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

【摘要】：鋼鐵工業(yè)是我國國民經(jīng)濟長期的支柱性產(chǎn)業(yè),是發(fā)展其他產(chǎn)業(yè)的重要基礎(chǔ),在社會發(fā)展和經(jīng)濟建設(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。維持高爐的長期穩(wěn)定、高效運行,是鋼鐵行業(yè)追求的共同目標。高爐內(nèi)部料面分布是影響高爐爐況的重要因素之一,對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。本文針對高爐的密閉性,不能準確、直觀檢測料面狀態(tài)的問題,運用高爐雷達檢測技術(shù)、高爐料面形成機理、群智能算法、智能計算等技術(shù)進行“高爐料面形狀檢測與預(yù)測方法”的課題研究。主要從雷達料面檢測傳感器安裝位置優(yōu)化部署、高爐料面檢測、料面下降速度預(yù)測以及料面異常診斷四個角度深入研究,對高爐的長期穩(wěn)定運行和節(jié)能減排具有一定的指導(dǎo)和應(yīng)用價值。本文的主要研究內(nèi)容及創(chuàng)新點包括以下4個方面：(1)提出一種高爐雷達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,實現(xiàn)信息統(tǒng)一,避免冗余。同時,保證在使用最少雷達數(shù)量的前提下,實現(xiàn)雷達對爐喉料面的完全覆蓋以及料面關(guān)鍵點的K-重覆蓋。優(yōu)化方法一方面根據(jù)爐喉半徑與雷達覆蓋直徑的關(guān)系,建立料面的環(huán)形區(qū)域,減少雷達安裝數(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)化部署的評價函數(shù),利用改進的人工魚群智能算法求解優(yōu)化問題。最后,分別利用標準測試函數(shù)和現(xiàn)場實際數(shù)據(jù)驗證方法的有效性。(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)律和多點雷達數(shù)據(jù)實現(xiàn)對高爐料面的重構(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,提取雷達傳感器采集的料面實時高度數(shù)據(jù),引入多源信息融合的思想,對基于布料規(guī)律的料面方程進行修正。最后,基于爐料體積約束原則,采用迭代計算確定料面方程。經(jīng)過與其他算法的測試比較,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)測模型,首先,對表征高爐料面變化的雷達時間序列運用C-C算法計算時間延時和嵌入維數(shù),重構(gòu)相空間,利用小數(shù)據(jù)量法計算最大Lyapunov指數(shù),證明料面高度變化具有混沌性,為混沌預(yù)測方法的應(yīng)用建立了理論基礎(chǔ)；其次,分別采用極限學習機和在線慣序極限學習機建立高爐料面下降速度的離線和在線預(yù)測模型；最后,利用實際生產(chǎn)數(shù)據(jù)進行測試,并與同類算法比較,結(jié)果表明,預(yù)測算法在預(yù)測精度和速度上有良好的表現(xiàn),說明混沌方法在預(yù)測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)測模型,模型針對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,對旋轉(zhuǎn)森林集成算法的樣本選取和建立基分類器兩個階段分別進行優(yōu)化,建立料面異常分類預(yù)測模型。用標準測試集進行測試,并與其他集成算法比較,改進算法在總體精度和對少數(shù)類別分類精度都有所提高；利用實際生產(chǎn)數(shù)據(jù)建立分類預(yù)測模型,對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.

【學位授予單位】：北京科技大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TF54;TP18

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