Multi-objective optimization for aluminum electrolysis production process
Multi-objective optimization for aluminum electrolysis production process
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作者：
Weng Xin-hai
针对铝电解生产过程中分子比、电解温度和极性距离难以根据不同条件及时调整，无法达到预期的一体化生产目标的问题，提出了以电流效率最大值和铝槽电压最小为优化目标，以氧化铝浓度和工艺要求为约束条件的基本优化思路。首先，采用多元线性回归和改进的BP神经网络方法，建立了铝电解生产过程非线性不等式约束下的多目标优化模型；然后，采用比例加权系数法和GRG（广义约简梯度）组合算法求解多目标优化问题。最后得到了分子比、电解温度和极性距离的优化目标值。结果表明，该模型在正常生产条件下的相对误差约为5%，在异常生产条件下的相对误差小于10%，表明该方法具有较高的精度。基于生产数据的优化结果的效率在90%以上，在正常生产中可达到97%，可以很好地满足企业的需求。
Based on the fact that in aluminum electrolysis production process,it's difficult to adjust molecular ratio,electrolytic temperature and polar distance timely according to different conditions,the anticipant integrated production target can't be reached,a basic optimization idea was proposed using the maximum of current efficiency and minimum of aluminum cell voltage as the optimization objective and using concentration of alumina and technological requirements as the constraint conditions.Firstly,the multiple linear regression and improved BP neural network methods were used to build a multi-objective optimization model with nonlinear inequality constraints for aluminum electrolysis production process.Then,the proportion weighted coefficient method and GRG(Generalized reduced gradient) combinatorial algorithm were applied to resolve the multi-objective optimization problem.Finally,the optimized target values of molecular ratio,electrolytic temperature and polar distance were obtained.The results show that the relative error of the proposed model is about 5% under normal producing condition and is less than 10% during abnormal production,which indicates the approach owns high precision.What's more,the efficiency of the optimization results based the producing data is higher than 90%,and can reach 97% during normal production so the approach can meet the enterprise needs well.