有非线性、时变性、强耦合和不确定性的特点,导致供电系统精确建模非常困难。
论文首先建立了硅锰铁合金埋弧炉供电系统主回路计算模型,分析了不同电极控制策略下电炉的不对称负载特性和三相熔池有功功率不平衡的原因,提出了基于电源中心点和熔池中心点的三相电极有功功率在线估计算法。然后,通过对铁合金埋弧炉熔炼工艺机理的分析,研究了基于最小二乘支持向量机的工艺指标预测方法,并以12500KVA硅锰铁合金埋弧炉为对象进行了实验验证。论文主要研究工作及创新性成果如下：
(1)针对埋弧炉三相负载不对称、采用恒电流控制策略无法保证三相电极有功功率平衡的问题,提出了基于电极电流比的三相电极有功功率平衡控制策略。首先通过对硅锰铁合金埋弧炉供电系统主回路参数计算,建立了硅锰铁合金埋弧炉供电系统等效电路模型,采用牛顿法求解非线性方程组得到了埋弧炉等效电路参数,通过调节电极电流比实现了三相电极有功功率平衡控制。实验结果表明,该方法保证三相电极功率平衡精度达到±3%以内。
(2)针对埋弧炉三相电极有功功率不可测、难以实现三相熔池有功功率平衡的问题,提出了基于电源中心点和熔池中心点的三相电极功率在线估计方法。通过人造电源中心点,实现了电炉变压器高压侧各相电压、电流和相有功功率的在线测量。通过人造熔池中心点,实现了变压器低压侧三相熔池电压的在线测量,并基于能量守恒原理建立了硅锰铁合金埋弧炉电极有功功率机理模型。考虑到建模过程中各种假设引起的估计误差较大,采用改进BP神经网络对机理模型的误差进行在线修正。仿真结果表明,基于改进BP神经网络修正的电极功率机理模型相对误差为±5.71%,具有较高的精度。
(3)针对硅锰铁合金埋弧炉熔炼环境恶劣、合金成分难以实时检测的问题,提出了基于自适应递推最小二乘支持向量机的硅锰合金成分预测方法,建立了硅锰合金成分预测模型,采用增长记忆递推算法、限定记忆递推算法和缩减记忆递推算法对模型进行在线训练,以最小化LSSVM目标损失函数为准则自适应调整预测模型的输出,动态调整模型的结构和参数,提高了模型的训练速度和预测精度。仿真结果表明该预测方法是有效的。
(4)针对硅锰铁合金埋弧炉熔炼过程中炉渣成分检测滞后大、难以根据冶炼工况优化操作参数的问题,提出了基于最小二乘支持向量机的炉渣成分预测方法。首先基于物料平衡原理建立了理想工况和特定化学反应条件下硅锰铁合金炉渣成分机理模型,反映了炉渣成分的变化规律。由于机理建模是对熔炼工况进行了一定简化和假设,模型预测精度不能完全满足工业现场对炉渣成分的检测要求。然后,基于数据驱动的思想建立了约简最小二乘支持向量机炉渣成分预测模型和核主元分析与最小二乘支持向量机相结合的炉渣碱度预测模型。约简最小二乘支持向量机合金成分预测模型通过施密特正交变换获得高维特征空间核矩阵的基,用自适应差分进化算法优化最小二乘支持向量机核函数参数,提高模型的鲁棒性和预测精度。核主元分析与最小二乘支持向量机相结合的炉渣碱度预测模型利用核主元分析对输入样本数据进行降噪处理和特征提取,提高模型的抗噪性能和训练速度。仿真结果表明,模型的预测精度可以满足生产现场对炉渣成分检测的要求。
The submerged arc furnace (SAF) is used to convert the high-voltage electric energy to arc heat and charge resistance heat to meet the heat needs of silicon-manganese ferroalloy smelting. The power supply system model consists of a transformer, short network, three-phase electrode, electric-arc, charge resistors and so on. Because of the position adjustment and the corrosion of three-phase electrode, as well as the load coupling, the power supply system model has the characteristics of nonlinearity, timevayring and uncertainty, which make it difficult to establish its precise mathematical model. Moreover, the silicon-manganese smelting process is a process with physical and chemical reactions of hign temperature and multi-composition, which has such characteristics as complex mechanism, multivariable and large delay. The process indexes are extremely difficult to be measured and the operating parameters can't be optimized, which leads to fluctuant product quality, poor productivity and high power consumption.
