本文選題：高爐料面 + 群智能算法��； 參考：《北京科技大學(xué)》2017年博士論文

【摘要】：鋼鐵工業(yè)是我國(guó)國(guó)民經(jīng)濟(jì)長(zhǎng)期的支柱性產(chǎn)業(yè),是發(fā)展其他產(chǎn)業(yè)的重要基礎(chǔ),在社會(huì)發(fā)展和經(jīng)濟(jì)建設(shè)中發(fā)揮著舉足輕重的作用。高爐是鋼鐵生產(chǎn)過程中的關(guān)鍵設(shè)備,關(guān)系到行業(yè)的鋼鐵產(chǎn)量、能源消耗和環(huán)境污染。維持高爐的長(zhǎng)期穩(wěn)定、高效運(yùn)行,是鋼鐵行業(yè)追求的共同目標(biāo)。高爐內(nèi)部料面分布是影響高爐爐況的重要因素之一,對(duì)于維持高爐煤氣流合理分布、增加料層透氣性和高爐優(yōu)化操作有重要的作用。本文針對(duì)高爐的密閉性,不能準(zhǔn)確、直觀檢測(cè)料面狀態(tài)的問題,運(yùn)用高爐雷達(dá)檢測(cè)技術(shù)、高爐料面形成機(jī)理、群智能算法、智能計(jì)算等技術(shù)進(jìn)行“高爐料面形狀檢測(cè)與預(yù)測(cè)方法”的課題研究。主要從雷達(dá)料面檢測(cè)傳感器安裝位置優(yōu)化部署、高爐料面檢測(cè)、料面下降速度預(yù)測(cè)以及料面異常診斷四個(gè)角度深入研究,對(duì)高爐的長(zhǎng)期穩(wěn)定運(yùn)行和節(jié)能減排具有一定的指導(dǎo)和應(yīng)用價(jià)值。本文的主要研究?jī)?nèi)容及創(chuàng)新點(diǎn)包括以下4個(gè)方面：(1)提出一種高爐雷達(dá)傳感器安裝位置的優(yōu)化方法,方法可以兼顧已安裝的其他類型傳感器,實(shí)現(xiàn)信息統(tǒng)一,避免冗余。同時(shí),保證在使用最少雷達(dá)數(shù)量的前提下,實(shí)現(xiàn)雷達(dá)對(duì)爐喉料面的完全覆蓋以及料面關(guān)鍵點(diǎn)的K-重覆蓋。優(yōu)化方法一方面根據(jù)爐喉半徑與雷達(dá)覆蓋直徑的關(guān)系,建立料面的環(huán)形區(qū)域,減少雷達(dá)安裝數(shù)量,加快優(yōu)化速度：另一方面,根據(jù)料面形狀特征分析,給出傳感器優(yōu)化部署的評(píng)價(jià)函數(shù),利用改進(jìn)的人工魚群智能算法求解優(yōu)化問題。最后,分別利用標(biāo)準(zhǔn)測(cè)試函數(shù)和現(xiàn)場(chǎng)實(shí)際數(shù)據(jù)驗(yàn)證方法的有效性。(2)提出一種重構(gòu)高爐料面形狀的模型框架,模型基于布料規(guī)律和多點(diǎn)雷達(dá)數(shù)據(jù)實(shí)現(xiàn)對(duì)高爐料面的重構(gòu)。首先提出料面形狀描述方程,根據(jù)力學(xué)原理,求解料面堆角和堆尖位置,建立基于布料規(guī)律的料面方程;其次,提取雷達(dá)傳感器采集的料面實(shí)時(shí)高度數(shù)據(jù),引入多源信息融合的思想,對(duì)基于布料規(guī)律的料面方程進(jìn)行修正。最后,基于爐料體積約束原則,采用迭代計(jì)算確定料面方程。經(jīng)過與其他算法的測(cè)試比較,提出的重構(gòu)模型具有較高的精度,料面方程更加合理。(3)提出高爐料面下降速度預(yù)測(cè)模型,首先,對(duì)表征高爐料面變化的雷達(dá)時(shí)間序列運(yùn)用C-C算法計(jì)算時(shí)間延時(shí)和嵌入維數(shù),重構(gòu)相空間,利用小數(shù)據(jù)量法計(jì)算最大Lyapunov指數(shù),證明料面高度變化具有混沌性,為混沌預(yù)測(cè)方法的應(yīng)用建立了理論基礎(chǔ)；其次,分別采用極限學(xué)習(xí)機(jī)和在線慣序極限學(xué)習(xí)機(jī)建立高爐料面下降速度的離線和在線預(yù)測(cè)模型；最后,利用實(shí)際生產(chǎn)數(shù)據(jù)進(jìn)行測(cè)試,并與同類算法比較,結(jié)果表明,預(yù)測(cè)算法在預(yù)測(cè)精度和速度上有良好的表現(xiàn),說明混沌方法在預(yù)測(cè)料面下降速度方面的應(yīng)用是可行的。(4)提出高爐料面異常的不平衡樣本分類預(yù)測(cè)模型,模型針對(duì)高爐料面異常樣本數(shù)量少,造成訓(xùn)練樣本類別分布不平衡的特點(diǎn),對(duì)旋轉(zhuǎn)森林集成算法的樣本選取和建立基分類器兩個(gè)階段分別進(jìn)行優(yōu)化,建立料面異常分類預(yù)測(cè)模型。用標(biāo)準(zhǔn)測(cè)試集進(jìn)行測(cè)試,并與其他集成算法比較,改進(jìn)算法在總體精度和對(duì)少數(shù)類別分類精度都有所提高；利用實(shí)際生產(chǎn)數(shù)據(jù)建立分類預(yù)測(cè)模型,對(duì)料面異常樣本具有較高的分類精度。
[Abstract]:The iron and steel industry is China's long-term national economic pillar industry, is an important foundation for the development of other industries, plays an important role in the social development and economic construction. The blast furnace is the key equipment in steel production process, related to the industry of steel production, energy consumption and environmental pollution. To maintain long-term stability of blast furnace the efficient operation of the steel industry is the pursuit of common goals. The burden distribution of blast furnace is one of the important factors that affect the internal state of blast furnace, blast furnace gas flow to maintain reasonable distribution, increase the permeability of blast furnace operation and optimization has an important role. In this paper the sealing property of the blast furnace can not accurately detect the state of charge, visual problems the use of radar detection technology of blast furnace, the formation mechanism of blast furnace, swarm intelligence algorithm, intelligent computing technology for blast furnace shape detection and prediction method of the research. Mainly from the radar level detection sensor location optimization deployment, blast furnace detection, four aspects of in-depth study of abnormal diagnosis rate prediction and burden