基于互信息案例推理的氧气脱碳效率预测模型

Prediction Model of Oxygen Decarburization Effciency Based on Mutual Information Case-Based Reasoning

提出基于互信息案例推理的氧气脱碳效率预测模型,并依据预测结果计算转炉炼钢静态和动态阶段吹氧量.首先提出一种新的吹氧量预测方法,将氧气脱碳效率作为案例推理的解属性;然后将互信息引入属性权重的确定过程中,解决了传统案例检索方法忽略问题属性与解属性之间信息量的不足.将所提方法用于一座150t转炉的实际生产数据中,仿真结果表明该模型预测精度较高.该方法能够实现对转炉炼钢吹氧量的准确计算,满足实际生产的要求.

A prediction model of oxygen decarburization effciency is proposed based on mutual information case-based reasoning,and the oxygen blowing amounts of static and dynamic phases are calculated according to the forecasting results.A new prediction method of oxygen blowing amount is proposed firstly,which attributes the oxygen decarburization effciency as solution properties of case-based reasoning.Then the mutual information is introduced into the process of determining weights of properties,which solves the problem that the lack of information is ignored between the problem properties and the solution properties in the traditional case retrieval method.The proposed model is applied to the actual production data of a 150 t converter,and the simulation results show that the model has high prediction accuracy.The method can achieve precise calculation of blowing oxygen volume and satisfy the requirement of production.