In this paper, a calculation model for the power supply system of the SAF was developed and the properties of three-phased asymmetric loads and the reasons of unbalanced three phase electrode effective power for different electrode control strategies were analyzed. An on-line calculation method of three phase electrode effective power was proposed by designing an electrical zero in the primary circuit and a neutral point in the bottom of the bath. Then, on the basis of the process mechanism analysis of the SAF, the process indexes prediction models based on least squares support vector machine were proposed. The proposed methods were validated by using the production data of a12500KVA submerged arc silicomanganese furnace. Main research work and innovation achievements are as follows:
(1) Aiming at the unbalanced effective power of three-phase electrode caused by three-phased asymmetric loads of the SAF under constant current control strategy, an effective power balanced control strategy based on electrode current ratio was proposed. The equivalent circuit equations for the the power supply system were first established. Then, the equivalent electrical parameters were calculated by solving the nonlinear circuit equations using Newton method. At last, a three phase electrode effective power balanced control strategy based on electrode current ratio was obtained. The tests results show that the unbalanced degree of three-phase electrode effective power was restrained within±3%by using the proposed method.
(2) Considering the difficulties in the measurement of three-phase electrode effective power and the problems in balance of three phase electrode effective power, an on-line calculation method of three phase electrode effective power was proposed. An electrical zero in the primary circuit and a neutral point in the bath bottom were designed, where the phase voltage, phase current, phase active power in the primary side of transformer as well as the electrode voltage in the secondary side can be measured. Then a mechanism model based on Energy Conservation Method of three-phase electrode effective power was proposed and an improved BP neural network was presented to compensate the estimated error. The simulation results show that the proposed method has good performance and its relative error is only±5.71%.
(3) Aiming at the problem that the silicon-manganese alloy composition is difficult to be measured, an on-line prediction model for silicon-manganese alloy composition based on adaptive recursive least squares support vector machine was proposed. Three recursive including methods increased memory algorithm, fixed memory algorithm and decreased memory algorithm, are employed to train the prediction model. To improve the solution speed and prediction precision of the proposed model, an error minimizing function was set to adjust the structure and parameters of the prediction model adaptively. The simulation results show its effectiveness.
(4) Aimed at the large-lag of manual examination in the submerged arc smelting process, an on-line prediction method for silicon-manganese slag composition based on simplified least squares support vector machine was proposed. Firstly, a slag composition mechanism model and a slag basity mechanism model based on mass balance were constructed from special chemical reactions under ideal production state, which reflects the change tendency of slag composition. However, for the simplification of the process reactions and condition hypothesis in the process modeling, the accuracy of the mechanism model could not completely meet the technical requirement of industrial production process. Then, a simplified least square support vector machines (SLS-SVM) based slag composition prediction model and a least square support vector machines with kernel principal component analysis (KPCA-LSSVM) based slag basity prediction model are implented. In the SLS-SVM prediction model, the sample data are mapped to the high dimensional feature space using Schmitt orthogonalized to obtain the kernel matrix of the space. Then, the regression parameters were identified by Direct Kernel PLS, and the kernel function parameters in the SLS-SVM were optimized by adaptive differential evolution algorithm. In the KPCA-LSSVM prediction model, the kernel principal component analysis is used to denoise the input data and capture the high-ordered nonlinear principal components among the input data space to improve the model's performace. The experiment results show that the accuracy of the proposed method can meet the requirement of slag composition determination in the industrial process.