descent level, long-term stable operation of blast furnace and energy saving has certain guiding significance and application value. The main research contents and innovations of this paper include the following 4 aspects: (1) put forward the optimization method of blast furnace radar sensor installation location, method can take into account other types of sensors have been installed, to achieve unified information, to avoid redundancy. At the same time, ensure the quantity of at least under the radar, radar on the surface and throat completely cover the surface of key point K- optimization method according to the coverage. The relationship between the furnace throat radius and the diameter of the annular region of radar coverage, establish level, reduce the number of radar installation, to speed up the optimization speed. On the other hand, according to the surface The shape feature analysis, optimal deployment evaluation function is given, using the improved artificial fish swarm algorithm for solving the optimization problem. Finally, the validity of the standard test functions and actual data validation method respectively. (2) proposed a model frame reconstruction of blast furnace shape, reconstruction of the implementation of the rules and distribution of blast furnace burden multi radar data. Based on the first proposed surface shape description equation, according to the mechanics principle, solving the charge level angle and pile tip position, establish the equations based on the laws of surface cloth; secondly, the real-time surface height data extraction of radar sensor acquisition, introducing the idea of multi-source information fusion, the surface equation based on modified distributing law finally, the charge volume constraint based on the principle of using iterative calculation to determine the surface equation. After testing and comparison with other algorithms, the proposed model is reconstructed High precision, surface equation is more reasonable. (3) the blast furnace burden descent speed prediction model, firstly, the characterization of blast furnace burden change radar time sequences by C-C algorithm to calculate the time delay and embedding dimension of phase space reconstruction, using a small amount of data to calculate the maximum Lyapunov index, that burden height change is chaos, application chaos prediction method established a theoretical foundation; secondly, using extreme learning machine and used online offline and online order limit learning machine to establish prediction model of blast furnace burden rate of decline; finally, with the actual production data were tested, and compared with other similar algorithms, the results show that the prediction algorithm has good performance in prediction accuracy and speed, speed down that chaos method in the prediction of application level is feasible. (4) the blast furnace burden imbalance abnormal sample classification prediction model Type, model number for blast furnace abnormal samples, resulting in uneven distribution of the training samples, the rotation forest integration algorithm and sample selection of base classifier is established in two phases optimization, establish surface anomaly classification prediction model was tested using the standard test set, and compare with other integrated algorithm, improved algorithm the overall accuracy of minority class and classification accuracy are improved; the classification prediction model is established by using the actual production data, has higher classification accuracy on the level of abnormal samples.

