A novel performance assessment method of the carbon efficiency for iron ore sintering process

过程(计算) 模糊逻辑 计算机科学 航程(航空) 碳纤维 先决条件 工艺工程 数据挖掘 可靠性工程 算法 人工智能 工程类 复合数 程序设计语言 航空航天工程 操作系统
作者
Kailong Zhou,Xin Chen,Min Wu,Yosuke Nakanishi,Weihua Cao,Jie Hu
出处
期刊:Journal of Process Control [Elsevier BV]
卷期号:106: 44-53 被引量:4
标识
DOI:10.1016/j.jprocont.2021.08.011
摘要

Improving carbon efficiency is an effective way to save energy and reduce harmful emission for a sintering process. Optimizing carbon efficiency is an effective way to achieve that goal, and its precondition is to assess the performance of the carbon efficiency. However, there is seldom research about how to assess the carbon efficiency whether it needs to be optimized. To address this, this paper introduces a performance assessment method for evaluating the performance of the carbon efficiency. First, the sintering process and the key characteristics are analyzed, and the carbon efficiency indexes are defined. Second, the structure of the assessment method is presented. The method consists of a prediction model based on three NNs, and an assessment method based on the fuzzy synthetic evaluation method. Two-level combination strategy is proposed to improve prediction performance and assessment accuracy, with the using of bootstrap aggregating, linear combination, and majority voting. Finally, verification based on process data shows that the proposed method can assess the performance of the carbon efficiency with high accuracy. More specially, the prediction errors of the combination model for the CCR are basically in the range of [-2.738 kg/t, 3.442 kg/t], and for the CO/CO2 they are basically in the range of [-8.16 × 10−3, 4.828 × 10−3]. The combination models have better prediction performance than single NNs. Moreover, the assessment accuracy of the proposed method is 87%, which has higher accuracy than other models. This model lays the groundwork of improving the carbon efficiency for sintering process.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
molihuakai应助多情少浅采纳,获得10
1秒前
Enuo完成签到,获得积分10
1秒前
科研通AI6.4应助锦鲤附体采纳,获得10
1秒前
啵啵应助啊啊采纳,获得50
1秒前
GH完成签到,获得积分10
2秒前
2秒前
不搞科研的狗完成签到 ,获得积分20
2秒前
诚心梦松发布了新的文献求助10
2秒前
COCA发布了新的文献求助10
2秒前
2秒前
2秒前
2秒前
沁晨发布了新的文献求助10
3秒前
赘婿应助时翎采纳,获得10
3秒前
chriswtr发布了新的文献求助10
4秒前
唠叨的星月完成签到 ,获得积分10
4秒前
平常叫兽发布了新的文献求助10
4秒前
哈哈发布了新的文献求助10
4秒前
5秒前
受伤冰菱完成签到,获得积分10
5秒前
豆豆发布了新的文献求助10
5秒前
东方元语应助圣诞结采纳,获得20
6秒前
6秒前
fantec发布了新的文献求助10
7秒前
帅气雨雪完成签到,获得积分20
7秒前
7秒前
8秒前
华仔应助吴竟钊采纳,获得10
8秒前
9秒前
lcsolar完成签到,获得积分10
9秒前
研友_LkBYo8发布了新的文献求助10
9秒前
慕青应助诚心梦松采纳,获得10
9秒前
情怀应助靓丽小土豆采纳,获得10
10秒前
超帅秋翠完成签到 ,获得积分20
10秒前
10秒前
Hui完成签到,获得积分10
10秒前
酷波er应助冷水鱼采纳,获得10
11秒前
老赵是真的帅完成签到,获得积分10
11秒前
琉璃苣发布了新的文献求助10
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
Synthesis of P-Chiral Phosphine Ligands and Their Applications in Asymmetric Catalysis 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7622608
求助须知:如何正确求助?哪些是违规求助? 9197925
关于积分的说明 19716684
捐赠科研通 7194042
什么是DOI,文献DOI怎么找? 3272994
关于科研通互助平台的介绍 2435430
邀请新用户注册赠送积分活动 2268413