论文首先建立了硅锰铁合金埋弧炉供电系统主回路计算模型,分析了不同电极控制策略下电炉的不对称负载特性和三相熔池有功功率不平衡的原因,提出了基于电源中心点和熔池中心点的三相电极有功功率在线估计算法。然后,通过对铁合金埋弧炉熔炼工艺机理的分析,研究了基于最小二乘支持向量机的工艺指标预测方法,并以12500KVA硅锰铁合金埋弧炉为对象进行了实验验证。论文主要研究工作及创新性成果如下：
(1)针对埋弧炉三相负载不对称、采用恒电流控制策略无法保证三相电极有功功率平衡的问题,提出了基于电极电流比的三相电极有功功率平衡控制策略。首先通过对硅锰铁合金埋弧炉供电系统主回路参数计算,建立了硅锰铁合金埋弧炉供电系统等效电路模型,采用牛顿法求解非线性方程组得到了埋弧炉等效电路参数,通过调节电极电流比实现了三相电极有功功率平衡控制。实验结果表明,该方法保证三相电极功率平衡精度达到±3%以内。
(2)针对埋弧炉三相电极有功功率不可测、难以实现三相熔池有功功率平衡的问题,提出了基于电源中心点和熔池中心点的三相电极功率在线估计方法。通过人造电源中心点,实现了电炉变压器高压侧各相电压、电流和相有功功率的在线测量。通过人造熔池中心点,实现了变压器低压侧三相熔池电压的在线测量,并基于能量守恒原理建立了硅锰铁合金埋弧炉电极有功功率机理模型。考虑到建模过程中各种假设引起的估计误差较大,采用改进BP神经网络对机理模型的误差进行在线修正。仿真结果表明,基于改进BP神经网络修正的电极功率机理模型相对误差为±5.71%,具有较高的精度。
(3)针对硅锰铁合金埋弧炉熔炼环境恶劣、合金成分难以实时检测的问题,提出了基于自适应递推最小二乘支持向量机的硅锰合金成分预测方法,建立了硅锰合金成分预测模型,采用增长记忆递推算法、限定记忆递推算法和缩减记忆递推算法对模型进行在线训练,以最小化LSSVM目标损失函数为准则自适应调整预测模型的输出,动态调整模型的结构和参数,提高了模型的训练速度和预测精度。仿真结果表明该预测方法是有效的。
(4)针对硅锰铁合金埋弧炉熔炼过程中炉渣成分检测滞后大、难以根据冶炼工况优化操作参数的问题,提出了基于最小二乘支持向量机的炉渣成分预测方法。首先基于物料平衡原理建立了理想工况和特定化学反应条件下硅锰铁合金炉渣成分机理模型,反映了炉渣成分的变化规律。由于机理建模是对熔炼工况进行了一定简化和假设,模型预测精度不能完全满足工业现场对炉渣成分的检测要求。然后,基于数据驱动的思想建立了约简最小二乘支持向量机炉渣成分预测模型和核主元分析与最小二乘支持向量机相结合的炉渣碱度预测模型。约简最小二乘支持向量机合金成分预测模型通过施密特正交变换获得高维特征空间核矩阵的基,用自适应差分进化算法优化最小二乘支持向量机核函数参数,提高模型的鲁棒性和预测精度。核主元分析与最小二乘支持向量机相结合的炉渣碱度预测模型利用核主元分析对输入样本数据进行降噪处理和特征提取,提高模型的抗噪性能和训练速度。仿真结果表明,模型的预测精度可以满足生产现场对炉渣成分检测的要求。
The submerged arc furnace (SAF) is used to convert the high-voltage electric energy to arc heat and charge resistance heat to meet the heat needs of silicon-manganese ferroalloy smelting. The power supply system model consists of a transformer, short network, three-phase electrode, electric-arc, charge resistors and so on. Because of the position adjustment and the corrosion of three-phase electrode, as well as the load coupling, the power supply system model has the characteristics of nonlinearity, timevayring and uncertainty, which make it difficult to establish its precise mathematical model. Moreover, the silicon-manganese smelting process is a process with physical and chemical reactions of hign temperature and multi-composition, which has such characteristics as complex mechanism, multivariable and large delay. The process indexes are extremely difficult to be measured and the operating parameters can't be optimized, which leads to fluctuant product quality, poor productivity and high power consumption.