【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TF54;TP18

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 ;氣固流化系統(tǒng)壓力和料面測(cè)定裝置[J];硫酸工業(yè);1974年04期

2 洪聲忠;水鋼高爐降料面用風(fēng)量判斷料面行程的實(shí)踐[J];煉鐵;1998年01期

3 ;天津東方紅化工廠試制成電子式料面計(jì)[J];石油化工自動(dòng)化;1968年00期

4 羅明華;;杭鋼2號(hào)高爐回收煤氣降料面實(shí)踐[J];浙江冶金;2003年01期

5 滕召杰;程樹森;杜鵬宇;;無鐘爐頂料面形狀檢測(cè)及平臺(tái)的形成[J];鋼鐵研究學(xué)報(bào);2012年03期

6 徐樹德;;新型高爐微波料面儀[J];湖南冶金;1988年04期

7 許永華;吳敏;曹衛(wèi)華;;基于信息融合的高爐料面分布模型與應(yīng)用[J];信息與控制;2005年06期

8 劉德馨;李曉理;丁大偉;陳先中;;基于雷達(dá)觀測(cè)數(shù)據(jù)的高爐料面多模型控制[J];控制理論與應(yīng)用;2012年10期

9 黃福華;固體料面測(cè)量元件介紹[J];化學(xué)世界;1962年11期

10 張賀順;王勝;馬洪斌;陳軍;任健;;首鋼1號(hào)高爐降料面停爐實(shí)踐[J];煉鐵技術(shù)通訊;2010年04期

相關(guān)會(huì)議論文 前10條

1 王效東;郭寂;鄧衛(wèi)民;丁汝才;;首秦1#高爐中修降料面及送風(fēng)恢復(fù)實(shí)踐[A];2010年全國(guó)煉鐵生產(chǎn)技術(shù)會(huì)議暨煉鐵學(xué)術(shù)年會(huì)文集（上）[C];2009年

2 賈軍民;;遷鋼高爐特殊爐況的降料面操作[A];2008年全國(guó)煉鐵生產(chǎn)技術(shù)會(huì)議暨煉鐵年會(huì)文集（下冊(cè)）[C];2008年

3 任國(guó)強(qiáng);董文明;楊光濤;;天鋼1~#高爐降料面實(shí)踐[A];2012年全國(guó)煉鐵生產(chǎn)技術(shù)會(huì)議暨煉鐵學(xué)術(shù)年會(huì)文集（上）[C];2012年

4 高征鎧;高泰;;激光在線探測(cè)高爐料面形狀技術(shù)的新進(jìn)展[A];2014年全國(guó)煉鐵生產(chǎn)技術(shù)會(huì)暨煉鐵學(xué)術(shù)年會(huì)文集(上)[C];2014年

5 齊俊茹;李朝旺;劉鵬君;米艦君;;唐鋼4號(hào)高爐中修降料面操作實(shí)踐[A];2013年河北省煉鐵技術(shù)暨學(xué)術(shù)年會(huì)論文集[C];2013年

6 郭先q，

本文編號(hào)：1736914