中南大学学报（自然科学版）2018，Vol.49Issue(1)：15-21,7.DOI:10.11817/j.issn.1672-7207.2018.01.003
基于CF-EEMD-LSSVR算法的铅冶炼系统 温室气体排放的评估与预测
Evaluation and prediction of greenhouse gas emission of lead smelting system based on CF-EEMD-LSSVR
摘要
Abstract
Input-output (I-O) model for each step of lead smelting system to evaluate greenhouse gas emission per unit product was established based on carbon footprint (CF) theory. Due to the nonlinear characteristic of greenhouse gas emission data, a prediction model was developed based on the combination of ensemble empirical mode decomposition (EEMD) and the least square support vector regression (LSSVR).The procedures were as follows: the data of greenhouse gas emission of leads melting system was firstly decomposed into a series of relatively stable intrinsic mode functions (IMF), and then they were separately predicted by LSSVR. The predicted values were compared with the real results. The results show that the root mean square error of the predicted values and the real results is 2.8961％, which verifies that the proposed method can realize the accurate evaluation and prediction of greenhouse gas emission of lead smelting system.关键词
铅冶炼系统/温室气体排放/碳足迹/集合经验模态分解/最小二乘支持向量回归机Key words
lead smelting system/greenhouse gas emission/carbon footprint(CF)/ensemble empirical mode decomposition(EEMD)/the least square support vector regression(LSSVR)分类
矿业与冶金引用本文复制引用
罗曦,王洪才,李玉强..基于CF-EEMD-LSSVR算法的铅冶炼系统 温室气体排放的评估与预测[J].中南大学学报（自然科学版）,2018,49(1):15-21,7.基金项目
国家自然科学基金资助项目(51478470) (Project(51478470) supported by the National Natural Science Foundation of China) （51478470）