In this paper, a calculation model for the power supply system of the SAF was developed and the properties of three-phased asymmetric loads and the reasons of unbalanced three phase electrode effective power for different electrode control strategies were analyzed. An on-line calculation method of three phase electrode effective power was proposed by designing an electrical zero in the primary circuit and a neutral point in the bottom of the bath. Then, on the basis of the process mechanism analysis of the SAF, the process indexes prediction models based on least squares support vector machine were proposed. The proposed methods were validated by using the production data of a12500KVA submerged arc silicomanganese furnace. Main research work and innovation achievements are as follows:
(1) Aiming at the unbalanced effective power of three-phase electrode caused by three-phased asymmetric loads of the SAF under constant current control strategy, an effective power balanced control strategy based on electrode current ratio was proposed. The equivalent circuit equations for the the power supply system were first established. Then, the equivalent electrical parameters were calculated by solving the nonlinear circuit equations using Newton method. At last, a three phase electrode effective power balanced control strategy based on electrode current ratio was obtained. The tests results show that the unbalanced degree of three-phase electrode effective power was restrained within±3%by using the proposed method.
(2) Considering the difficulties in the measurement of three-phase electrode effective power and the problems in balance of three phase electrode effective power, an on-line calculation method of three phase electrode effective power was proposed. An electrical zero in the primary circuit and a neutral point in the bath bottom were designed, where the phase voltage, phase current, phase active power in the primary side of transformer as well as the electrode voltage in the secondary side can be measured. Then a mechanism model based on Energy Conservation Method of three-phase electrode effective power was proposed and an improved BP neural network was presented to compensate the estimated error. The simulation results show that the proposed method has good performance and its relative error is only±5.71%.
(3) Aiming at the problem that the silicon-manganese alloy composition is difficult to be measured, an on-line prediction model for silicon-manganese alloy composition based on adaptive recursive least squares support vector machine was proposed. Three recursive including methods increased memory algorithm, fixed memory algorithm and decreased memory algorithm, are employed to train the prediction model. To improve the solution speed and prediction precision of the proposed model, an error minimizing function was set to adjust the structure and parameters of the prediction model adaptively. The simulation results show its effectiveness.
(4) Aimed at the large-lag of manual examination in the submerged arc smelting process, an on-line prediction method for silicon-manganese slag composition based on simplified least squares support vector machine was proposed. Firstly, a slag composition mechanism model and a slag basity mechanism model based on mass balance were constructed from special chemical reactions under ideal production state, which reflects the change tendency of slag composition. However, for the simplification of the process reactions and condition hypothesis in the process modeling, the accuracy of the mechanism model could not completely meet the technical requirement of industrial production process. Then, a simplified least square support vector machines (SLS-SVM) based slag composition prediction model and a least square support vector machines with kernel principal component analysis (KPCA-LSSVM) based slag basity prediction model are implented. In the SLS-SVM prediction model, the sample data are mapped to the high dimensional feature space using Schmitt orthogonalized to obtain the kernel matrix of the space. Then, the regression parameters were identified by Direct Kernel PLS, and the kernel function parameters in the SLS-SVM were optimized by adaptive differential evolution algorithm. In the KPCA-LSSVM prediction model, the kernel principal component analysis is used to denoise the input data and capture the high-ordered nonlinear principal components among the input data space to improve the model's performace. The experiment results show that the accuracy of the proposed method can meet the requirement of slag composition determination in the industrial process.
引文
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