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

Fengyun LI; Zehui DOU; Peng LI; Wei GUO

doi:10.11959/j.issn.2096-0271.2024051

您当前的位置：

首页 >

文章列表页 >

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

TOPIC: BIG DATA AND CLOUD STORAGE | 更新时间：2024-08-07

- Carbon emission prediction method of steel plants based on long short-term memory network
- Big data research Vol. 10, Issue 4, Pages: 66-76(2024)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2024051
  CLC： TP393
- Online First：2024-07，
  
  Published：15 July 2024
- 稿件说明：
移动端阅览
Fengyun LI, Zehui DOU, Peng LI, et al. Carbon emission prediction method of steel plants based on long short-term memory network[J]. Big data research, 2024, 10(4): 66-76.
DOI：

Fengyun LI, Zehui DOU, Peng LI, et al. Carbon emission prediction method of steel plants based on long short-term memory network[J]. Big data research, 2024, 10(4): 66-76. DOI： 10.11959/j.issn.2096-0271.2024051.

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。

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.

关键词

Keywords

references

FAN Z Y , FRIEDMANN S J . Low-carbon production of iron and steel:technology options,economic assessment,and policy [J ] . Joule , 2021 , 5 ( 4 ): 829 - 862 .

魏子强 , 温鹏 , 梁志 , 等 . 计及需求响应比例的园区综合能源系统低碳经济调度方法 [J ] . 太阳能学报 , 2023 , 44 ( 10 ): 38 - 45 .

WEI Z Q , WEN P , LIANG Z , et al . Lowcarbon economy dispatching method of park integrated energy system considering demand response ratio [J ] . Journal of Solar Energy , 2023 , 44 ( 10 ): 38 - 45 .

ONARHEIM K , MATHISEN A , ARASTO A . Barriers and opportunities for application of CCS in Nordic industry—a sectorial approach [J ] . International Journal of Greenhouse Gas Control , 2015 , 36 : 93 - 105 .

NAZERI M , SOLTANAGHAEI M , KHORSAND R . A predictive energyaware scheduling strategy for scientific workflows in fog computing [J ] . Expert Systems with Applications , 2024 ,247:123192.

LIU J L . Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak [J ] . Frontiers in Ecology and Evolution , 2023 ,11:1155401.

FAN D Y , SUN H , YAO J , et al . Well production forecasting based on ARIMALSTM model considering manual operations [J ] . Energy , 2021 ,220:119708.

ZHENG L , MUELLER M , LUO C B , et al . Predicting whole-life carbon emissions for buildings using different machine learning algorithms:a case study on typical residential properties in Cornwall,UK [J ] . Applied Energy , 2024 ,357:122472.

CAI K , WU L F . Grey prediction of carbon emission and carbon peak in several developing countries [J ] . Engineering Applications of Artificial Intelligence , 2024 ,133:108210.

LIU B , CHANG H D , LI Y , et al . Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model:evidence from Chongqing Municipality,China [J ] . Environmental Science and Pollution Research International , 2023 , 30 ( 32 ): 78849 - 78864 .

国家发展和改革委员会办公厅 . 关于印发首批10个行业企业温室气体排放核算方法与报告指南（试行）的通知:发改办气候[2013]2526号 [S ] . 2013 .

General Office of the National Development and Reform Commission . Notice on printing and distributing the first batch of greenhouse gas emission accounting methods and reporting guidelines for enterprises in 10 industries (for trial implementation):fa gai ban climate[2013]No.2526 [S ] . 2013 .

PRIARONE P C , CATALANO A R , SETTINERI L . Additive manufacturing for the automotive industry:on the lifecycle environmental implications of material substitution and lightweighting through re-design [J ] . Progress in Additive Manufacturing , 2023 , 8 ( 6 ): 1229 - 1240 .

朱广新 , 周春雷 , 李俊妮 , 等 . 基于图模型的电力系统碳流计算优化研究 [J ] . 大数据 , 2024 , 10 ( 2 ): 179 - 191 .

ZHU G X , ZHOU C L , LI J N , et al . Research on power system carbon flow calculation based on graph database and graph computing engine [J ] . Big Data Research , 2024 , 10 ( 2 ): 179 - 191 .

ZHANG Z , ZHANG S Q , CHEN C M , et al . A systematic survey of air quality prediction based on deep learning [J ] . Alexandria Engineering Journal , 2024 , 93 : 128 - 141 .

Views

147

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of long short-term memory networks in virtual power plant data centers

Related Author

Jun CHEN

Siheng NING

Related Institution

Shanghai SS Energy Co., Ltd.

School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273110 Visits Today：16

⁰