摘要
针对氧化铝悬浮焙烧能耗信息表征和模型应用的实际需求,建立一种最小二乘支持向量机(LS-SVM)能耗估计模型。基于该类模型结合遗传算法(GA)提出一种模型参数优化和工业应用策略。采用灰关联分析确定模型的主输入为主炉温度、烟气含氧量、原料含水量;采用K折交叉验证优化样本数据;采用比较模型预测误差确定核函数为径向基函数(RBF)核。建立输入为能耗参数,输出为模型标志的支持向量机工况模型选择器。能耗估计模型的自学习与动态优化通过样本的更新和聚类实现,模型的选择和投运通过模型选择器依据工况状态实施切换。实验结果表明,建立的焙烧能耗估计模型和模型应用策略,能提高模型的泛化能力、增强模型的工况适应性,是一种有效的焙烧能耗参数估计和分析方法。
According to the requirement of energy consumption information representation and model application in aluminum hydroxide gas suspension calcinations process,a kind of energy consumption estimation model was established based on Least Squares Support Vector Machine （LS-SVM） method.By combining the energy consumption model with Genetic Algorithm （GA）,a kind of parameters optimization and industry application strategy was presented.Input parameters of energy estimation model were analyzed through grey relational analysis method,and the main factors of input parameters consisted of main furnace temperature,oxygen content of exhaust gas and containing water of aluminum hydroxide.The sampled data of energy consumption parameters were regrouped and optimized through K-fold cross-validation method.By comparing prediction accuracy of energy consumption models with various kernel functions,Radial Basis Function （RBF） kernel function was adopted to express feature information of sampling data.A model switcher whose inputs were energy parameters and output was symbol parameter of energy estimation model was constructed by Support Vector Machines （SVM） method.Self-learning and dynamic optimization processes of energy estimation model were realized by sample data updating and clustering.Model selection and application were realized by using the model switcher according to various calcinations conditions.The experimental results show that the LS-SVM modeling and application strategy can improve the generalization capability and conditions adaptability of energy estimation model.The presented strategy of model application is a feasible method for energy parameter analysis and estimation.
出处
《计算机应用》
CSCD
北大核心
2014年第4期1217-1221,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61304019)
湖南省教育厅资助项目(11C0025)
关键词
氧化铝
气态悬浮炉焙烧
能耗模型
最小二乘支持向量机
遗传算法
aluminum hydroxide
gas suspension calcinations
energy consumption model
Least Squares Support Vector Machine （LS-SVM）
Genetic Algorithm （GA）
