大数据 ›› 2024, Vol. 10 ›› Issue (4): 66-76.doi: 10.11959/j.issn.2096-0271.2024051

• 专题：大数据与云存储 • 上一篇    

基于长短期记忆网络的炼钢厂碳排放量预测方法

李凤云1, 窦泽慧1, 李朋2, 郭威1   

  1. 1 东北大学计算机科学与工程学院，辽宁 沈阳 110819
    2 沈阳城市学院智能与工程学院，辽宁 沈阳 110112
  • 出版日期:2024-07-01 发布日期:2024-07-01
  • 作者简介:李凤云（1981- ），女，博士，东北大学计算机科学与工程学院副教授，主要研究方向为工业互联网、车联网、网络安全、数据隐私保护。
    窦泽慧（2001- ），女，东北大学计算机科学与工程学院硕士生，主要研究方向为联邦学习、信息安全。
    李朋（1980- ），男，沈阳城市学院智能与工程学院讲师，主要研究方向为工业互联网、联邦学习。
    郭威（2000- ），男，东北大学计算机科学与工程学院硕士生，主要研究方向为故障诊断、领域泛化、开集识别。
  • 基金资助:
    国家重点研发计划项目(2022YFE0114200)

Carbon emission prediction method of steel plants based on long short-term memory network

Fengyun LI1, Zehui DOU1, Peng LI2, Wei GUO1   

  1. 1 School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
    2 School of Intelligent and Engineering, Shenyang City University, Shenyang 110112, China
  • Online:2024-07-01 Published:2024-07-01
  • Supported by:
    The National Key Research and Development Program of China(2022YFE0114200)

在线阅读

21

PDF下载

56

摘要：

钢铁行业作为中国制造业碳排放量第二大的行业，拥有较大的碳减排潜力。为便于相关部门对碳排放量进行监管和控制，展开碳排放量预测研究。以某炼钢厂为研究对象，首先，分析炼钢流程中的二氧化碳排放，确定了引起碳排放的10种能源物质，据此收集了炼钢厂 20 01—2023年的基础能源数据，依据碳排放核算方法由基础能源数据核算出碳排放量；其次，基于长短期记忆网络预测未来7年的碳排放量，训练误差和测试误差均接近0.01，实际误差为1 323 307.46 t二氧化碳，并与其他3种预测模型进行对比，结果表明所提预测模型的拟合精度较高、预测效果较好；然后，采用Mann-Kendall趋势检验法评估炼钢厂的整体碳排放趋势；最后，为积极响应低碳环保目标，针对炼钢厂提出合理建议。

关键词: 炼钢厂, 长短期记忆网络, 碳排放预测, 低碳环保

Abstract:

As the second largest carbon emitter in China, iron and steel enterprises have great potential for carbon emission reduction.In order to facilitate the supervision and control of carbon emissions by relevant departments, carbon emission prediction research is carried out.Taking a steelmaking plant as the research object, firstly, the carbon dioxide emissions in the steelmaking process were analyzed, and 10 energy substances that caused carbon emissions were determined.The basic energy data of the steelmaking plant from 2001 to 2023 were collected, and the carbon emissions were calculated from the basic energy data according to the carbon emission accounting method.Secondly, based on the long short-term memory network to predict the carbon emissions in the next 7 years, the training error and test error were close to 0.01, and the actual error was 1 323 307.46 tons of carbon dioxide.Then, the Mann-Kendall trend test was used to evaluate the overall carbon emission trend of the steelmaking plant.Finally, some reasonable suggestions were put forward for steelmaking plants in order to actively respond to the goal of low-carbon environmental protection.

Key words: steel plant, long short-term memory network, carbon emission prediction, low carbon and environmental protection

中图分类号: 

No Suggested Reading articles found!